University of Calgary PRISM: University of Calgary's Digital Repository Graduate Studies The Vault: Electronic Theses and Dissertations 2014-12-24 Changes in tendon compliance and muscle energetics of in vivo human skeletal muscle Fletcher, Jared Fletcher, J. (2014). Changes in tendon compliance and muscle energetics of in vivo human skeletal muscle (Unpublished doctoral thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/25251 http://hdl.handle.net/11023/1980 doctoral thesis University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. Downloaded from PRISM: https://prism.ucalgary.ca UNIVERSITY OF CALGARY Changes in tendon compliance and muscle energetics of in vivo human skeletal muscle by Jared R Fletcher A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY GRADUATE PROGRAM IN KINESIOLOGY CALGARY, ALBERTA DECEMBER, 2014 © Jared R Fletcher 2014 Abstract Recently published reports suggest the role of the muscles and tendons of the lower limbs are an important factor in determining the energy cost of running (Erun). Specifically, there exists a link between the mechanical properties of the Achilles tendon (AT) and Erun but the impact of the muscle’s energy cost is not considered. To date, very little is known regarding the interaction between AT stiffness, muscle energetics and Erun. Further, little is known about the AT stiffnessenergetics relationship in female runners. Therefore, the overall goal of this thesis was to explore the relationship between AT stiffness and muscle energetics in male and female distance runners. The first study revealed AT stiffness of female runners was lower than in males, but Erun was similar to males. Further, the relationship between Erun and Achilles tendon stiffness was not different between the sexes. Results from the second study demonstrated that when reductions in AT stiffness were simulated, the rate of muscle energy use was elevated and the magnitude of muscle activation needed to reach a target force was increased. A novel method of assessing AT moment arm was assessed in study four. A key finding was that moment arm did not change through ankle range of motion. These results were used in the fifth study which demonstrated using estimates of muscle energetics, along with kinematics and kinetics during running that strain energy release from the AT during running was significantly lower than the muscle energy cost required for strain energy storage to occur. Lastly, using a prolonged run as an acute method of reducing AT stiffness, the impact of changes in AT stiffness during running on muscle energetics and Erun was evaluated. Results from this final study suggest that prolonged running reduces AT stiffness, the impact of which is an elevated muscle energy cost and increased whole-body Erun without a significant increase in estimated AT strain energy release. Together these findings support the notion that the role of the AT in running is to accommodate muscle-tendon unit length change, thereby reducing the amount of muscle fascicle shortening and therefore muscle energy cost. ii Preface Chapters two through seven, respectively, are based on the following manuscripts: Fletcher, J.R., and B.R. MacIntosh. Energetic aspects of running economy. In preparation. Fletcher, J.R., Pfister, T.R. and B.R. MacIntosh. Energy cost of running and Achilles tendon stiffness in male and female trained runners. Physiol Rep, 1(7), e00178, doi:10.1002/phys2.178, 2013. Used under the terms of the Creative Commons Attribution License. Fletcher, J.R., Groves, E.M., Pfister, T.R. and B.R. MacIntosh. Does muscle shortening alone explain the energy cost of muscle contraction in vivo? European Journal of Applied Physiology, 113: 2312-2322. Used with kind permission of Springer Science+Business Media. Fletcher, J.R., and B.R. MacIntosh. Estimates of Achilles tendon moment arm length at different ankle joint angles: effect of passive force. Re-submitted to Experimental Physiology, November 5, 2014. EXPPHYSIOL/2014/084004 Fletcher, J.R. and B.R. MacIntosh. Achilles tendon strain energy in distance running: consider the muscle energy cost. Journal of Applied Physiology, in press. DOI: 10.1152/japplphysiol.00732.2014. Used with kind permission of the American Physiological Society. Fletcher, J.R. and B.R. MacIntosh. Changes in Achilles tendon stiffness and energy cost following a prolonged run in trained distance runners. Submitted to Applied Physiology, Nutrition, and Metabolism, October 21, 2014, apnm-2014-0446. iii This dissertation is based on a collection of stand-alone manuscripts, and there may be some redundancy in the introduction, methods and discussion of chapters two through seven. iv Acknowledgements I would like to thank the following individuals and organizations, whose support throughout this process was invaluable: Dr. Brian MacIntosh, for the continuous and unwavering support throughout my academic journey, for encouraging me to explore the answers to the questions posed within this thesis no matter where the literature search or data analysis road took me and most importantly for his time and dedication that was put into this work. I never would have thought entering graduate school that my love for distance running would have taken me this far. My supervisory committee, Dr. Walter Herzog and Dr. Doug Syme. The knowledge and insight you have both imparted upon me throughout the entire doctoral process was invaluable. Dr. John Bertram for serving as internal/external examiner on both the candidacy and thesis oral examination committees. Dr. Stephen Ingham for serving as external examiner. Dr. Erik Groves and Ted Pfister, whose contributions are reflected as co-authors on publications contained in this thesis. Your help with data collection, analysis and interpretation is greatly appreciated. The truly dedicated subjects who winningly and eagerly volunteered to participate in these studies, often having to return on several occasions to ensure the data were ‘perfect’. v Dr. Chris Barclay for his insight into muscle energy cost estimates during running. The various sources of funding throughout my doctoral training: NSERC, the Faculties of Graduate Studies and Kinesiology, the Sport Science Association of Alberta and Own the Podium. Without this support, none of this would have been possible. Shane Esau, for constantly encouraging me to think outside the box, for allowing me to explore research related to elite athlete physiology, for constantly demonstrating your dedication to excellence in elite sport and for always challenging me to ask “why?” or “how?” Whether on the road, in the air or on or around the water, I have cherished all of the great chats (academic or otherwise) we have had over cold beers and cold tubs as we continue to pursue excellence in applied sport science research. Members of the Applied Muscle Physiology Lab past and present. Your thoughts, feedback and suggestions on material presented (often minutes after the completion of data analysis) have been greatly appreciated. The numerous collaborators I have had the pleasure of working with over the course of this PhD who have demonstrated that the academic process isn’t ‘all about the thesis’. These collaborations have resulted in numerous manuscripts, conference abstracts and proceedings. Your collaborations have been and will continue to be most valuable as this academic journey continues. vi Drs. Geoff Power and Atsuki Fukatani for their collaborations on in situ muscle preparations, studying dynamic post-activation potentiation and residual force enhancement. A lot of excellent conversations were had at various conferences around the world, over a drink or in the lab waiting for that 20 minute recovery period to end. Drs. Lisa Stirling, Vincenz von Tsharner and Benno Nigg for our work in the quantification of the manifestation of fatigue during running. Inge Stoter, Dr. Floor Hettinga and Spencer Pootz for our work on pacing strategies in speed skating and cycling. My parents, Rod and Karen, who from an early age encouraged me to pursue my dreams, no matter what they might have been, no matter how big and seemingly unachievable they should have been, and no matter where those dreams took me. Words cannot express how much your continued support throughout this entire journey has meant to me. Genesta, who has always been there to listen, to laugh and to love. Your words of encouragement, particularly during this writing period (“you can do it!”), like all your kind words, kept me on the right track until the very end. Once the snow melts, let’s go golfing… vii Table of Contents ABSTRACT........................................................................................................................ II   PREFACE ..........................................................................................................................III   ACKNOWLEDGEMENTS ................................................................................................V   TABLE OF CONTENTS................................................................................................VIII   List of Tables ................................................................................................................ xii   List of Figures .............................................................................................................. xiii   List of symbols, abbreviations and nomenclature .........................................................xv   Epigraph ...................................................................................................................... xvii   CHAPTER ONE: INTRODUCTION ..................................................................................1   1.1 General introduction ..................................................................................................1   1.2 Purpose of the research ..............................................................................................4   1.3 Hypotheses .................................................................................................................4   1.4 Overview of separate chapters ...................................................................................5   CHAPTER TWO: ENERGETIC ASPECTS OF RUNNING ECONOMY ........................8   2.1 Abstract ......................................................................................................................9   2.2 Introduction ..............................................................................................................10   2.2.1 Importance of Erun to distance running performance .......................................10   2.2.2 Quantifying the energy cost of running ...........................................................12   2.3 Skeletal Muscle energetics.......................................................................................14   2.3.1 Skeletal muscle energetics ...............................................................................15   2.3.1.1 Cross-bridge turnover and activation (ion pumping) .............................15   2.3.1.2 Turnover of cross-bridges ......................................................................16   2.3.2 Energy cost of muscle contraction ..................................................................18   2.3.2.1 Force-length relationship .......................................................................20   2.3.2.2 Force-velocity relationship ....................................................................21   2.3.2.3 Motor unit recruitment ...........................................................................22   2.4 Factors not affected by training ...............................................................................23   2.4.1 Environment ....................................................................................................23   2.4.1.1 Wind.......................................................................................................23   2.4.1.2 Temperature ...........................................................................................24   2.4.1.3 Altitude ..................................................................................................24   2.4.2 Surface Features ..............................................................................................26   2.4.2.1 Friction ...................................................................................................26   2.4.2.2 Surface stiffness .....................................................................................27   2.4.2.3 Other surface features ............................................................................28   2.4.2.4 Footwear ................................................................................................28   2.4.3 Anthropometry ................................................................................................30   2.4.3.1 Ankle & Foot Morphology ....................................................................30   2.4.3.2 Body mass, body composition and mass distribution ............................33   viii 2.4.3.3 Frontal surface area ................................................................................36   2.5 Factors affecting Erun that are altered by Training ...................................................39   2.5.1 Anthropometry ................................................................................................39   2.5.1.1 Body mass ..............................................................................................39   2.5.1.2 Allometric-scaling for body mass ..........................................................40   2.5.2 Muscle Properties ............................................................................................41   2.5.3 Tendon stiffness...............................................................................................42   2.5.3.1 Does an “optimal stiffness” exist to reduce the EC of running? ...........46   2.5.4 Running Mechanics .........................................................................................47   2.5.4.1 Stride Length and stride frequency ........................................................47   2.5.4.2 Ground contact time...............................................................................50   2.5.4.3 Footstrike Pattern ...................................................................................51   2.5.4.4 Flexibility ...............................................................................................53   2.6 Conclusions and future directions: Muscle energetics and Erun ...............................54   2.7 Author Contribution .................................................................................................55   2.8 Acknowledgements ..................................................................................................55   2.9 Figures .....................................................................................................................56   CHAPTER THREE: ENERGY COST OF RUNNING AND ACHILLES TENDON STIFFNESS IN MALE AND FEMALE TRAINED RUNNERS ............................64   3.1 Abstract ....................................................................................................................65   3.2 Introduction ..............................................................................................................66   3.3 Methods ...................................................................................................................69   3.3.1 Ethical Approval ..............................................................................................69   3.3.2 Experimental Protocol .....................................................................................69   3.3.3 Correction for joint rotation.............................................................................72   3.3.4 Statistics ...........................................................................................................73   3.4 Results ......................................................................................................................74   3.4.1 Tendon Mechanical Properties ........................................................................75   3.5 Discussion ................................................................................................................76   3.6 Conclusion ...............................................................................................................80   3.7 Author Contribution .................................................................................................80   3.8 Acknowledgements and disclosures ........................................................................81   3.9 Tables .......................................................................................................................82   3.10 Figures ...................................................................................................................85   CHAPTER FOUR: TENDON COMPLIANCE, MUSCLE SHORTENING AND MUSCLE ENERGETICS ..........................................................................................................88   4.1 Abstract ....................................................................................................................89   4.2 Introduction ..............................................................................................................90   4.3 Methods ...................................................................................................................93   4.3.1 Subjects............................................................................................................93   4.3.2 Tendon Mechanical Properties ........................................................................94   4.3.3 Correction for joint rotation.............................................................................95   4.3.4 Measurement of EC of contraction..................................................................96   ix 4.3.4.1 Testing Protocol .....................................................................................97   4.3.5 Statistics .........................................................................................................100   4.4 Results ....................................................................................................................100   4.5 Discussion ..............................................................................................................103   4.6 Conclusions ............................................................................................................107   4.7 Author Contributions .............................................................................................107   4.8 Ethical standards ....................................................................................................108   4.9 Conflict of Interest statement .................................................................................108   4.10 Acknowledgements ..............................................................................................108   4.11 Tables ...................................................................................................................109   4.12 Figures .................................................................................................................110   CHAPTER FIVE: ESTIMATES OF ACHILLES TENDON MOMENT ARM LENGTH AT DIFFERENT ANKLE JOINT ANGLES: EFFECT OF PASSIVE MOMENT .....117   5.1 Abstract ..................................................................................................................118   5.2 Introduction ............................................................................................................119   5.3 Methods .................................................................................................................121   5.3.1 Correction for passive force ..........................................................................123   5.3.2 Statistics and data analysis ............................................................................126   5.4 Results ....................................................................................................................127   5.5 Discussion ..............................................................................................................128   5.6 Conclusion .............................................................................................................132   5.7 Author Contributions .............................................................................................133   5.8 Conflict of Interest statement .................................................................................133   5.9 Acknowledgements ................................................................................................133   5.10 Figures .................................................................................................................134   CHAPTER SIX: ACHILLES TENDON STRAIN ENERGY IN DISTANCE RUNNING: CONSIDER THE MUSCLE ENERGY COST ......................................................139   6.1 Abstract ..................................................................................................................140   6.2 Introduction ............................................................................................................141   6.3 Methods .................................................................................................................144   6.3.1 Experimental Protocol ...................................................................................145   6.3.2 Muscle energy cost ........................................................................................147   6.3.3 Statistics .........................................................................................................148   6.4 Results ....................................................................................................................149   6.5 Discussion ..............................................................................................................151   6.5.1 Erun between groups .......................................................................................152   6.5.2 Tendon strain energy release .........................................................................152   6.5.3 Estimates of muscle energy cost to allow for tendon strain energy storage ..154   6.6 Conclusion .............................................................................................................157   6.7 Author Contributions .............................................................................................157   6.8 Acknowledgements and disclosures ......................................................................158   6.9 Tables .....................................................................................................................159   6.10 Figures .................................................................................................................161   x CHAPTER SEVEN: CHANGES IN ACHILLES TENDON STIFFNESS AND ENERGY COST FOLLOWING A PROLONGED RUN IN TRAINED DISTANCE RUNNERS .................................................................................................................................167   7.1 Abstract ..................................................................................................................168   7.2 Introduction ............................................................................................................169   7.3 Methods .................................................................................................................172   7.3.1 Determination of AT Stiffness ......................................................................173   7.3.2 AT energy storage/release and muscle energy cost .......................................174   7.3.3 Measurement of Erun ......................................................................................176   7.3.4 Statistics .........................................................................................................176   7.4 Results ....................................................................................................................177   7.5 Discussion ..............................................................................................................178   7.6 Conclusion .............................................................................................................184   7.7 Author Contributions .............................................................................................185   7.8 Acknowledgements and disclosures ......................................................................185   7.9 Tables .....................................................................................................................186   7.10 Figures .................................................................................................................187   CHAPTER EIGHT: CONCLUSIONS AND FUTURE DIRECTIONS .........................193   8.1 Conclusions ............................................................................................................193   8.2 Future Directions ...................................................................................................195   8.2.1 How quickly does AT stiffness get ‘tuned’ to be optimal? ...........................195   8.2.2 Validation of muscle energetics model .........................................................196   8.2.3 Direct measurement of force-length-velocity relationship ............................196   8.2.4 Estimates of role of tendon stiffness in other muscles relevant during running198   REFERENCES ................................................................................................................199   APPENDIX A: COPYRIGHT PERMISSIONS ..............................................................238   xi List of Tables Table 3-1. Subject Characteristics. ............................................................................................... 82   Table 3-2. Running Economy and RER. ...................................................................................... 83   Table 3-3. Tendon mechanical properties for males and females. ............................................... 84   Table 4-1. Subject characteristics. .............................................................................................. 109   Table 6-1. Subject Characteristics. ............................................................................................. 159   Table 6-2. Fz (N) as a function of % sLT.................................................................................... 160   Table 7-1. Subject characteristics. .............................................................................................. 186   xii List of Figures Figure 2.1. Erun and muscle energy cost in male and female runners. .......................................... 56   Figure 2.2. Average estimated Achilles tendon force-elongation curves during running for elite male (EM), trained male (TM) and trained female (TF) distance runners.................... 57   Figure 2.3. The effect of greater shortening velocity on muscle activation to achieve a target force. ..................................................................................................................................... 58   Figure 2.4. Estimated Achilles tendon (AT) force from ground reaction forces (GRF) and the gear ratio, the ratio of the moment arms of the GRF and AT, respectively.......................... 59   Figure 2.5. The metabolic cost and upper body kinematics of running with and without arm swing. .................................................................................................................................... 60   Figure 2.6. Allometric-scaling of body mass on submaximal VO2 during running. ................... 61   Figure 2.7. Force-length (A) and force-power-velocity relationship (B) of the frog semimembranosus muscle during maximal jumping. ........................................................... 62   Figure 2.8. Determining running costs at self-selected (RCsel) and optimal stride frequency (RCopt). ................................................................................................................................ 63   Figure 3.1. O2 cost at the three measured relative speeds in both males and females. ................ 85   Figure 3.2 Erun as a function of absolute running speed. .............................................................. 86   Figure 3.3. The relationship between Achilles tendon (AT) stiffness and Erun for males and females. ................................................................................................................................. 87   Figure 4.1. The effect of greater shortening velocity on muscle activation to achieve a target force. ................................................................................................................................... 110   Figure 4.2. Experimental set-up. ................................................................................................ 111   Figure 4.3. Representative tracing for one subject of HbO2 desaturation as measured by NIRS. .................................................................................................................................. 112   Figure 4.4. Mean maximum rate of muscle oxygen uptake (maximum HbO2) compared to mean torque during the experimental conditions. ............................................................... 113   Figure 4.5. The relationship between the rate of energy use to maintain a given torque (HbO2•impulse-1, AU•Nm•s-1) and magnitude of MG muscle fascicle shortening (cm). ... 114   Figure 4.6. The relationship between the rate of energy use to maintain a given torque (HbO2•impulse-1, AU•Nm-1•s-1) and shortening velocity (Lf•s-1). ...................................... 115   xiii Figure 4.7. EMG amplitude for LG (top) and SOL (bottom) for both experimental conditions expressed relative to EMG amplitude measured during the isometric MVC (100% MVC). ................................................................................................................................. 116   Figure 5.1 Calculation of the uncorrected and corrected Achilles tendon MA length from joint angle and tendon displacement. .................................................................................. 134   Figure 5.2. Calculation of passive moment influencing the estimate of Achilles tendon MA length................................................................................................................................... 135   Figure 5.3. Estimates of TP from practical and criterion experimental approaches. .................. 136   Figure 5.4. Passive Moment (A) and corrected elongation (B) for all subjects as a function of ankle joint angle. ................................................................................................................. 137   Figure 5.5. Corrected (solid line) vs uncorrected (dashed line) Achilles tendon MA estimates as a function of ankle angle. ............................................................................................... 138   Figure 6.1. Estimated Achilles tendon energy storage and release during running. .................. 161   Figure 6.2. Erun at the three measured relative speeds for all groups. ....................................... 162   Figure 6.3. Average F-dL curves for all groups during running. ............................................... 163   Figure 6.4. Comparison of the energy cost per stride for all groups and across relative speeds. ................................................................................................................................. 164   Figure 6.5. AT energy release (filled bars) relative to estimated muscle energy cost required to allow AT energy storage to occur (hashed bars) for all groups and all measured running speeds. ................................................................................................................... 165   Figure 6.6. Relationship between AT energy release and 95% sLT. .......................................... 166   Figure 7.1. Experimental protocol. ............................................................................................. 187   Figure 7.2. Erun prior to (PRE) and following (POST) RUN. ..................................................... 188   Figure 7.3 AT force-elongation curve prior to and following RUN. .......................................... 189   Figure 7.4. AT stiffness measured prior to (PRE) and following (POST) RUN. ....................... 190   Figure 7.5. Relationship between the change in Erun and AT stiffness following RUN. ............ 191   Figure 7.6. Relationship between the change in Erun and TS muscle energy cost following RUN. ................................................................................................................................... 192   xiv List of symbols, abbreviations and nomenclature Symbol AT ATP Definition Achilles tendon Adenosine triphosphate [BLa-] BM Ca2+ ATPase CSA dL EC ECM EM EM EMG Erun FM Fz HbO2 HHbO2 ISO K KIN Lf LG MA MC MG MM MVC NIRS RER RMS RUN S Blood lactate concentration Body mass Calcium adenosine triphosphate Cross-sectional area Elongation Energy cost Rate of muscle energy use Rate of muscle use per unit volume Elite male Electromyography Energy cost of running Muscle force Vertical ground reaction forces Oxyhaemoglobin Deoxyhaemoglobin Isometric Achilles tendon stiffness Isokinetic Fascicle length Lateral gastrocnemius Moment arm Corrected moment Medial gastrocnemius Measured moment Maximal voluntary contraction Near-infrared spectroscopy Respiratory Exchange Ratio Root Mean Square 90 minute prolonged run Speed SERCA Sarco-endoplasmic reticulum Ca2+ ATPase xv symbol SL sLT SOL SR SWC TE TF TM TS V/Vmax VL Definition Stride length Speed of lactate threshold Soleus Sarcoplasmic reticulum Smallest worthwhile change Tendon Excursion (method) Trained female Trained male Triceps Surae Velocity of shortening relative to maximal unloaded shortening velocity Vastus lateralis Maximal rate of oxygen uptake   Rate of oxygen uptake xvi Epigraph “Great ideas originate in the muscles.” -Thomas A. Edison xvii Chapter One: Introduction 1.1 General introduction This thesis presents an investigation into the changes in tendon stiffness and muscle energetics of in vivo human skeletal muscle and is compiled as a series of independent manuscripts. Therefore, each chapter contains an introduction specific to that investigation. A general introduction to the thesis is presented here. The energy cost of running (Erun) has been a unique trait to human evolution, as it allowed our Homo sapiens ancestors to cover great distances on a finite energy supply. This allowed these individuals the unique advantage of hunting by foot over great distances while generating very little metabolic heat. The reduced metabolic heat generation, as well as the ability to dissipate that heat which is generated offered a unique advantage to Homo sapiens over the rest of the animal kingdom, who did not have this advantage of economy of locomotion and thermoregulatory capacity (Bramble and Lieberman, 2004). Since quadrupeds cannot simultaneously pant and gallop (Bramble and Jenkins, 1993), the technique of forcing animals to gallop over long distances particularly during the hottest times of day, allowed early Homo sapiens to literally follow the hunted animal until the animal was driven to hyperthermia and died. Today, the importance of Erun is highlighted in that it is an important physiological predictor of distance running performance among elite runners and may be the key performance variable in breaking the two-hour marathon. As it relates to health, understanding the factors which dictate a good exercise economy may be beneficial in that it allows one to perform exercise or activities of daily living at a low metabolic cost. This becomes important when individuals are faced with a rapidly-declining maximal oxygen uptake in the face of aging or disease; a good exercise economy, defined as a low metabolic cost, may be the sole way of accomplishing these routine tasks. The energy cost of exercise is primarily determined by the energy cost of muscle contraction yet much of the work studying the energy cost of running does not consider what the muscles are doing. More specifically, few studies have investigated the interaction between muscle and tendon in minimizing the energy cost of muscular contraction and how this may translate to improving the whole-body Erun. Furthermore, virtually nothing is known about muscle- tendon interactions in female runners. Most research on this topic has been done on male runners. It has become apparent in recent years that the role of the muscle-tendon unit in the lower limbs may be an important determinant of Erun. During running, muscular contractions are repeated. The energy cost of these contractions is thought to be dependent on the required force and the amount of fibre shortening during the contractions. The tendon, if sufficiently compliant, may allow the fibres to remain isometric during a stretch-shorten cycle of the whole muscle-tendon unit. As a result, the tendon alone can accommodate the joint range of motion, keeping the energy cost of the contraction low (Roberts et al, 1997). Also during these actions, elastic energy is stored in the tendon (Voigt et al, 1995) during the tendon stretch, and a portion of this elastic energy is returned during the shortening phase (Cavagna et al, 1968, Voigt et al, 1995). The tendons of the lower limbs act as a spring. There is growing evidence to suggest that the elastic recoil provided by the tendon contributes a significant portion of the energy for propulsion (Arampatzis et al, 2006, Hof et al, 2002, Lichtwark and Wilson, 2008, Scholz et al, 2008). The elastic properties of tendons can enhance muscle performance, as well as reduce the energetic cost of contraction, during stretch-shortening cycle activities because tendon stretch 2 and recoil reduces the muscular work (Gabaldon et al, 2008, Lichtwark et al, 2007), and muscle work is thought to require additional energy beyond that required for isometric force maintenance (Ryschon et al, 1997, Chasiotis et al, 1987, Bergstrom and Hultman, 1988, Russ et al, 2002). It has previously been shown that in a group of trained distance runners, the most economical runners displayed a higher stiffness of the Achilles tendon (AT) compared to the less economical runners (Arampatzis et al, 2006, Fletcher et al, 2010). To date, it is unclear how and why a stiff AT may be associated with a lower Erun. There are apparent advantages of stiff tendons in some cases, and compliant tendons in other cases. The lengthening of a tendon for energy storage is relevant in stretch-shortening cycles where a substantial pre-stretch of the tendon occurs early in a contraction. A compliant tendon allows more energy conversion of either kinetic or gravitational energy to potential energy. This energy can subsequently be released upon shortening. A compliant tendon may also help by allowing the tendon to lengthen during the stretch phase of the stretch-shortening cycle thereby keeping fascicle shortening velocity low. This permits high active force to be generated. In contrast, a stiff Achilles tendon is associated with lower Erun, in spite of lower capacity for elastic energy storage and return. Differences in the mechanical properties of the tendon will result in differences in the storage and release of tendon strain energy as well as the muscle energy cost to allow that tendon strain energy to occur. To date, the interaction between tendon strain energy and the associated muscle energy cost has not been examined, nor has this interaction been studied where AT mechanical properties are altered acutely. 3 1.2 Purpose of the research Therefore, the purpose of this thesis was to examine changes in tendon compliance and skeletal muscle energetics in vivo. Examining the difference in muscle and whole-body energy cost between stiff and compliant Achilles tendons was accomplished in several ways: 1. By examining differences in Erun between males and females, where it was anticipated the AT mechanical properties would differ. 2. By acutely altering AT stiffness by allowing additional shortening of the muscle tendon unit during contraction and examining the change in the rate of muscle energy use. 3. By examining differences in Erun and AT mechanical properties between elite and trained runners, where it was anticipated the AT mechanical properties would differ. 4. By estimating the amount of AT strain energy released per stride, and the associated muscle energy cost for this tendon strain energy storage/release to occur in elite and trained male and female runners. 5. By estimating the change in AT mechanical properties and the associated energy cost of muscle contraction before and after a prolonged run, where it was predicted the AT mechanical properties and whole-body Erun would be altered as a result of the run. 1.3 Hypotheses It was hypothesized that: 1. Erun and AT mechanical properties would differ between similarly-trained male and female runners. 4 2. Additional muscle fascicle shortening, as measured by ultrasonography would result in an elevated muscle energy cost measured using near-infrared spectroscopy. 3. Erun measured at a common relative speed would differ between elite and trained runners. These differences would be partially attributable to the differences in AT mechanical properties between elite and trained runners. 4. The differences in AT mechanical properties would result in differences in both the estimated amount of tendon strain energy and muscle energy cost for storage of tendon strain energy to occur between elite and trained male and female runners. 5. A prolonged submaximal run would result in: a. A reduction in AT stiffness b. An increase in the estimated tendon strain energy storage/release c. An elevated muscle energy cost in order for tendon strain energy to occur, a result of elevated muscle fascicle shortening following the run. d. An elevated Erun following the prolonged run, some of the elevated energy cost being attributable to the elevated triceps surae muscle energy cost post-run. 1.4 Overview of separate chapters Chapter two is dedicated to understanding the major factors that influence Erun from a muscle energetics perspective. Many reviews have been dedicated to understanding the specific factors dictating Erun and how they may be improved with various forms of training (Saunders et al, 2004, Anderson, 1996, Barnes and Kilding, 2014). However, most studies do not consider the fact that our skeletal muscles require energy to contract and this is where the energy is going. Here, we review the general factors that influence the energy cost of running, and try to put them 5 into the context of understanding the role that muscle contraction and muscle energetics plays in contributing to the variability in the Erun and how, from a muscle energetics point of view, the energetic factors which likely can and cannot be changed with training. In Chapter 3, we address the question of whether or not there are differences in Erun between similarly-trained male and female distance runners. While this question has been addressed previously by several authors (Ingham et al, 2008, Daniels and Daniels, 1992, Helgerud et al, 2010, Pate et al, 1992), there is still contention as to whether differences between male and female runners exist. We believe much of this controversy arises from the inappropriate measurement of Erun itself. Furthermore, very little is known regarding the specific AT mechanical properties in female runners and how this relates to Erun. If Erun does not differ between the sexes, yet the AT mechanical properties do, why might this be the case? Is AT stiffness ‘tuned’ to a higher compliance in the slower runners in order to minimize muscle shortening, and therefore run with a lower muscle energy cost? In Chapter 4 we address whether additional muscle shortening and/or work contributes to an elevated muscle energy cost, and therefore likely an elevated Erun. To accomplish this, we measured muscle fascicle shortening, the level of muscle activation and the rate of muscle energy use in vivo during isometric and isokinetic contractions. These data have allowed us to quantify in vivo the differences in the rate of muscle energy use between isometric and isokinetic contractions, where it was anticipated the amount and rate of muscle shortening would differ. In order to appropriately estimate triceps surae muscle and Achilles tendon forces from joint moments during running, it is important to accurately estimate the AT moment arm at different ankle angles. In Chapter 5 we propose a novel correction for passive force in estimating the AT moment arm length. These data allowed us to accurately estimate tendon 6 forces during running in order to estimate AT tendon strain and muscle energy cost during running. Chapter 6 presents the relationship between AT strain energy storage and release and the estimated corresponding muscle energy cost for this strain energy storage to occur, allowing us to reconsider the role of the AT during distance running. Given that elite runners have considerably stiffer ATs, they should have reduced capacity of the AT to store tendon strain energy. Here, we further evaluate the idea that the role of the AT in running is to reduce muscle shortening, thereby reducing muscle energy cost. In Chapter 7 we continue with the idea that the role of the AT during distance running is not to store and release strain energy but to reduce muscle shortening; we have evaluated the change in AT stiffness and Erun following a prolonged run in trained distance runners. It was anticipated that the prolonged run would result in dynamic creep and thus reduced AT stiffness. Therefore the amount of strain energy stored and released during each stride should increase. However, we also anticipated that this reduced AT stiffness would result in an elevated muscle energy cost, thus reducing the effectiveness of the additional storage and release of tendon strain energy. Lastly, we offer some general conclusions regarding the role of the AT during distance running and how this relates to muscle energetics in vivo. Furthermore, commentary regarding how this work can be applied to future research, with particular attention to the interaction between in vivo tendon mechanical properties and muscle energetics during exercise. 7 Chapter Two: Energetic Aspects of Running Economy Jared R. Fletcher & Brian R. MacIntosh Human Performance Laboratory, Faculty of Kinesiology, University of Calgary Calgary, AB Canada 8 2.1 Abstract The economy of running has traditionally been studied as the oxygen cost of running, and there is a considerable body of literature that presents economy this way, without consideration of the energy equivalent. Fundamentally, the understanding of the major factors that influence the energy cost of running can be obtained with this approach. However, a recent return to presenting economy as an energy equivalent (Fletcher et al., 2009, Shaw et al., 2014) has allowed a refocusing of the topic on the origin of the need for energy and this approach will provide a more basic understanding of the factors affecting the energy cost of running (Erun). Without muscle contraction, running would be impossible. Yet most of what we understand about Erun ignores the fact that our skeletal muscles require energy to contract and this is where the energy is going. Here, we review the general factors that influence Erun, and try to put them into the context of understanding the role that muscle contraction and muscle energetics plays in contributing to the variability in the Erun. In order to achieve this approach successfully, it is important to understand the determinants of muscle energy cost that are not affected by training. These include, environmental factors, surface characteristics and certain anthropometric features. We contrast this with a presentation of the factors affecting Erun that are affected by training, including certain anthropometric features, muscle and tendon properties and running mechanics. Summarizing the key features that dictate muscle energy cost during distance running has allowed us to consider the influence of biomechanics (limb weight and length, AT and vertical ground reaction force etc) and physiology (force-length-velocity properties of muscle) in dictating the muscle energy cost, and therefore Erun. 9 2.2 Introduction 2.2.1 Importance of Erun to distance running performance Endurance running performance is determined by a combination of physiological factors. These include a high maximal oxygen uptake (𝑉𝑂!!"# ), the ability to minimize disturbances to homeostasis at a higher fraction of 𝑉𝑂!!"# and the energy cost required to run (Erun) at that high fraction of 𝑉𝑂!!"# . The ability to tolerate disturbance to homeostasis may also be important. With few exceptions, world-class marathon running performances are achieved in runners who possess 𝑉𝑂!!"# values above 75 ml•kg-1•min-1 and the portion of 𝑉𝑂!!"# that can be sustained for the marathon distance is at least 80% of 𝑉𝑂!!"# (Foster and Lucia, 2007). Using the ACSM metabolic equations for the energy cost of running over level ground, a mean 𝑉𝑂! of 71.9 ml•kg1 •min-1 is required to achieve the current marathon world-best (2:03:23, IAAF). Given this assumed 𝑉𝑂! to run at this speed (342 m•min-1) and assuming the runner could maintain 80% of 𝑉𝑂!!"# for the marathon, this effort would require a 𝑉𝑂!!"# of nearly 90 ml•kg-1•min-1. Values this high have rarely, if ever, been reported in distance runners. If an elite marathoner could sustain 90% of 𝑉𝑂!!"# for over two hours, this would require a 𝑉𝑂!!"# of 80 ml•kg-1•min-1 to surpass the current world marathon best time. 𝑉𝑂!!"# values this high have been reported in elite runners but it seems unlikely that elite runners could sustain relative intensities of 90% 𝑉𝑂!!"# for this time frame (Billat et al., 2001). So how are such phenomenal running performances possible? It is likely the energy demanded by an elite marathoner to cover the distance in world-record time is significantly lower than that estimated by the ACSM metabolic equation. Assuming the marathon distance is sustained at a relative intensity of 80% of 𝑉𝑂!!"# , and Erun was reduced by 10%, then the 10 required 𝑉𝑂!!"# would only be 81 ml•kg-1•min-1. Values this high have been frequently reported in elite distance runners (Pollock, 1977; Zhou et al., 2001). Thus, Erun can greatly influence the speed at which the marathon can be sustained, and will likely be a key determinant in breaking the 2-hour mark for the marathon (Joyner et al., 2011). These estimates, however, do not consider that Erun may increase over the course of the run (Brueckner et al., 1991; Petersen et al., 2007). This phenomenon, which has been largely unexplored in elite distance runners, may be one of the main contributors preventing a sub 2-hour marathon (Fletcher et al., 2011) and further highlights the important impact of Erun on distance running performance. It is known that Erun is likely influenced by a number of physiological and biomechanical factors and several excellent reviews have been written on the topic in the last 25 years (Anderson, 1996; McCann and Higginson, 2008; Morgan et al., 1989; Morgan and Craib, 1992; Saunders et al., 2004). Recently, we have estimated that the active muscle energy cost represents a substantial portion of the total metabolic cost of running (Fletcher and MacIntosh, 2014). Specifically, we have estimated that the energy cost of triceps surae muscles contraction during the running stride of highly-trained runners represents nearly 25% of the total metabolic cost of running. This proportion increases to nearly 40% in lesser-trained male and female runners (Figure 2.1). The energy cost of other active muscles would also contribute to the total metabolic cost of running. Consequently, understanding the specific factors that dictate the muscle energy cost during running offers a unique understanding of the underlying factors which might also dictate Erun. However, to date the factors which dictate the muscle energy cost during running have not been given appropriate consideration. 11 2.2.2 Quantifying the energy cost of running It has long been established that there exists a linear relationship between running speed and oxygen uptake in humans (Henry, 1951; McMiken and Daniels, 1976). Erun has predominantly been expressed as the steady-state 𝑉𝑂! (mlŸkg-1Ÿmin-1) at a particular running speed (Daniels, 1985); a low 𝑉𝑂! at a given speed implying a ‘good running economy’. A good running economy (low Erun) further suggests success in or previous training for athletic endurance performance (Conley and Krahenbuhl, 1980; Daniels and Daniels, 1992; Daniels, 1985); therefore, considerable effort was made in assessing distance running performance potential by measuring 𝑉𝑂! at a given submaximal speed. For example, Dill (1965) found that the 𝑉𝑂! of a champion marathoner was 17% lower than a champion miler running at the same speed. Similar findings that marathoners may be more economical than runners specializing in shorter distances have frequently been reported (Costill et al., 1973; Pollock, 1977). This observation is probably a consequence of the measured speed being better suited to the marathoners’ typical training paces. Furthermore, 𝑉𝑂! is frequently found to vary in similarly-trained runners of equal abilities (Daniels, 1974) and has been identified as an important factor in the success of East African distance runners (Lucia et al., 2006; Wilber and Pitsiladis, 2012). ATP is resynthesized from ADP and Pi from the energy released during oxidative phosphorylation. O2 is consumed when it accepts electrons at the end of the electron transport chain to form ATP via ATP synthase. Thus,  𝑉𝑂! reflects the quantity of ATP used when aerobic metabolism can provide all of the energy at a given running speed. This is only true: 1) when sufficient time is given to achieve a physiological steady-state and 2) when the speed is less than that which results in accumulation of blood lactate. This latter point is important because at 12 speeds greater than this, steady-state conditions are unlikely as a result of the 𝑉𝑂! slow component and non-aerobic metabolism contributes to the energy cost. Berg (2003) conceded that measurement of 𝑉𝑂! was reliable but suggested that using the 𝑉𝑂!  to assess Erun may not be entirely sound. He recognized that  𝑉𝑂! does not account for the type of substrate utilized, a factor that modifies the energy equivalent per volume of oxygen metabolized. This fact has been given previously to mistakenly justify the expression of Erun as simply a 𝑉𝑂! . However, substrate selection may be important in long duration events where sparing of muscle and liver glycogen is important. As a consequence, Berg (2003) suggested it was important to consider the respiratory exchange ratio and complete the calculation of Erun. Based on this notion, we suggested that the measurement of the energy cost to run a given distance (kcal or kJ per km) was a more sensitive and more appropriate measure of Erun than 𝑉𝑂! alone (Fletcher et al., 2009). Recently, these suggestions were confirmed, showing that this method of expressing Erun in terms of energy cost is a more valid and reliable measure of Erun compared to 𝑉𝑂! or O2 cost alone (Shaw et al., 2013; Shaw et al., 2014). Despite the vast array of research on the aerobic demand of running at a common speed, comparing athletes at the same absolute running speed does not account for differences in the speed associated with the lactate threshold (sLT), which is an estimate of the anaerobic threshold. The anaerobic threshold is defined as the highest sustained intensity of exercise for which measurement of oxygen uptake can account for the entire energy requirement (Svedahl and MacIntosh, 2003). Consequently, by not accounting for differences in sLT between runners, the same absolute running speed represents a different relative speed for each runner. Expressing Erun at a common relative speed below the lactate threshold is important to ensure 13 that a 𝑉𝑂! steady-state can be achieved and to minimize differences in substrate use. We (Fletcher et al., 2009) and others (Costill et al., 1979) have demonstrated that the respiratory exchange ratio (RER) increases as a function of running speed and with it, the energy made available per liter of oxygen increases (Lusk, 1928). Lastly, it seems logical to measure Erun at a speed that relates to some competition distance, like the marathon (ie. at a similar intensity relative to the lactate threshold) as long as that speed is below the speed at which lactate accumulates. Despite the expression of Erun in terms of energy having been established in the 1960s (Margaria et al., 1963), it is only recently that studies have taken advantage of the additional information given by the RER (Albracht and Arampatzis, 2013; Di Prampero et al., 1986; Di Prampero et al., 1993; Fletcher et al., 2013a; Franz et al., 2012; Mooses et al., 2013; Pialoux et al., 2008; Shaw et al., 2013). Therefore, we suggest appropriate expression of Erun should be done in terms of units of energy (kcal or kJ) and per unit distance (per km) and per body mass (kg) rather than simply a 𝑉𝑂! to best reflect the actual energy cost and to permit comparisons when the absolute speed of running is different. In calculating Erun in this way, we were able to address such issues as the apparently paradoxical negative relationship between 𝑉𝑂!!"#  and Erun (Morgan and Daniels, 1994; Pate et al., 1992) and apparent differences in Erun in similarlytrained male and female runners (Daniels and Daniels, 1992; Fletcher et al., 2013a; Helgerud, 1994; Helgerud et al., 2010). 2.3 Skeletal Muscle energetics Without muscle contraction, running would be impossible. Yet most of what we understand about Erun ignores the fact that our skeletal muscles require energy to contract and 14 this is where the energy is going. Here, we review the general factors that influence the energy cost of running, and try to put them into the context of understanding the role that muscle contraction and muscle energetics plays in contributing to variability in the Erun. The energy cost of muscle contraction is primarily dictated by the rate at which ATP is hydrolyzed. The primary use of ATP within the muscle can be divided into two portions: the energy cost of cross-bridge cycling and of ion pumping. 2.3.1 Skeletal muscle energetics 2.3.1.1 Cross-bridge turnover and activation (ion pumping) Muscle energy cost in vivo arises from cross-bridge turnover as well as non-cross bridge processes. The latter being the energy cost of ion pumping, primarily from the Na+-K+ ATPase and the sarco-endoplasmic reticulum Ca2+ ATPase (SERCA) pumps. The energy cost associated with Ca2+ re-uptake represents the majority of the energy cost associated with ion pumping (Homsher and Kean, 1978). There are two main methods to estimate the non-cross-bridge energy cost of contraction. Typically, this energy cost is calculated during isometric contractions when muscle length is changed above optimal length and estimated for a long length where filament overlap would be prevented (Barclay et al., 1993) or by preventing cross-bridge interactions pharmacologically. In doing so, cross-bridge cycling, and thus ATP use at the cross-bridge site, is prevented (Young et al., 2003). Eliminating energy for cross-bridge interactions yields the energy use for non-crossbridge function. This is assumed to be primarily for ion pumping. Excitation-contraction coupling (E-CC) is the sequence of events in a muscle including the following: an action potential is propagated along the surface membrane and into the 15 transverse tubules; Ca2+ is released from the sarcoplasmic reticulum (SR); Ca2+ is then bound to troponin, causing a re-configuration of tropomyosin and the ultimate binding of the myosin head to actin; ATP is broken down by actomyosin ATPase, releasing energy and causing the myosin head to swing resulting in force development and/or translation of the actin filament. This E-CC includes passive activation as well as the energy requiring cross-bridge interactions. Energy is not required for action potential propagation or Ca2+ release. Chemical energy is required in order to regenerate ATP that is used for crossbridge turnover (section 2.3.1.2). Relaxation occurs when the Ca2+ is actively transported out of the cytoplasm and back into the SR. Energy is required by the SERCA pump, which shuttles 2 Ca2+ ions per ATP hydrolyzed (Rüegg, 1986). When contractions are of short duration, the energy cost is greater because of the fibre shortening against the series elastic structures during force development. . Combining the energy cost of SERCA and Na+-K+ ATPase pumps accounts for 30-40% of the energy used during an isometric contraction, where the energy associated with cross-bridge cycling as a result of shortening is not considered (Barclay et al., 1993; Barclay, 1996; Homsher et al., 1972). When shortening is considered, the proportion of the energy cost attributed to noncrossbridge ATPases is much less because shortening considerably increases the cross-bridge turnover (Smith et al., 2005). Considering this cost of shortening, an isometric contraction is more costly in the initial part of the contraction when force is rising because the fibres shorten against the series elastic components of the muscle. During locomotion, most muscle contractions are of short duration, so this is a relevant aspect of the energy cost of locomotion. 2.3.1.2 Turnover of cross-bridges 2.3.1.2.1 Isometric contractions 16 During an isometric contraction, energy cost is elevated compared to the resting state. Since, by definition no external work is performed during an isometric contraction, the energy cost must arise primarily from time-dependent cross-bridge cycling. Barclay et al. (2010a) have previously estimated this rate to be 1.5 ATP split•s-1 in frog sartorius muscle at 0°C. In human muscle, assuming the ATP turnover rate is 1 mmol•kg-1 wet wt•s-1 (Katz et al., 1986) and a crossbridge concentration of 0.18 mM (Barclay et al., 2010b), each cross-bridge splits 5.6 ATP per second. That is, the ATP splitting cycle requires 180 ms. A cross-bridge duty cycle of 0.3 (Barclay et al., 2010a) would require the cross-bridge be attached for 60 ms; the remaining 120 ms presumably being required for the cross-bridge to return to a state from which it can attach again. This duty cycle would be fibre-type specific; a shorter duty cycle associated with fasttwitch fibres. Thus, the energy required during an isometric contraction is dependent solely on the required force (which dictates the number of cross-bridges required to support that force) and the contraction duration. However, recognizing that as force develops in an isometric contraction, the tendon is stretched thereby requiring shortening of the fibres, additional crossbridge cycles will be required. 2.3.1.2.2 Shortening contractions Shortening at a velocity greater than a critical velocity can increase the rate of turnover of individual cross-bridges. This critical velocity depends on the rate of shortening being fast enough that the isometric crossbridge turnover is exceeded and is different for myosin isoforms: faster for fast-twitch myosin isoforms. This critical velocity is the equivalent of a cross-bridge sweep (per half sarcomere) per isometric cross-bridge cycle time for each half sarcomere fibre length. At the optimal velocity, that for which efficiency is maximal, the energy cost of a 17 shortening contraction is 2-3 fold greater than that expected during an isometric contraction. This increase in energy demand is referred to as shortening-induced increase in ATP turnover (Woledge et al., 1988). The amount of ATP split, and therefore energy use by the muscle is proportional to the amount of shortening within each half sarcomere and is dependent on the working stroke (or cross-bridge sweep) of each cross-bridge. This working stroke distance increases as a function of shortening velocity (Barclay et al., 2010a). At velocities greater than that associated with optimal efficiency, the cross-bridge remains attached beyond the filament displacement at which their force reaches zero, opposing filament movement generated by crossbridges at earlier stages of their attachment cycle. These cross-bridges thus contribute to the decreasing average force per cross-bridge as shortening velocity increases. Therefore the shortening energy cost is also proportional to shortening velocity (Hill, 1938; Homsher and Kean, 1978). 2.3.2 Energy cost of muscle contraction Running can be considered a series of voluntary muscle contractions; the force of contraction being dictated by the running speed and the controlled motion of the lower leg. The required level of voluntary muscle activation is primarily determined by the force-lengthvelocity relationships of the muscle and the need for force or movement through a specific angular displacement. The level of muscle activation a combination of motor unit recruitment and rate coding as measured by surface EMG, dictates the energy cost since the rate of energy consumption depends on the number of fibres activated and the rate and number of cross-bridge cycles required. The volume-specific rate of energy consumption is greater in fast-twitch 18 muscles during isometric contractions and slow shortening because faster muscles have higher rates of time-dependent cross-bridge cycling (Katz et al., 1986; Rall, 1985). The energetic cost of generating force is also dependent on the average length of the activated muscle fibre. For muscles having similar fibre type compositions and operating under similar levels of activation and shortening velocities (relative to length), muscles with shorter fascicles can be expected to consume proportionally less ATP per unit force generated compared to muscles with longer fascicles (Roberts et al., 1998b). The volume of active muscle recruited to generate the required force is the product of fascicle length and active cross-sectional area. Consequently, a muscle with longer fascicles will require a greater active volume of muscle and therefore, a greater amount of ATP will be consumed. Thus, it seems likely that muscle architecture is adapted to economize the metabolic cost of generating the required force. The link between muscle fascicle length, tendon stiffness and muscle contractile energetics has been modeled previously (Lichtwark and Wilson, 2008). These authors have demonstrated that maximum efficiency is achieved over a wide range of muscle fascicle lengths and tendon stiffness values; however, most importantly, these authors demonstrated that different muscle fascicle lengths and tendon compliance combinations are required to maximize contractile efficiency depending on the gait conditions (walking vs running) and speed. Their results suggest different fascicle length-tendon compliance combinations are selected to keep muscle shortening velocity (and therefore the amount of muscle work) low. This reduces the required volume of activated muscle according to the force-velocity relationship. This idea is wellmatched with the energy cost of running considering that during running, the triceps surae muscles perform little work (Ishikawa et al., 2007; Roberts et al., 1997) and economy of muscle force generation, rather than efficiency, may be the more relevant feature. 19 2.3.2.1 Force-length relationship The force of contraction during running is dictated by the running speed and the controlled motion of the lower leg. It has been known for some time that the force a muscle can produce depends on its average sarcomere length whereby an optimal muscle length exists. Muscle contraction at longer or shorter lengths than this optimal length results in less muscle force (Gordon et al., 1966; Ramsey and Street, 1940). As it relates to Erun, for a given amount of muscle force required to run a particular speed, the required level of activation can be minimized if the muscle is operating near optimal length. In keeping the level of activation low, muscle energy cost and therefore Erun can be reduced (Stainsby and Lambert, 1979). The classic experiments of Gordon et al. (1966) were the first to relate this force-length curve to the amount of myofilament overlap and the corresponding number of cross-bridges available to develop force. At sarcomere lengths above and below optimum, the measured tension decreases as a function of the number of active cross-bridges available to develop the tension. The force-length relationship has been expanded to in vivo human sarcomere length estimation (Walker and Schrodt, 1974) and the quantification of the force-length relationship of whole muscle using ultrasound (Austin et al., 2010; Finni et al., 2001; Ichinose et al., 1997; Maganaris, 2003). An important consideration, however, is that the classic force-length curve of Gordon et al. (1966) applies only to maximally active muscle fibers. As it relates to whole-body energy cost, very few exercises are performed under conditions of maximal activation. During submaximal stimulation, the length at which peak force occurs is longer than the optimal length associated with maximal stimulation. This is apparent both in situ (Rack and Westbury, 1969) and in vivo (Ichinose et al., 1997) and appears to be associated with enhanced 20 contractile response at long muscle lengths (Lambert et al., 1979; Rassier et al., 1999). This probably relates to a decrease in intermyofilament spacing and subsequent changes in calcium sensitivity at long sarcomere lengths (Fuchs and Wang, 1991; Stephenson and Wendt, 1984; Wang and Fuchs, 1994). At short muscle-tendon unit lengths, the tendon is slack (Huijing et al., 1989). Thus, muscle shortening is required simply to take up the tendon slack prior to the development of any appreciable joint moment. This can be seen in vivo as a large toe region of the force-tendon elongation curve where considerable stretch the tendon occurs with little force development (Figure 2.2). For a given muscle-tendon unit length, a higher force is associated with more tendon elongation and a corresponding additional muscle shortening. This additional shortening could result in the muscle sarcomere length being shorter than the optimal and would necessarily increase the level of muscle activation needed to reach a given force (Ichinose et al., 1997). 2.3.2.2 Force-velocity relationship The relationships between mechanical work, efficiency and speed of shortening were first proposed by AV Hill almost 100 years ago (Hill, 1922). Since the rate of mechanical work (or power output) is the product of force and velocity, the maximum power output that can be generated by a muscle, or group of muscles, is defined and limited by their force-velocity relationships. The now commonly used hyperbolic force-velocity equation of Hill (1938) dictates the conditions for power output. Since power is the product of force and velocity, the power-velocity relationship is also bound by this equation. However, under conditions of submaximal activation, the velocity at which peak power is achieved is slower and peak power achieved is lower. This has been 21 extensively described previously (Chow and Darling, 1999; MacIntosh et al., 2000; Sargeant, 2007). For a given force requirement, the level of activation can be minimized if the muscle can operate at a lower shortening velocity (Figure 2.3). In running, where a set force must be generated, the level of activation can be minimized if the velocity of shortening is low (Fletcher et al., 2013b). 2.3.2.3 Motor unit recruitment The muscle’s in vivo force-length and force-velocity relationships dictate the magnitude of activation required to achieve a given force and velocity of shortening (Praagman et al., 2006). The force-velocity relationship dictates that force production for a given level of activation is maximal when that force can be developed isometrically (Roberts et al., 1997, Gabaldon et al., 2008, Biewener, 1998, Fenn and Marsh, 1935) and decreases as shortening velocity increases. Stainsby and Lambert (1979) suggest that the major determinant of metabolic cost of contraction in voluntary movement should be motor unit recruitment. This notion is consistent with the observed increase in EMG during cycling, which has a minimum at a unique cadence associated with a given power (MacIntosh et al., 2000), and this cadence is closely related to the optimal cadence for best efficiency (Coast and Welch, 1985). Load, shortening and velocity of shortening have less impact on the magnitude of energy requirement (Stainsby and Lambert, 1979). For submaximal contractions like those imposed during running, the level of activation needed to generate a given force can be minimized when the fascicles are allowed to develop force isometrically. 22 Keeping the underlying factors dictating energy use in muscle in mind, attention will turn to the energy cost of running: those factors that are not affected by training will be considered. This will be followed by an examination of those factors which can be affected by training. 2.4 Factors not affected by training Erun can acutely change by factors other than those factors related to training. These factors include environmental factors (wind, temperature, altitude), surface features and footwear. 2.4.1 Environment 2.4.1.1 Wind The energy required to overcome wind-resistance is a function of the runner’s frontal surface area (section 2.4.3.3), air density (altitude, humidity and pressure) and the wind velocity. Pugh (1971) found that work required to overcome wind resistance was a linear function of running speed and wind velocity-squared. As such, when running at speeds approaching the 2hour marathon barrier (5.8 m•s-1), the extra energy required to overcome air resistance is approximately 8% higher compared to running with no air resistance (Pugh, 1970). This extra energy can be nearly completely abolished by drafting behind other runners, which saves 80% of the extra energy required to overcome wind resistance (Pugh, 1971). However, wind also serves a thermoregulatory function in that cooler air crosses the skin during running, allowing for greater heat loss by convection. This may result in lower heat storage, and a longer exercise duration as a consequence. Presumably the extra energy is required due to the need to generate 23 greater propelling force. This would relate to the need for increased motor unit recruitment in muscles contributing to the forward propulsion. 2.4.1.2 Temperature The environmental temperature can certainly limit running performance since high environmental temperatures lowers a runner’s ability to dissipate heat. Heat exchange between the body and the environment is defined by the relative impact of the metabolic rate (ie. Erun), which is balanced by heat loss by convection, radiation and evaporation (Cheuvront and Haymes, 2001). Thus, a lower Erun for a given environmental temperature and humidity will result in less heat storage, and a longer exercise duration is permitted. Similarly, a runner with a comparatively low Erun can perform at a higher metabolic rate, corresponding to a faster speed, for the same level of heat storage. Where less heat is generated, less energy is required for peripheral circulation as warmed blood from the core is transferred to the skin (MacDougall et al., 1974; Rowell et al., 1969). Pulmonary ventilation is also elevated in hyperthermic conditions (Chu et al., 2007), which may also explain the elevated Erun, owing to an elevated work of breathing (COAST et al., 1993). 2.4.1.3 Altitude Measured at a common absolute speed (255 m•min-1), sea-level oxygen cost of running is approximately 4.5% greater than that measured at an altitude of 2300 m (Daniels et al., 1977). The difference in oxygen cost measured on the treadmill is 4%, so most of the altitude dependent difference is not related to overcoming air resistance. The only mechanism suggested for the lower oxygen cost at altitude was the possibility of differences in the anaerobic energy 24 contribution at altitude (Daniels et al. 1977). The possibility for anaerobic contribution at altitude when there was not at sea level relates to the compromised maximal oxygen uptake and the lower intensity associated with the anaerobic threshold. Any contribution by anaerobic metabolism would decrease the oxygen demand, even if the total energy cost was not different. There is another possible contributing factor. When converting the oxygen cost to the energy cost, the energy equivalent of the oxygen uptake increases at altitude, so for the same energy yield, oxygen uptake would be lower. When running the same absolute speed at altitude as at sea level, this speed represents a greater speed, relative to the compromised lactate threshold. At a greater relative speed, the RER will be closer to 1 and the energy yield per volume of oxygen will be higher. At an altitude of 2300m, the measurements of oxygen cost were likely made at a speed at altitude which represents a relative speed which is approximately 10-15% faster as a result of the compromised lactate threshold at altitude (Daniels, 1970; Faulkner et al., 1968; Friedmann et al., 2004). This elevated relative speed is accompanied by a higher RER and a corresponding increase in the caloric equivalent of oxygen uptake. An increase in the speed relative to the lactate threshold (sLT) of 10-15% represents an increase of more than 1.5% in the caloric equivalent of oxygen. For example, at 90% sLT, the caloric equivalent of oxygen is approximately 4.97 kcal•L-1 (Fletcher et al., 2009). A 10% increase in the running speed (to 99% sLT) raises the caloric equivalent to nearly 5.04 kcal•L-1; an increase of 1.2% in the energy equivalent of oxygen uptake. This would impact the measurement of Erun between sea-level and altitude conditions. If the sea-level measurements were made at a relative intensity of 90% sLT, the RER at this speed should be approximately 0.93 (Fletcher et al., 2009). Using the 𝑉𝑂! data presented by Daniels et al. (1977) at 255 m•min-1, this represents a caloric cost of 0.99 kcal•kg-1•km-1 for his 25 58 kg runners. That same running speed at altitude would represent a speed approaching (or exceeding) the sLT. At this speed, the RER would be close to 1.00 and the equivalent caloric cost becomes 0.96 kcal•kg-1•km-1. Taking into account the energy yield per litre of oxygen accounts for more than half of the 4% difference in oxygen cost between altitude and sea-level treadmill running and yields a difference in sea-level vs altitude Erun of 0.03 kcal•kg-1•km-1. It was also hypothesized that the thinner air at altitude presents less resistance to ventilation, and therefore a lower work of breathing at altitude. However, these authors showed that pulmonary ventilation at altitude was 15-20% greater compared to at sea-level (110 L•min-1 vs. 96 L•min-1) and thus could not explain the lower oxygen cost at altitude. Estimating the energy cost of ventilation at altitude (96 L•min-1) and at sea-level (110 L•min-1) according to Mazess (1968), the energy cost of ventilation would have been 0.11 and 0.08 kcal•kg-1•km-1 at sea-level and at altitude, respectively. Thus, the lower resistance to ventilation at altitude (0.03 kcal•kg-1•km-1) likely explains the lower Erun at altitude when Erun is presented as an energy equivalent. Taken together, the differences between sea-level and altitude oxygen cost may likely be explained by the lower work of ventilation, increased energy per litre of oxygen uptake and possible anaerobic contribution that was not accounted for. 2.4.2 Surface Features 2.4.2.1 Friction Running straight ahead at a constant speed on a dry, smooth, flat surface requires friction between shoe (or foot) and surface (Frederick, 1986). When on a slippery or wet surface or when changing speed or direction, subjects tend to modify their kinematics (and therefore use a 26 less-than-optimal movement pattern) to compensate for surface characteristics (Frederick, 1983). Presumably, this also elevates Erun, although further research is required to determine the magnitude of this increase as a result of the less than optimal kinematics. 2.4.2.2 Surface stiffness Runners are capable of adjusting their leg stiffness, allowing them to run with similar kinematics over a variety of different surfaces with varied stiffness (Ferris et al., 1998). If a runner was not able to quickly adjust leg stiffness based on the surface, the vertical displacement of the centre of mass would be greater as surface stiffness decreased. There exists an optimal surface stiffness over which a runner’s best performance can be achieved (McMahon and Greene, 1979). These authors have demonstrated that a ‘tuned’ track surface, could be built for which ground contact time is decreased, and there is an increase in stride length and ultimately this could result in 2-3% improved performance times. These improvements in performance were most pronounced over long-distance races, for which ground contract time and stride length is related to Erun. (Kram and Taylor, 1990). Later, Kerdok et al. (2002) examined the energetic implications of running surfaces of variable stiffness, the lowest of which was within the range of stiffness tested by McMahon and Greene (1979). They showed that while the running mechanics during the support phase were essentially unchanged, Erun increased as a function of surface stiffness. The Erun was reduced with lower surface stiffness. These authors postulated that an increase in energy rebound from the compliant surface in the latter portion of ground contact contributed to the lower Erun. 27 2.4.2.3 Other surface features Erun is also elevated on soft and uneven surfaces, as evidence by Erun being significantly elevated while running on sand compared to grass or concrete (Lejeune et al., 1998; Pinnington and Dawson, 2001; Zamparo et al., 1992). The elevated Erun on sand has been attributed to a reduction in the re-utilization of elastic energy and/or the energy lost due to backwards translation of the foot during push-off. It has also been hypothesized that an elevated muscletendon work while running on sand contributes to the elevated Erun (Lejeune et al., 1998). In terms of the muscle energetics presented earlier, these mechanisms (foot slip, increased work and decreased tendon strain energy release) translate to an increased shortening and probably increased motor unit recruitment. Both of these factors would increase the energy cost of muscle contraction. 2.4.2.4 Footwear It has been suggested that runners attempt to maintain a specific (optimal?) movement pattern during running (Nigg and Wakeling, 2001), which therefore explains the oftendemonstrated lack of significant change in kinematics and/or kinetics between footwear designs (Cole et al., 1995; Dufek et al., 1991; Nigg et al., 1987). Adapting to a less than optimal movement pattern would result in changes in muscle activation and which would manifest as differences in metabolic cost. It has been suggested that a potential mechanism by which footwear might reduce Erun is because footwear serves to reduce some of the impact shock. A reduction in Erun of 3% with well-cushioned shoes compared to poorly-cushioned ones support this notion (Frederick et al., 1986). These authors developed a ‘cost of cushioning’ hypothesis whereby a portion of the 28 measured Erun in well-cushioned shoes is reduced because less muscle activation is required to brace the force of impact with the ground. To support this hypothesis, Erun was compared between well-cushioned shod and unshod conditions. The former condition would incur an estimated increase in Erun as a result of the mass of the shoes. Despite the added mass of the shoes, Erun was not different between shod and unshod conditions. These results contrast with those of Perl et al. (2012) who demonstrate a 2-3% reduction in Erun while barefoot running on a treadmill compared to shod, despite accounting for differences in shoe mass, footstrike type (forefoot vs rearfoot) and stride frequency. The authors attribute the reduction in Erun in the barefoot condition to more elastic energy storage and release in the longitudinal arch. At the speed at which their subjects ran (3 m•s-1), the additional energy release required to account for the 2-3% difference in Erun between conditions would be approximately 17-24 J•stride-1. This additional energy release was estimated from the absolute energy cost differences between conditions and the estimated stride length at that running speed (Cavanagh and Kram, 1989). This seems feasible given that approximately 34 J•stride-1 is stored in the arch of the foot during running (Ker et al., 1987). Presumably cushioned shoes reduce this energy storage and release. To eliminate any confounding factors such as shoe construction, Tung et al. (2014) isolated the effect of cushioning on Erun by attaching the same cushioning foam to the belt of a treadmill. In so doing, Erun was reduced by 1.6% when runners ran unshod on the cushioned belt in comparison to running unshod without the cushioning. Interestingly, Erun was not different between shod and unshod conditions on a normal treadmill belt, likely because the beneficial effects of cushioning were balanced by the detrimental effects of added shoe mass. These results suggest 1) shoe mass can have a meaningful influence on the measured Erun and 2) there exists a 29 trade-off between running in very light running shoes at the expense of extra cushioning in order to minimize Erun. Runners are also able to assess shoe comfort reliably (Hennig et al., 1996) and it has been hypothesized that comfort could relate to performance (Nigg, 2001). In fact, oxygen cost was 0.7% lower in shoes deemed ‘most comfortable’ compared to those deemed ‘least comfortable’ (Luo et al., 2009). Further insight into the specific mechanism for a lower Erun between footwear (kinematics, kinetics, muscle activity etc) should be investigated. 2.4.3 Anthropometry 2.4.3.1 Ankle & Foot Morphology Erun is determined primarily by the energy needed for muscle contraction of sufficient force to support body weight during the stride duration (Kram and Taylor, 1990). Therefore, average muscle force and thus muscle energy cost is related to the average Fz during stance, as dictated by body mass and the Fz moment arm and the moment arm of the Achilles tendon (Carrier et al., 1994; Ker et al., 1987). These moment arms are shown in Figure 2.4. The ratio of Fz moment arm to that of the Achilles tendon is referred to as the gear ratio. Often, the Fz moment arm length is interpolated from known forefoot length. In this case, the ratio of forefoot length to AT moment arm is referred to as the foot lever ratio (Kunimasa et al., 2013). Both Fz and AT moment arms can be altered by changing ankle joint kinematics during the stance phase. This has important implications to Erun since changes in joint angle configuration at touch-down result in changes in both the Fz and AT moment arms. The relative change in the gear ratio for a given Fz will determine the magnitude of the required muscle force. Reductions in the gear ratio result in a reduction in muscle forces. 30 It has previously been suggested that the Achilles tendon moment arm length changes with ankle angle, the maximum of which occurs at large dorsiflexion angles (Fath et al., 2013; Maganaris et al., 1998; Maganaris et al., 2000). However, we (Fletcher and MacIntosh, submitted) and others (Hashizume et al., 2012) have recently demonstrated that this may not be the case; Achilles tendon moment arm length remains constant throughout the range of motion. Thus, additional ankle excursion during stance may not result in a greater muscle force required to generate a given ankle moment during the stance as a result of a decreasing AT moment arm length. During running, the ankle angle at touchdown is nearly 90 degrees, with elite runners exhibiting less ankle excursion during stance compared to good runners (Cavanagh et al., 1977; Williams and Cavanagh, 1987). A small excursion translates to lower angular velocity and a corresponding slower velocity of contraction. A slower velocity of contraction results in lower level of activation needed to generate a given force and consequently lower energy cost of muscle contraction. A shorter AT moment arm, measured at rest, is associated with a lower Erun (Mooses et al., 2014; Scholz et al., 2008). The advantage of a short AT moment arm in reducing Erun has been attributed to increases in the elastic energy storage/release from the AT during running since larger AT forces for a given joint moment are required with a short AT moment arm; more elastic strain energy is stored and released in a tendon stretched to the same magnitude if AT forces are higher. It has been estimated that a reduction in the AT moment arm of 10% would result in a reduction in running 𝑉𝑂! of approximately 4.2 ml•kg-1•min-1 (Scholz et al., 2008). The estimated energy savings of a shorter moment arm are based solely on the extra elastic energy storage from the shorter AT moment arm and ignore the additional muscle energy cost associated 31 with elevated AT force. This muscle energy cost would be considerably higher (Fletcher and MacIntosh, 2014), Shorter moment arms also require smaller muscle shortening velocity to achieve a given joint angular velocity (Nagano and Komura, 2003). This effect may be substantial, given the relatively large angular velocities at the ankle joint during submaximal running (KyroLAINEN et al., 2001). As previously suggested however, the elevated AT force associated with a shorter moment arm may also incur a substantial muscle energy cost (Fletcher and MacIntosh, 2014) and as such, a longer AT moment arm may help reduce Erun by reducing the required muscle force and level of muscle activation to sustain a given joint moment. To support this hypothesis, elite Kenyan long-distance runners, a population known for their exceptionally-low Erun (Larsen, 2003; Wilber and Pitsiladis, 2012), have longer AT moment arm lengths and shorter forefoot lengths compared to similarly-trained Japanese distance runners (Kunimasa et al., 2013). Furthermore, both long AT moment arm and short forefoot lengths are associated with better endurance performance. Considering a lower gear ratio reduces the energy cost of muscle contraction (Biewener et al., 2004; Carrier et al., 1994). Reducing the gear ratio from 2 to 1.5 reduces the estimated triceps surae muscle energy cost by nearly 40% (Fletcher and MacIntosh, 2014), assuming the same amount of shortening. However, a long AT moment arm also necessitates a greater amount of shortening for a given angular displacement. The length of the Fz moment arm is also dictated by the length of the forefoot. Forefoot length is another anatomical feature (along with presumably short or long moment arm lengths) for which humans have evolved, presumably to favor economical walking and running. In relation to body mass, humans possess extremely short forefoot lengths (Rolian et al., 2009). This evolutionary adaptation has long been assumed to benefit bipedal locomotion since short 32 toes require smaller plantarflexor forces to balance the large dorsiflexion moments as a result of Fz (MANN and HAGY, 1979; Weidenreich, 1923). Using kinematic, force and plantar pressure measurements, this hypothesis was tested in a sample of human subjects. It was demonstrated that relatively long forefoot lengths had to generate more than four times the peak flexor force compared to a short-toed individual over a single stance phase (Rolian et al., 2009). The authors suspected that such an increase in force output would lead to at least a small increase in the metabolic cost of running. This seems very likely given that the elevated muscle force would result in a greater active muscle volume and a concomitant increase in energy cost. Thus, it seems logical to suggest that it is the ratio of Fz to AT moment arm lengths, rather than the absolute AT moment arm length itself which dictates the muscle energy cost. 2.4.3.2 Body mass, body composition and mass distribution Not only is absolute body mass an important energy cost parameter in the energy cost of running, but body composition and distribution of that mass may be equally important. Active skeletal muscle is primarily responsible for the energy use, so a body mass consisting of a high proportion of skeletal muscle mass and low fat mass should be advantageous in reducing the energy cost of running over a fixed distance, since transporting metabolically-inactive tissue like fat would come at a metabolic cost. In fact, Kenyan boys show a lower leg circumference than boys of similar age from other continents (Larsen, 2003). This suggests that even lower muscle mass may be advantageous. It is estimated that the energy cost of running (measured as 𝑉𝑂! ) was elevated by 4.5% for every additional kg of load carried distally whereas the energy cost was only elevated by 1% when that same weight was carried on the trunk (Jones et al., 1986). Therefore, minimizing the 33 weight of the swinging limbs, by minimizing fat and muscle weight in these areas should reduce the energy cost of running, as long as the muscle mass necessary to generate the forces and movements is maintained. Since running involves rotation of the limbs, a substantial portion of the body mass should be located at a close proximity to the joint centre of rotation. This serves to minimize the limb moment of inertia, which comprises a substantial portion of the total metabolic cost of running, since joint moment need to impart an angular acceleration is proportional to the moment of inertia (Cavagna et al., 1964; Fenn, 1930). Swinging the limbs during running may come at a substantial energy cost. Using measurements of blood flow as a proxy for energy use by the active skeletal muscles in running guinea fowl, Marsh et al. (2004) were able to conclude that 26% of the total lower limb blood flow (and thus an equal proportion of the metabolic rate) was responsible for swinging the limb. This proportion of the metabolic rate was independent of running speed. Using a device that pulled the leg anteriorly during the swing phase, reducing the need of the muscles to swing the leg directly, Modica and Kram (2005) showed a reduction in metabolic cost by 20%. This estimate was later refined to ~7% of the metabolic cost of running (Warddrip, 2007), the difference likely a result of the device used by Modica and Kram also aiding in forward propulsion (Arellano and Kram, 2014a). The metabolic cost of arm swing has also been addressed: is swinging the arms metabolically beneficial or costly (Arellano and Kram, 2014b)? By having subjects hold their arms in different positions, these authors demonstrated that running with a normal arm swing incurred the lowest metabolic cost (Figure 2.5). While swinging the arms might incur a metabolic cost, these data suggest the arm swing serves to reduce the amplitude of shoulder and 34 torso rotation. Without arm swing, shoulder and torso rotation must increase to counterbalance the rotational angular momentum of the swinging legs. A reduction in the moment of inertia of the swinging limbs can be accomplished either by decreasing the distally-located mass, such as reducing fat, muscle or shoe weight, or reducing the radius over which that mass is rotated. The latter can be accomplished acutely by increasing the knee flexion angle during the swing phase. Assuming similar knee flexion angles, runners possessing short femur and long tibia lengths would also possess a smaller lower limb moment of inertia compared to runners with relatively long femurs and short tibias. Limb moment of inertia may also be minimized during the swing phase by reducing the angle formed between femur and tibia. Elite runners tend to exhibit a more acute knee angle during the swing phase compared to good runners (Cavanagh et al., 1977). However, these authors did not assess Erun and as such it cannot be said with certainty how these kinematic differences are related to Erun. While the assumption that a higher joint moment is required where limb moment of inertia is high remains clear in theory, the influence of limb moment of inertia on the energy cost has only scarcely been examined. Cavanagh et al. (1977) showed small differences in knee joint angle between elite and good distance runners; however these differences were small and not significant. Later, Williams and Cavanagh (1987) showed no difference in limb lengths in runners whose energy costs were different. To date though detecting differences in the energy cost of running as a result of various anthropometric measures and/or masses from the various segments of the body have been difficult, with many studies showing no differences in economy as a result (Cavanagh et al., 1977; Williams, 1985; Williams and Cavanagh, 1987). This can either result from the large sample size requirements and/or the technical error of measurement to detect these small differences in the energy cost. Conversely, the influence of individual 35 differences in lower limb mass distribution and/or moments of inertia on Erun is not as great as theoretically suggested. As suggested by Williams and Cavanagh (1987), there does not appear to be easily identifiable and universally applicable patterns of economical movement that will apply to all runners. 2.4.3.3 Frontal surface area It is apparent that much of what we now know about Erun has been derived from metabolic measurements performed on a treadmill in the laboratory. These measurements are sometimes difficult to extrapolate to overground running given the lack of air and wind resistance in the laboratory. Therefore, it is also difficult to determine with any degree of certainty to what extent the 𝑉𝑂2 is elevated when running overground. The magnitude of difference in 𝑉𝑂2 between overground and treadmill running has been the subject of much investigation (Bassett et al., 1985; Jones and Doust, 1996; Maksud et al., 1971; McMiken and Daniels, 1976). The difference between overground and treadmill running would be related to the energy in overcoming aerodynamic drag. A smaller frontal area reduces the drag (resisting force) opposing the runner’s forward motion (Pugh, 1971). Many researchers have attempted to correct for the additional energy required to overcome wind resistance by imposing some gradient to the laboratory treadmill when measuring Erun. From measurements made of a model runner in a wind tunnel, A.V. Hill (1928) suggested there might be an equivalent work against vertical forces (as in running up a grade of some slope) and the horizontal work required to overcome air resistance; as such, the equivalent treadmill slope could be calculated in order to best simulate the effect of air resistance on overground running. Later however, Pugh (1970) found, as would be predicted from Hill’s 36 (1928) original equation, that the energy cost of running was proportional to wind resistance and thus running speed, but a precise relationship between grade and overground running (where wind resistance could be considered) was not shown. Using a portable Douglas bag system, Maksud et al. (1971) measured 𝑉𝑂2 during track and treadmill running and concluded that at speeds faster than 187 m•min-1, track running resulted in a ‘generally higher 𝑉𝑂2 ’ compared to treadmill running. These authors attributed this difference primarily to the need to overcome air resistance during track running. This would account for an additional 8% energy cost at 358 m•min-1(Pugh, 1970), equivalent to 14:00 over 5,000m. Later, McMiken and Daniels (1976), using a similar gas collection system (Daniels, 1971), could not demonstrate a difference between overground and treadmill running in elite distance runners at speeds up to 260 m•min-1 and concluded that level-grade treadmill running was a valid instrument for the estimation of Erun in distance runners. The following year however, it was reported that track running resulted in a higher Erun compared to running on the treadmill, at least at speeds greater than 255 m•min-1 (Daniels et al., 1977). It would seem from early theoretical observations (Hill, 1928; Pugh, 1971) that overground Erun would be higher than level grade treadmill Erun, and some measurements made using portable gas-collection/measurement systems would confirm these theoretical findings (Daniels, 1971; Maksud et al., 1971). Thus, attempts have been made to correct for these differences by imparting some gradient to the laboratory treadmill in order to best reflect the metabolic cost of overground running. For example, studies have employed grades of 1-2% (Heck et al., 1985; Helgerud et al., 2010; Jones and Doust, 1996; Tegtbur et al., 1991), but only the study by Jones and Doust (1996) has justified the rationale for choosing such a gradient. These authors suggest a 1% grade best reflects the 𝑉𝑂! during overground running; however 37 only at speeds greater than 225 m•min-1 was overground running significantly different than level-grade treadmill running. There are three reasons which confound the use of some gradient when measuring Erun on the treadmill to compensate for wind resistance First, differences in the imposed treadmill slope make it difficult to compare values for Erun between studies. Secondly, the fundamental factors dictating Erun on level ground are different than those factors while running up a slope. When running up a slope at progressively faster speeds, Erun increases in proportion to body mass since the increased energy expenditure when running up a slope is related to the gain in potential energy (van Ingen Schenau, 1979). This may not be the case in uphill treadmill running because of the potential differences during the support phase of running since the supporting leg is moving down the belt during ground contact; foot contact occurs at a similar vertical position on each step (van Ingen Schenau, 1979). Additionally, if the centre of mass was displaced horizontally along the belt, energy would be required to propel the body back up the belt with each step. Furthermore, over level ground, running at progressively faster speeds, the Erun increases as a cubic function of the running speed (Léger and Mercier, 1984), in proportion to body surface area and aerodynamic drag (Pugh, 1971). Lastly, the biomechanics of running up a slope may be different compared to running on the level, as a result of differences in kinematics and/or kinetics of running between the two modes of exercise (Anderson, 1996; Nelson et al., 1972). These differences have been shown to increase the muscle energy cost as a result of greater muscle work during uphill running (Roberts et al., 1997). Above, we have attempted to outline those factors not affected by training which likely affect the energy cost of muscle, and therefore, whole-body Erun. There exist specific anthropometric (eg. limb length) and morphological (eg. ankle and foot anatomy) characteristics 38 that influence the measured Erun. However, it is well-known that Erun is lower in trained distance runners compared to lesser-trained runners (Fletcher et al., 2009; Pollock, 1977) thus it is clear that Erun is likely altered by both short and long-term training protocols. These training strategies have recently been reviewed (Barnes and Kilding, 2014). Below we outline the various factors of Erun that are altered by training and consider the influence of muscle energy cost on those factors. 2.5 Factors affecting Erun that are altered by Training 2.5.1 Anthropometry 2.5.1.1 Body mass Long-distance runners are smaller and lighter than middle-distance runners (Cavanagh and Kram, 1989). Also, elite African runners appear to be of lower body mass (Coetzer et al., 1993) and BMI (Saltin et al., 1995) compared to their Caucasian counterparts. These anthropometric differences appear to have persisted since childhood (Larsen et al., 2004). While little research has examined why body mass confers an athletic advantage, several factors specifically related to the energy cost of muscle contraction may explain this. For example, it is well established that Fz expressed relative to body mass is increased as a function of running speed (Keller et al., 1996). Thus, at a given running speed, the absolute Fz is lower in lighter runners compared to heavier runners. As such, there should be lower energy cost required by the active muscles. Over a wide range of body masses, Taylor et al. (1980) showed that the energy cost of running at a particular speed is proportional to the force exerted by the muscles active during stance. By manipulating the required muscle force by the addition of extra mass it was shown that the increased energy cost was proportional to the mass of the carried load. 39 2.5.1.2 Allometric-scaling for body mass Because body mass must be supported during running, the expression of Erun is typically done as the energy cost relative to the subject’s body mass (in kg). Because oxygen consumption during running does not increase to the same extent as body mass (Bergh et al., 1991; Rogers et al., 1995), allometric-scaling for body mass has been used. The allometric scaling relationship is: 𝑉𝑂2 = aBMb 2-1 where BM is body mass, a is a constant and b is the scaling exponent. Where the relationship between BM and  𝑉𝑂2 is linear, the value of b should be 1 and Erun should be scaled to BM-1. For mammals, ranging in mass from less than 100 g to greater than 1000 kg, the allometricscaling exponent for basal oxygen uptake is generally taken to be ¾ (Kleiber, 1932; SchmidtNielsen, 1984). However, it has been argued that the body mass (BM) scaling factor of ¾ for  𝑉𝑂2 is by no means universal (Glazier, 2005; Heusner, 1987; Welsman et al., 1996) and a wide range of body masses is necessary to accurately assess the relationship. Therefore, appropriate verification of the classic allometric scaling relationship (Equation 2-1) should be used (West et al., 1997; West et al., 2002). This may be particularly important in situations where ranges of BM are relatively small. Such would be the case when scaling within the range of body masses seen in adult human studies (Fletcher et al., 2013a). Furthermore, since the metabolic cost of running is dictated by the muscle energy use, there is no reason to believe the Erun follows the same scaling as basal metabolic rate. Since body mass contributes to the metabolic cost of transport in a linear fashion (Roberts et al., 1998a), it is not obvious that a scaling factor other 40 than b = 1 is justified. The linear relationship between Erun and body mass in trained male and female runners is shown in Figure 2.6. 2.5.2 Muscle Properties Erun at a given relative speed, eg. relative to the speed associated with the lactate threshold, is determined by the total volume of muscle that must be active to support body weight during the stance phase as well as the rate at which that unit volume of muscle transforms energy (Kram and Taylor, 1990; Roberts et al., 1998b). The volume of active muscle is equal to the cross-sectional area (CSA) and the muscle fascicle length. The rate at which the muscle transforms energy is dependent on muscle fibre type. Each of these factors dictating muscle energy cost has been elaborated upon previously (Kram and Taylor, 1990; Roberts et al., 1998b; Taylor, 1985). The rate of muscle energy use (ECM) is given by: ECM = (LF/σ)EM Where L and F are fibre length and muscle force, respectively and σ is the force per unit crosssectional area. EM is the rate at which each unit volume uses energy which for isometric contraction is related to the muscle fibre type; fast-twitch muscles have higher rates of energy use related to the elevated cost of cross-bridge cycling and activation costs (Barclay et al., 2010a; Rall, 1985). Erun increases as a function of running speed since force is developed more rapidly, implying activation of additional motor units. Faster running speed also requires a faster velocity of shortening. At some critical velocity of shortening, the time-dependent turnover of cross-bridges becomes inconsequential and the turnover is related to the velocity of shortening. This critical velocity will be faster with slow-twitch muscle. 41 It is well-established that muscle cross-sectional area increases after a period of resistance training which may (Blazevich et al., 2003; Kawakami et al., 1995) or may not (Blazevich et al., 2007; Seynnes et al., 2007) be accompanied by a concomitant decrease in muscle fascicle length, at least in pennate muscle; changes in fascicle length appearing prior to an increase in muscle CSA. However, to run at a given submaximal speed, an increase in absolute strength as a result of increased muscle CSA would result in a lower relative intensity. This lower relative intensity would not require the need to recruit higher threshold motor units, where the muscle energy cost is higher. This may be one of the explanations by which Erun is improved following a period of strength training. The effect of strength training on Erun has been recently summarized quite well (Barnes and Kilding, 2014). Chronic endurance training may also result in a shift to a higher proportion of slow Type I fibres (Rusko, 1992) further ameliorating the reduction in muscle energy cost at a given speed. 2.5.3 Tendon stiffness Strength training has also been shown to increase tendon stiffness (Kubo et al., 2001a; Kubo et al., 2001b; Kubo et al., 2002b) and increased tendon stiffness has been proposed to be one of the main mechanisms behind an improved Erun following plyometric training (Saunders et al., 2006) despite the apparent reduction in energy storage and return associated with a stiff tendon. It is not well understood how specific mechanical alterations of tendon can affect the energy cost of muscle contractions. It is suggested that the energy cost of contraction is related to the level of motor unit activation and both the amount of shortening and the shortening velocity (Stainsby and Lambert, 1979). Further, the amount and velocity of shortening are 42 dictated by the muscle’s in vivo force-length and force-velocity relationships (Praagman et al., 2006). In fact, many seminal papers make inferences regarding the energy cost of contraction assuming muscles operate over a similar range of the force-velocity relationship, regardless of speed of locomotion or body size (Kram and Taylor, 1990). As such, it would be of interest to know where the muscles operate and how altering the mechanical properties of the tendon affect the operating range of the muscle on their respective force-length and force-velocity curves. Recent classic papers from a variety of species and muscle functional tasks highlight the fact that muscle shortening patterns during natural movement are well matched to their contractile properties (Askew and Marsh, 1998; Lutz and Rome, 1994; Roberts et al., 1997). The fact that muscles operate at the most appropriate loads and favorable velocities based on these contractile demands suggest that contractile properties of muscle and the tendon are well matched. For example, Lutz and Rome (1994) found that the semimembranosus muscle of the frog operated at appropriate lengths and shortening velocities to maximize power output during maximal jumping (Figure 2.7). This effect would not be possible unless the tendon was perfectly tuned (with respect to stiffness and proportion of muscle-tendon length occupied) to allow the muscle to operate at the appropriate length and velocity. This effect is also shown during human cycling whereby vastus lateralis muscle fascicle lengths operate on the plateau of the forcefascicle length relationship during maximal cycling (Austin et al., 2010). At submaximal power outputs, the fascicle lengths operate at longer lengths due to less strain of the tendon. This shift to a longer length can presumably take advantage of the shift in the submaximal optimum of the force-length relationship (Ichinose et al., 1997). Thus, a “functional coupling” (Austin et al., 2010) exists between the mechanical properties of the tendon and the muscle fascicle length and velocity. 43 The tendon can also act in such a way as to minimize the amount of work that is required by the muscle in order to minimize metabolic cost. By minimzing the length change during active muscle contraction, the tendon allows the muscle’s force-length-velocity relationship to be optimized. In theory, if the length change of the whole muscle-tendon unit can be accomodated by the tendon alone, the muscle can operate isometrically, thus minimizing the level of muscle activation required to produce the necessary force. By outfitting wild turkeys with surgically implanted strain gauges on the tendon and sonomicrometry crystals on muscle fascicles of the lateral gastrocnemius, Roberts et al. (1997) were able to measure the force and fascicle length changes of the muscle-tendon unit as the turkeys ran on level ground. What they demonstrated was that the fibers of the lateral gastrocnemius developed force but underwent very little length change during the stance phase of running. Thus, the Achilles tendon was able to take up much of the muscle-tendon length change so the fibre shortening could be reduced. The Achilles tendon also accomodates much of the muscle-tendon unit length change during human running. (Ishikawa et al., 2007; Lichtwark et al., 2007) and thus greatly reducing the shortening-induced muscle energy cost (Fletcher and MacIntosh, 2014). Presumably, the mechanical properties of the Achilles tendon was ‘tuned’ to accommodate the majority of muscle-tendon unit length change. Any change in these mechanical properties would affect the magnitude of length change of the muscle fascicles, and energy cost would necessarily be higher. The relative shortening velocities in running turkeys has recently been measured directly in which the above hypothetical scenario has been shown to occur (Gabaldon et al., 2008). During level running, the shortening velocity of the lateral gastrocnemius were quite low (~0.05 v/vmax), supporting the notion that force can be maximized and activation minimized (as reflected by electromyography) at low shortening velocities. Having to run up an incline required slightly 44 greater V/Vmax ratios (~0.12 V/Vmax) and the volume of active muscle that had to be recruited increased in accordance with the muscle’s force-velocity properties. Regardless of the mechanical energy fluctuations of the body that occur during the running stride, running animals (including humans) moving at a constant speed must generate enough muscle force to intermittently support their body weight. As running speed increases, force must be developed more quickly which requires the recruitment of additional motor units, likely related to the faster, less economical muscle fibers. This results in a higher metabolic cost for the same impulse: greater force is developed in a shorter period of time. This is why the cost of generating these muscle forces determines to a large extent the metabolic cost of running, from rodents to horses (Kram and Taylor, 1990). Of course, this argument is only valid if one considers that during running, the tendon compliance is tuned in such a way that it allows the muscle fascicles to generate force at low shortening velocities. If the tendon is too stiff, then lengthening and shortening is required by the fascicles and the volume of active muscle recruitment increases. If the tendon is too compliant, much of the energy for force generation will be consumed shortening the fascicles even with negligible joint rotation. In the case where high forces need to be generated quickly, as Kram and Taylor (1990) would suggest in fast running, too compliant a tendon would require greater fascicle shortening than that necessary for joint rotation, resulting in higher velocity. This suggests that there may be an “optimal tendon compliance” with respect to minimizing muscle shortening. 45 2.5.3.1 Does an “optimal stiffness” exist to reduce the EC of running? It has previously been shown that in a group of trained distance runners, the most economical runners displayed a higher Achilles tendon stiffness compared to the less economical runners (Arampatzis et al., 2006; Fletcher et al., 2010). The former study demonstrated the opposite to be true in the patellar tendon – that the most economical runners had a lower patellar tendon stiffness compared to the less economical runners (Arampatzis et al., 2006). This opposite result suggests that the roles of these two muscles in minimizing the energy cost during running are different. The reason for these apparently contrary observations with respect to the impact of tendon stiffness on the muscle energy cost is not obvious, however. We contend that the role of the tendon in running is to minimize the energy cost of muscle contraction. Is it possible that energy cost is minimized in the quadriceps muscles by a more compliant tendon, while a stiffer tendon reduces energy cost in the triceps surae? The two muscle groups are known to behave in different ways during running. The quadriceps muscles undergo a stretch –shortening cycle (Gillis and Biewener, 2001), but the triceps surae has little if any stretch and performs predominantly a shortening contraction (Ishikawa et al., 2007). This suggests the role of these two muscles and their tendons during running are different. There are apparent advantages of stiff tendons in some cases, and compliant tendons in other cases. The lengthening of a tendon for energy storage is relevant in stretch-shortening cycles where a substantial pre-stretch of the tendon occurs early in a contraction. A compliant tendon allows more energy conversion of either kinetic or gravitational energy to potential strain energy. This energy can subsequently be released upon shortening. A compliant tendon may also help by allowing the tendon to lengthen during the stretch phase of the SSC and shorten 46 during the shortening phase, thereby keeping fascicle shortening velocity low and reducing the necessary level of activation of motor units required to generate the force. If tendon compliance is optimal, the power-velocity relationship can be optimized because the fascicles are shortening at the appropriate velocity (Askew and Marsh, 1998; Gabaldon et al., 2008). This may be the case in the patellar tendon, which would lend support to previous evidence suggesting a more compliant patellar tendon might decrease Erun (Albracht and Arampatzis, 2006; Arampatzis et al., 2006). Conversely, a more compliant AT requires greater muscle fibre shortening and/or velocity of fibre shortening for a given joint movement. In the AT, force transmission to the joint may be favoured over elastic energy storage and release. This is the case because for a given amount and rate of muscle tendon unit shortening, less muscle fibre shortening is needed with a stiff tendon compared to a compliant one where additional fibre shortening is needed to accommodate tendon stretch. We have recently estimated the tendon strain energy release from the AT and compared that to the estimated muscle energy cost in order for this strain energy storage to occur (Fletcher and MacIntosh, 2014). These results demonstrate that the storage and release of tendon strain energy comes at a considerable muscle energy cost. Therefore, reducing shortening-induced energy cost contributes to a reduced Erun. Thus, it appears that a compliant tendon may be favoured in one case whereas a stiff tendon can be favoured in another. 2.5.4 Running Mechanics 2.5.4.1 Stride Length and stride frequency At speeds below the lactate/ventilatory threshold, where Erun is most appropriately measured, the lowest Erun in humans is generally thought to occur at stride frequencies of 83 to 47 91 strides per minute (Hunter and Smith, 2007). This freely-chosen stride frequency closely resembles the stride frequency associated with the lowest energy cost (Högberg, 1952; Hunter and Smith, 2007), particularly in trained runners, although the self-selected stride frequency is generally 3-8% lower than the optimal frequency when measured at 80% of sLT (de Ruiter et al., 2013). This is shown in Figure 2.8. The difference between self-selected and optimal stride frequency in terms of oxygen uptake is generally small (< 3 ml•kg-1•min-1); however, a larger increase in oxygen uptake is seen when stride frequency is slower than optimal compared to a correspondingly faster stride frequency (de Ruiter et al., 2013; Högberg, 1952). Thus, it seems odd from an energy-saving perspective that runners freely choose a slightly slower than optimal stride frequency compared to a slightly faster one. However, the difference is relatively small and may be inconsequential. At a given running speed, concomitant with a change in stride frequency is a change in stride length, both of which tend to increase with running speed, although proportionally greater increases in stride frequency are seen compared to the increase in stride length, at least at submaximal speeds where the measurement of Erun is valid (Cavanagh and Kram, 1989). When Erun is expressed in terms of the energy cost required to transport a unit body weight a unit distance, small animals use more energy to run a given distance than do large animals (Kram and Taylor, 1990); since small animals must take many fast strides to cover the same distance a large animal can cover in one stride. The mass-specific energy cost is highest in small animals since the muscle fibres of these animals must develop force more quickly, thus requiring greater rates of cross-bridge cycling and Ca2+ pumping (Bárány, 1967; Rome, 1992). In human runners, those runners with longer legs, and thus longer stride lengths should have a lower energy cost; they will take fewer strides to cover a given distance than a runner with small strides. However, the 48 relationship between stride length (expressed either in absolute terms or relative to height or leg length) and Erun in human runners is moderate at best (Cavanagh and Williams, 1982; Williams and Cavanagh, 1987). Running is often considered a bouncing gait whereby humans literally bounce along the ground (Cavagna et al., 1964), storing and recovering kinetic and potential energy as the centre of mass rises and falls with each stride, thus closely resembling a simple spring. By having subjects hop at various speeds on a treadmill, Farley et al. (1991) were able to deduce that a range of hopping frequencies existed whereby the body behaved like a spring, storing and recovering elastic energy. However, at higher than optimal frequencies, the time available to apply force to the ground was necessarily shorter, but more contacts per unit time would be required. Average energy cost would probably relate to the integral of force over some fixed time, rather than per bounce. Below the optimal frequency, the body did not behave in a springlike manner and the recovery of elastic energy was reduced. Clearly there is a trade-off between ground contact time, and the requirement to generate force rapidly and the ability to generate large forces over a relatively long period during the stance phase, which serves to minimize Erun. The fact that runners tend to choose a stride frequency slightly lower than optimal frequency suggests a greater importance is placed on maintaining ground contact time (and thus allowing a slower recruitment of muscle fibres) over maximizing the storage and release of elastic energy. The self-selected stride frequency should be the one at which the metabolic cost of operating the springs is the lowest (Farley et al., 1993) since muscle metabolic energy is required in order to store and release elastic strain energy from the tendons (Alexander, 1986; Fletcher and MacIntosh, 2014). 49 2.5.4.2 Ground contact time Modeling running as a simple spring-mass system can characterize the mechanics of the body’s centre of mass quite well (Farley et al., 1993; McMahon and Cheng, 1990); however, it does not adequately explain the energetics of running, since theoretically a perfectly-elastic spring could supply all of the metabolic energy required to run (Arellano and Kram, 2014a). An alternative to the spring-mass model hypothesis, the ‘cost of generating force hypothesis’ was proposed (Taylor et al., 1980). By measuring the metabolic cost of carrying various loads, these authors observed the metabolic cost increased in direct proportion to the added load. Therefore it was proposed that the metabolic cost of running arose in association with the cost of generating force over time, rather than generating mechanical work. The metabolic cost is proportional to the average vertical force applied to the ground and inversely proportional to the ground contact time over which the force can be applied (Kram and Taylor, 1990). Higher running speeds are achieved with higher peak ground reaction force (Fz, (Cavanagh and Lafortune, 1980) but average Fz over a complete stride is equal to the subject’s body weight (Kram and Taylor, 1990). By determining a constant ‘cost coefficient’, these authors were able to determine that the mass-specific metabolic cost of running could be explained by how quickly Fz could be generated during the stance phase. Since faster running speeds are associated with shorter ground contact times, the required Fz needs to be generated more quickly as speed increases, elevating the metabolic cost. Roberts et al. (1998a) later showed that 70-90% of the speed-associated increase in metabolic rate could be explained by the increase in the rate of force generation. For any tendon stiffness the velocity of fascicle shortening will be proportional to the rate of force development. To generate a given force more 50 muscle fibres must be recruited to that produce force (Roberts et al., 1998a). To further support the cost of generating force hypothesis, several authors have shown an inverse relationship between Erun and ground contact time (Chapman et al., 2012; Di Michele and Merni, 2013; Williams and Cavanagh, 1987). Di Michele and Merni (2013) estimated that an increase in ground contact time of 1 ms was equivalent to a reduction in Erun of approximately 0.05 J•kg-1•m1 , since the force to support body weight has to be generated more rapidly. Together, these results suggest the speed-associated increase in Erun is a result of the elevated muscle energy cost associated with generating force more rapidly. 2.5.4.3 Footstrike Pattern The fastest marathon runners primarily use a forefoot strike pattern as opposed to heel strike (Ardigo et al., 1995; Cavanagh and Lafortune, 1980; Nilsson and Thorstensson, 1989). This seems counter-intuitive for minimizing Erun since ankle plantarflexor moments are larger during the first half of stance (Williams et al., 2000). Heelstrike reduces the Fz moment at the ankle because the centre of pressure resides under the heel of the foot during the first half of stance and this reduces the length of the Fz moment arm (Cavanagh and Lafortune, 1980; Williams and Cavanagh, 1987). Conversely, the centre of pressure during stance, a surrogate of the Fz moment arm length, is centered under the ball of the support foot in the forefoot landing pattern. Thus, heel strike pattern substantially reduced the EMG of the lateral gastrocnemius and soleus muscles compared to forefoot strike (Cunningham et al., 2010). However, despite heel striking to be theoretically-optimal to minimize Erun for the reasons listed above, when Erun was measured in the same subjects adopting either a heelstrike or forefoot strike pattern during running at various speeds, no difference in Erun (expressed as the O2 51 cost of transport) was seen between the two conditions (Cunningham et al., 2010). These results are contrary to those of Williams and Cavanagh (1987) who found the most economical runners were those with a heelstrike pattern. These authors suggested that a heelstrike pattern may reduce Erun because (contrary to a forefoot), the forefoot runners were not using the available cushioning in the heel of their running shoes, and thus the forefoot landing results in additional muscle activation in order to attenuate the impact associated with ground contact (Boyer and Nigg, 2004; Nigg and Wakeling, 2001). Alternatively, forefoot striking may result in a higher average gear ratio resulting in higher necessary TS force. The main issue with examining differences in Erun between forefoot and heel strike patterns is that many studies artificially impose an unnatural gait to the subject. Thus, a lower Erun measured under one condition may be the result of runners being unfamiliar with the novel gait pattern. We have described above how runners may self-optimize movement patterns (stride length, frequency, etc) to reduce Erun. It is also likely that runners self-select the footfall pattern that results in the lowest possible Erun. To demonstrate this theory, Gruber et al. (2013) measured Erun in habitual forefoot and heelstrike runners and found no difference in 𝑉𝑂! between groups when running with their habitual footstrike pattern. Interestingly, at all running speeds, runners habituated to the heel strike pattern showed a higher 𝑉𝑂! when asked to forefoot strike, which was not seen when the forefoot group ran with a heel strike pattern. Only at high speeds was the heel strike pattern less economical in the habitual forefoot runners. Taken together, these results suggest a heel strike pattern might confer an advantage in endurance running events, as a result of a lower Erun, in both habitual heel strike and forefoot runners. When the muscletendon unit of the triceps surae was modeled to assess the muscle mechanics and energetic differences between foot strike patterns, it was shown that the forefoot strike pattern resulted in a 52 near-isometric contraction during stance. This allows a lower muscle energy cost for a given force compared to the heel strike pattern, where high contraction velocities during stance were demonstrated (Gruber, 2012). A significant difference in the metabolic energy cost, however, could not be shown. 2.5.4.4 Flexibility Despite the general belief among runners and coaches that greater flexibility may result in improved Erun (Craib et al., 1996), there is very little evidence to support this notion. A lower flexibility (measured during a sit and reach test) is associated with a lower Erun (Craib et al., 1996; Gleim et al., 1990; Trehearn and Buresh, 2009). Various suggestions have been made by which a lower flexibility may decrease Erun: 1) reducing the trunk muscle energy cost to maintain stability (Craib et al., 1996) and/or 2) increasing the storage and return of elastic energy (Jones, 2002). The latter mechanism appears unlikely 1) given that such a small portion of the total metabolic energy (500-900 J) is stored and released as elastic energy (<90 J, Fletcher and MacIntosh, 2014; Ker et al., 1987) and 2) mechanically, a stiff AT stores less strain energy for a given force compared to a more compliant tendon. As we have previously suggested (section 2.5.3.1), an optimal tendon stiffness exists and therefore, a delicate balance between the amount of flexibility training (with the intention that stretching training will reduce AT stiffness (Kubo et al., 2002a; Morse et al., 2008)) and strength training (to increase tendon stiffness (Kubo et al., 2001a; Kubo et al., 2001b)) may result in less than optimal tendon mechanical properties in order to minimize muscle energy cost (Fletcher et al., 2013b).. 53 2.6 Conclusions and future directions: Muscle energetics and Erun Erun has been extensively studied in the biomechanics and exercise physiology literature and is known to be influenced by a variety of factors. However, much of the interpretation of Erun exists from the measurement of the steady-state 𝑉𝑂! at a given submaximal running speed, without calculation of the energy equivalent. It is difficult to conclude whether similar interpretations of Erun exist (eg. male vs female Erun, altitude vs sea-level) where Erun is expressed in terms of energy cost to run a fixed distance at a given relative intensity. It is only recently that this expression of Erun has been encouraged. For the first time, we have described the influence of many of the factors which influence Erun in terms of the biomechanical and physiological factors which dictate the muscle energy cost during the stance phase of running. This has allowed us to consider the relative importance of the storage and release of elastic energy from tendon in reducing the energy cost, which we argue is relatively minor compared to the muscle energy cost required to store the elastic strain energy. Consideration has been given to the influence of biomechanics (limb weight and length, AT and vertical ground reaction force etc) and physiology (force-length-velocity properties of muscle) in dictating the muscle energy cost, and therefore Erun. Future research in elite athletes should be aimed at the effectiveness of different training interventions (eg. strength, stretching or plyometric training) on Erun where it is expressed in terms of energy. Specifically, a greater understanding of the muscle and tendon interactions during running is warranted: during distance running, where does the muscle operate relative to their submaximal force-length-velocity relationships? How is this altered through training intervention (where muscle and tendon properties may be changed)? What is the impact of fatigue (mechanical or physiological) on the muscle energy cost, and on Erun? 54 Future directions should also include the measurement of factors which dictate muscle energy cost across different pathologies (aging, disease, disuse) in order to best prescribe appropriate training and/or rehabilitation programs for elite Paralympic athletes who may have compromised muscle and/or tendon function or for individuals where exercise tolerance may be limited by an elevated energy expenditure. 2.7 Author Contribution JRF drafted the manuscript and JRF and BRM revised it critically for important intellectual content. All authors approved the final draft of the article. 2.8 Acknowledgements The authors would like to thank Guillaume Millet for his thoughtful insight with regards to the structure of this review. JRF was supported by NSERC Canada. 55 2.9 Figures Figure 2.1. Erun and muscle energy cost in male and female runners. Whole-body energy cost per stride (solid bars) estimated across three relative running speeds in three groups: Elite males (EM), trained males (TM) and trained females (TF). Dashed bars indicate the estimated muscle energy cost (c) per stride for each group and speed, respectively. Adapted from Fletcher and MacIntosh (2014). Used with kind permission of the American Physiological Society. 56 Figure 2.2. Average estimated Achilles tendon force-elongation curves during running for elite male (EM), trained male (TM) and trained female (TF) distance runners. Solid and dashed lines represent the mean and sd of the second-order polynomial (Equation F = AdL2 + BdL 3-3) for all groups, respectively. Used with kind permission of the American Physiological Society. 57 Figure 2.3. The effect of greater shortening velocity on muscle activation to achieve a target force. The force-velocity relationship, scaled to activation (Chow and Darling, 1999). The short dashed and solid lines represent 50% and 85% of maximal motor unit activation, respectively. The long dashed line represents maximal activation. When force can be generated isometrically, target force can be achieved with minimal motor unit activation, as shown by the open square. When shortening is permitted, additional motor unit activation is required (filled square). Used with kind permission of Springer Science+Business Media. 58 Figure 2.4. Estimated Achilles tendon (AT) force from ground reaction forces (GRF) and the gear ratio, the ratio of the moment arms of the GRF and AT, respectively. 59 Figure 2.5. The metabolic cost and upper body kinematics of running with and without arm swing. Subjects were asked to run while: swinging their arms normally (NORMAL), holding the hands with the arms behind the back in a relaxed position (BACK), holding the arms across the chest (CHEST), and holding the hands on top of the head (HEAD). During the trials, oxygen uptake was collected in order to calculate Erun. The data demonstrate that running without arm-swing (compared with the control, * indicates P<0.05 and ** indicates P<0.01) increases net metabolic cost, indicating that arm-swing provides a small, but significant metabolic benefit during human running. Reprinted from Arellano and Kram (2014a). Used with kind permission of Oxford University Press. 60 Figure 2.6. Allometric-scaling of body mass on submaximal 𝐕𝐎𝟐 during running. Data are from Fletcher et al. (2013a), showing the relationship between the measured 𝐕𝐎𝟐 (LŸmin-1) at 95% sLT and body mass. Black lines show the linear regression (± 95% C.I.) of the relationship. The 95% C.I. for b in Equation 𝑉𝑂2= aBMb 2-1was 0.86 to 1.42. This value was not significantly different from 1 (p=0.553). 61 Figure 2.7. Force-length (A) and force-power-velocity relationship (B) of the frog semimembranosus muscle during maximal jumping. Measured data points are from Lutz and Rome (1994) and are shown as open and closed circles. A. During jumping, sarcomeres operate near the plateau region of the force-length relationship to maximize force production. B. The mean velocity of shortening during maximal jumping corresponds to a shortening velocity associated with maximal power production. Used with kind permission of the American Association for the Advancement of Science. 62 Figure 2.8. Determining running costs at self-selected (RCsel) and optimal stride frequency (RCopt). Data are from a representative subject from de Ruiter et al. (2013). The solid vertical arrow at the minimum of the curve indicates the stride frequency where running cost was minimal (SFopt). The dashed vertical arrow indicates the self-selected stride frequency (SFsel). This subject would reduce his running cost by about 4% if he chose to run at 91 instead of 82 stridesŸ min-1. Used with kind permission of Taylor & Francis. 63 Chapter Three: ENERGY COST OF RUNNING AND ACHILLES TENDON STIFFNESS IN MALE AND FEMALE TRAINED RUNNERS Jared R. Fletcher, Ted R. Pfister & Brian R. MacIntosh Human Performance Laboratory, Faculty of Kinesiology, University of Calgary Calgary, AB Canada Published under the terms of the Creative Commons Attribution Licence (CC BY), which allows users to copy, distribute and transmit an article and adapt the article and make commercial use of the article. The CC BY license permits commercial and non-commercial re-use of an open access article, as long as the author is properly attributed. Published: Fletcher, J.R., Pfister, T.R. and MacIntosh, B.R. 2013. Energy cost of running and Achilles tendon stiffness in male and female trained runners. Physiological Reports 1:e00178. 64 3.1 Abstract The energy cost of running (Erun), a key determinant of distance running performance, is influenced by several factors. Although it is important to express Erun as energy cost, no study has used this approach to compare similarly trained males and females. Furthermore, the relationship between Achilles tendon (AT) stiffness and Erun has not been compared between males and females. Therefore, our purpose was to determine if sex-specific differences in Erun and/or AT stiffness existed. Erun (kcal•kg-1•km-­‐1)  was determined by indirect calorimetry at 75%, 85% and 95% of the speed at lactate threshold (sLT) on 11 male (mean±SEM, 35±1 years, 177±1 cm, 78±1 kg, 𝑉𝑂2 max = 56±1 ml•kg-1•min-­‐1) and 18 female (33±1 years, 165±1 cm, 58±1 kg, 𝑉𝑂2 max = 50±0.3 ml•kg-1•min-­‐1)  runners. AT stiffness was measured using ultrasound with dynamometry. Male Erun was 1.01±0.06, 1.04±0.07 and 1.07±0.07 kcal•kg-1•km-1. Female Erun was 1.05±0.10, 1.07±0.09 and 1.09±0.10 kcal•kg-1•km-1. There was no significant sex effect for Erun or RER but both increased with speed (p<0.01) expressed relative to sLT. High-range AT stiffness was 191±5.1 N•mm-1 for males and 125±5.5 N•mm-1, for females (p<0.001). The relationship between low-range AT stiffness and Erun was significant at all measured speeds for females (r2=0.198, p<0.05), but not for the males. These results indicate that when Erun is measured at the same relative speed, there are no sex-specific differences in Erun or substrate use. Furthermore, differences in Erun cannot be explained solely by differences in AT stiffness. 65 3.2 Introduction Several studies have been concerned with the main physiological determinants of performance in distance running. These determinants include, among other variables: maximal oxygen uptake (𝑉𝑂2 max), fractional utilization of 𝑉𝑂2 max, the ability to withstand a disturbance in homeostasis, or tolerance, and the energy cost that is required to transport the body over a given distance (Di Prampero et al, 1993). In the case of running, this latter variable is typically defined as a subject’s energy cost of running (Erun). In a heterogeneous group of runners with a large range of 𝑉𝑂2 max values, a strong positive relationship exists between running performance and 𝑉𝑂2 max   (Costill et al, 1973). Among runners who possess similar 𝑉𝑂2 max values, Erun becomes a better predictor of running performance than 𝑉𝑂2 max alone (Pollock, 1977). Several studies have compared Erun between males and females and some argue that no difference exists (Ingham et al, 2008, Pate et al, 1992). In contrast, some contest that female runners are more economical than their male counterparts (Helgerud et al, 2010, Helgerud, 1994). Still others argue to the contrary; males are more economical (Daniels and Daniels, 1992). Further study of this controversy may allow a better understanding of the fundamentals that affect Erun. As we have previously outlined (Fletcher et al, 2009) appropriate measurement of Erun should be performed at similar %sLT between individuals, in order to ensure the steadystate of  𝑉𝑂2 is achieved and to minimize differences in substrate use (as reflected by the steadystate RER) between individuals, knowing that the energy equivalent of  𝑉𝑂2 changes with RER. It also makes sense to test at a speed that relates to a competition distance, like a 10 km race (ie. a similar %sLT). None of the previous studies comparing Erun of males and females have done this. Erun should also be expressed as an energy cost per unit distance rather than a  𝑉𝑂2 to allow 66 comparison between different absolute speeds. Thus, much of the conflicting evidence previously presented regarding Erun between males and females may result from an inappropriate expression of Erun itself. In one of the two studies to date that have compared Erun in male and female runners at similar relative intensities (relative to maximal oxygen uptake), Erun was expressed as ml•kg0.75 •m-1, without justification for doing so. It was reported that the lighter female runners were more economical than their heavier male counterparts (Helgerud et al, 2010). In the other study (Tarnopolsky et al, 1990) Erun was expressed as kcal/kg to run a given distance, and no significant difference between males and females was detected. Furthermore, no study has made direct comparison between similarly-trained male and female runners that have expressed Erun in terms of energy (kcal or J•kg-1•m-1). Could it be that sex-specific differences in Erun may be confounded as a result of differences in allometric scaling for body mass on metabolic rate? Since metabolic rate does not increase to the same extent as body mass (Bergh et al, 1991, Rogers et al, 1995), allometric-scaling for body mass has been used when comparing Erun for groups of varied body mass. The allometric scaling relationship is: 𝑽𝑶𝟐 = aBMb 3-1 where BM is body mass, a is a constant and b is the scaling exponent. Where the relationship between BM and  𝑉𝑂2 is linear, the value of b should be 1. Therefore, it needs to be confirmed whether the previous use of an allometric scaling factor of b=0.75 has influenced the comparison of Erun between sexes, or whether this is simply a function of the cohort tested and/or of the methods used in the determination of Erun. As we have proposed previously, Erun should be measured at similar intensities expressed relative to sLT for all runners. 67 It has become apparent in recent years that the muscle-tendon unit mechanical properties of the lower limbs may be important as determinants of Erun. Specifically, it has been shown that AT stiffness is generally less than optimal and greater stiffness corresponds with a lower energy cost of running (Arampatzis et al, 2006, Fletcher et al, 2010, Kubo et al, 2010, Lichtwark and Wilson, 2008). Recently, we have demonstrated that it is a combination of factors, including muscle shortening, shortening velocity and level of muscle activation which dictate the energy cost of muscle contraction in vivo (Fletcher et al, 2013) and this may help explain the relationship between AT stiffness and Erun. To date, virtually nothing is known about the muscle-tendon interactions in female runners. Female AT stiffness is generally thought to be lower than the AT stiffness in similarly trained males (Kubo et al, 2003). This may be a result of females having a lower isometric strength. The relationship between strength and AT stiffness is well-reported (Muraoka et al, 2005). Given the reported relationship between AT stiffness and Erun, it seems logical to hypothesize that Erun of females would be greater than that of the males. This hypothesis has not been tested directly to date. If Erun is not different between sexes, then the question exists: why are the ATs of female runners less stiff than their male counterparts? We hypothesize that this level of AT stiffness is required in the female runners, running at a slower speed than the males, in order to keep muscle shortening velocity low. Therefore, the primary purpose of this study was to investigate Erun and AT stiffness between similarly trained male and female runners and to determine if sex-specific differences in Erun and/or AT mechanical properties exist. In order to do so, and to compare directly with current literature, the use of allometric scaling for body mass was also considered. 68 3.3 Methods 3.3.1 Ethical Approval Male (11) and female (18) trained runners participated in this study (Table 3-1). The runners gave their informed written consent to participate in the experimental procedures, which were approved by the University of Calgary Conjoint Health Research Ethics Board. 3.3.2 Experimental Protocol All subjects participated in training for running a minimum of five times per week and none of the subjects had any neuromuscular or musculoskeletal injuries at the time of the study. All subjects were following a similar periodized training plan for either the 10 km or halfmarathon road race distance. Self-reported estimates of current 10 km race time (mean ± sd) were 39.67±4.51 minutes for the males and 47.17±6.07 minutes for the females. This difference in race time was significant (p<0.002). When compared to the current National records for the 10 km race distance, the mean male and female race times were 30.2±7.5% and 32.1±8.4% slower than National record times (http://athletics.ca/page.asp?id=66), respectively, illustrating a similar level of performance in the two groups (p=0.53) The subjects visited the lab on two separate occasions. On the first visit, an incremental exercise test to exhaustion was performed on a treadmill (Woodway Pro, Woodway USA, Waukesha, WA) to determine the subject’s maximal oxygen uptake (𝑉𝑂2 max) and speed associated with the lactate threshold (sLT). Prior to arriving at the lab, subjects were instructed to not consume any food or beverage, other than water, for a minimum of 12 hours prior to the testing. They were also asked to refrain from the ingestion of caffeine and avoid vigorous 69 physical activity for 24 hours prior to the testing. The subjects wore cool, loose clothing and their own lightweight running shoes. 𝑉𝑂2 max   and sLT were determined based on methods used previously in our lab (Fletcher et al. 2010; Fletcher et al. 2009). Following a self-selected warm-up of no more than 15 minutes of running, the subjects began running on a motorized treadmill with zero gradient at approximately 3 km•hr-1 slower than the subject’s self-reported 10 km race pace. Expired gases were collected by a metabolic cart (Parvomedics Truemax 2400, Salt Lake City, UT) for the determination of 𝑉𝑂2 (ml•kg-1•min-1) and carbon dioxide output (𝑉𝐶𝑂2 ,ml•kg-1•min-1). The metabolic cart was calibrated before and after each testing session, as described previously by Fletcher et al. (2009). The treadmill speed was increased by 0.48 km•hr-1 every 2 minutes. After each 2 minute stage, the subjects briefly straddled the belt and a fingertip blood sample was taken for the determination of blood lactate concentration ([BLa-], Lactate Pro). When [BLa-] rose more than 1 mM from the previous sample, the treadmill belt was returned to the previous speed and the gradient was increased 2% every minute until the subject was unwilling to continue. sLT was defined as the speed at the stage preceding that which elicited a [BLa-] increase of greater than 1 mM. All tests were terminated due to volitional exhaustion. 𝑉𝑂2 max  was defined as the highest 30 s average 𝑉𝑂2 during the test and was said to have been reached if there was an increase in 𝑉𝑂2 no greater than 2 ml·kg-1·min-1with an increase in treadmill gradient. 18 of the 29 subjects achieved 𝑉𝑂2 max based on this criterion. In the other 11 subjects, 𝑉𝑂2 max was said to have been reached if two of the following occurred: 1) RER greater than 1.15; 2) [BLa-] greater than 8 mM or 3) subjects reached their age-predicted 70 maximal heart rate (220 beats•min-1 - age). All of the remaining 11 subjects achieved 𝑉𝑂2 max  based on these criteria. Between 48-72 hours following the 𝑉𝑂2 max testing session, the subjects returned to the lab for determination of AT stiffness and Erun. The subjects followed the same pre-testing instructions as the first testing session. AT stiffness was determined on the right leg as described previously by Fletcher et al. (2010). The subjects laid prone with their knee at 180◦ and their ankle at 90◦. Before each MVC, the axis of rotation of the dynamometer (Biodex, Medical Systems Inc., Shirley, NY, USA) was carefully aligned with the axis of rotation of the ankle joint. The shank and unshod foot were affixed to the dynamometer using a series of Velcro straps. The subjects performed three isometric ramp MVC plantarflexions. Moment during the MVC was sampled at 100 Hz. The trial eliciting the highest moment was used for analysis. During each MVC, a 12.5 MHz linear array ultrasound probe (50mm, Philips Envisor, Philips Healthcare, Eindhoven, Netherlands) was used to visualize the medial gastrocnemius muscle (MG) fascicles, close to the AT. The ultrasound probe was placed on the MG muscle belly, near the myotendinous junction, and secured using a custom-built apparatus. Ultrasound scans were recorded at 49 Hz. A clear point where a fascicle inserts into the deep aponeurosis was followed throughout the MVC and its displacement was measured using ImageJ, (NIH, Baltimore, MD, USA). This displacement of a fascicle-aponeurosis junction was considered tendon elongation. An external function generator (B-K Precision 3010, Dynascan Corp., Chicago, IL, USA) was manually started at the beginning of the MVC and served as a timestamp between image and moment data collection. 71 3.3.3 Correction for joint rotation Despite affixing the ankle joint to the dynamometer tightly with Velcro straps, plantarflexion during the MVC is inevitable (Magnusson et al, 2001). This inevitable joint rotation would result in a lower resultant torque and would contribute erroneously to the apparent tendon elongation measured during the contraction (Muramatsu et al, 2001, Spoor et al, 1990). The resultant moment and apparent tendon elongation were corrected for this motion, as described previously (Fletcher et al, 2010). Ankle joint motion during the contraction was imaged at 30 Hz using a portable video camera (Canon GL1, Canon Inc., Tokyo, Japan). Joint angle change was determined by following two to four small dots drawn on the medial aspect of the unshod right foot. From this, ankle joint angle could be calculated throughout the contraction using ImageJ. We assumed the moment about the ankle resulted in a force perpendicular to the foot. Any change in angle of the foot relative to the Biodex lever will result in an underestimation of the ankle joint moment. To estimate this error, we measured the change in angle of the foot relative to the Biodex lever, and the corrected moments were calculated as: MC = MM x cos(θ)-1 3-2 where MC and MM are the corrected and measured moments, respectively, and θ, the angle of the foot during the MVC. The corrected moments were used for further calculation of plantarflexion force. The moment arm of the AT was estimated using the tendon travel method (An et al, 1984) under in vivo conditions (Ito et al, 2000, Maganaris, 2000). The displacement of a fascicle-aponeurosis cross-point (dL, mm) caused by passively rotating the ankle at 10º·s-1 from 5º of dorsiflexion to 5º of plantarflexion (dθ, rad) was measured. The AT moment arm was 72 calculated as the ratio dL/dθ (mm•rad-1). Triceps surae force was calculated by dividing the ankle joint moment by the estimated AT moment arm. AT stiffness was determined by fitting the Force (F) - elongation (dL) data to a quadratic regression equation using: F = AdL2 + BdL 3-3 Where A and B are constants. AT stiffness was calculated as the slope of the F-dL curve at three different force ranges: from 25-45% (low range stiffness), 30-70% (mid-range stiffness) and 50100% (high-range stiffness) of MVC force (Fletcher et al, 2010). These force ranges were chosen because the lower force ranges may be more similar to forces experienced by the AT during submaximal running (Giddings et al, 2000, Scott and Winter, 1990, Gruber, 2012) After a 10 minute warm up at 8 km•hr-1 for the females and 9.6 km•hr-1 for the males, the subjects ran at 75, 85 and 95% sLT for 5 minutes each, with a 5 minute standing rest period between speeds. [BLa-] was determined immediately prior to and following each speed. Erun was calculated as the O2 cost to cover a given distance (ml O2•kg-1•km-1) as well as the EC of running a given distance (kcal•kg-1•km-1), as described previously (Fletcher et al. 2009). The steady-state 𝑉𝑂2 , defined as the average 𝑉𝑂2 over the final 2 minutes of each stage, was used to calculate Erun. In all cases, the 𝑉𝑂2 over the final 2 minutes of each stage did not differ more than 2 ml•kg-1•min-1. 3.3.4 Statistics Values are presented as mean ± standard error of the mean (SEM), unless otherwise indicated. A two-way ANOVA (sex, speed) with repeated measures (speed) was used to test for differences in Erun, and for stiffness (repeated measures, sex by force range).When there was no 73 significant interaction and a significant main effect was found, Tukey’s post hoc test was used to detect significant differences between the three speeds. Pearson Product-moment correlation coefficients and simple linear regression were used to evaluate the relationship between BM and metabolic rate. Correlation and linear regression analysis were also used to evaluate the relationship between Erun and AT stiffness by sex. Sidak’s multiple comparison tests were used to correct for more than one linear regression analysis. All analyses were performed using GraphPad Prism version 6.01 for Windows (GraphPad Software, La Jolla, CA, USA, www.graphpad.com). Statistical significance was considered P<0.05. 3.4 Results Subject characteristics are listed in Table 3-1. Height and mass were significantly greater in the males compared to the females (p<0.05). The males also had a significantly greater sLT and relative 𝑉𝑂2 max. The intensity at which sLT occurred (expressed relative to 𝑉𝑂2 max) was not significantly different between sexes, indicating a similar level of training in these two groups. Two-way repeated measures ANOVA revealed no significant sex by speed interaction (p=0.48) and no differences in O2 cost (ml•kg-1•km-1) between sexes (Table 3-2); however O2 cost increased significantly with speed (p<0.0001, Figure 3.1). Similarly, there was no significant sex by speed interaction for RER and RER was not significantly different between sexes (p=0.59). RER increased significantly as a function of relative speed (p<0.0001). Consistent with these observations, for O2 cost and RER, there was no significant difference in Erun between sexes (Table 3-2). Erun also increased significantly with increasing relative speed (p<0.0001). 74 The use of allometric scaling to BM-0.75 did not reduce the inter-individual variability in either O2 cost or Erun. Furthermore, when our data were scaled to BM-0.75, there was still no sex by speed interaction in Erun (p=0.39) and no main effect for sex (p=0.30). When male and female data were combined, there was a significant positive relationship between 𝑉𝑂2 max and sLT (r2= 0.568, p<0.0001), suggesting that runners with the highest 𝑉𝑂2 max also possessed a high sLT. There was no relationship between 𝑉𝑂2 max and Erun at any of the measured speeds (p=0.468, 0.790, and 0.983 at 75, 85 and 95% sLT, respectively). Figure 3.2 shows the relationship between Erun and absolute speed during the Erun tests. Erun decreased significantly with increases in absolute speed at 75% sLT (r2= 0.197, p<0.02), suggesting the better runners (i.e. the runners with the highest sLT) had a lower EC of running. This relationship did not reach a significant level for the other two relative speeds (r2=0.128, p=0.056 at 85% sLT; r2=0.09, p=0.086 at 95% sLT), suggesting that runners with a high sLT were no more economical than the runners with a low sLT at high relative speeds. The Erunspeed relationship was not affected by substrate use, as no relationship between RER and absolute speed existed (r2<0.05, p>0.238 across all measured relative speeds). 3.4.1 Tendon Mechanical Properties Tendon mechanical properties for both males and females are shown in Table 3-3. AT stiffness of males was significantly greater than the AT stiffness of the females regardless of force range evaluated (p<0.001). There was also a significant, positive relationship between AT stiffness and body mass for all subjects (r2=0.295, p=0.002). 75 Figure 3.3 shows the relationship between AT stiffness at the highest force range and Erun in both males and females. There were no significant relationships between AT stiffness and the Erun at any force range in the males; however in the females, this relationship was significant at all force ranges at 75% sLT (corrected p<0.05), at the lowest force range at 85% sLT (p<0.05), and at the two lowest force ranges at 95% sLT (p<0.05). The relationships at the other speeds and force ranges approached statistical significance in the females (p=0.054 to p=0.084). When both male and female data were combined, and when AT stiffness was scaled to body mass, the relationship between AT stiffness and Erun was significant at all measured speeds (r2=0.1580.191, p<0.033). 3.5 Discussion The main findings of this study were three-fold. Firstly, Erun did not differ between sexes. This was true when Erun was expressed either as the energy cost (kcal•kg-1•km-1) or as an O2 cost (ml O2•kg-1•km-1). Secondly, expressing the energy cost relative to BM-0.75 did not change this conclusion. Lastly, relationships existed between AT stiffness and Erun in the female runners and between AT stiffness and body mass in all runners. We are aware of only one study to date, in which Erun of men and women were reported at similar relative intensities (Helgerud et al, 2010). These authors reported that females had a lower O2 cost than males. However, in that study, O2 cost was scaled to BM-0.75 .We observed no differences in O2 cost nor in Erun between the sexes when we scaled O2 cost to BM-.75. Another difference between the current study and Helgerud et al. (2010) is that our subjects ran on the treadmill at a level grade, while subjects in the Helgerud study ran on the treadmill with a grade of 1.5%. These latter authors report mean O2 costs of 670-685 and 753-755 ml•kg-0.75•km76 1 at speeds near the sLT for females and males, respectively. We have calculated the mean O2 costs of our runners to be approximately 593 and 621 ml•kg-0.75•km-1 at 95% sLT. It seems unlikely that the difference in oxygen cost between our study and that of Helgerud et al. (2010) can be accounted for by this methodological difference, but this methodological difference may contribute to the different results for the between sex comparison. It seems possible that the methodological difference (slope of the treadmill), coupled with the normalization to 0.75 of body mass are contributing factors for these discrepant results. For example, using the average mass of males and females from the study by Helgerud (2010) it can be calculated that O2 cost expressed per kg would have been 254 and 245 ml•kg-1•km-1 for males and females respectively. This represents a difference of 3.7%, whereas the reported values (752 and 686 ml•kg-0.75•km-1) respectively, differ by 9.6%. This larger difference may have allowed reaching statistical significance. The higher values associated with running on a slope of 1.5% coupled with the allometric scaling may be the main reasons for the finding of a significant difference. However, it is unclear whether other factors (apart from the increased work associate with greater BM of the males and expression of O2 cost relative to BM0.75) may have affected these reported differences. Although it has been found that running on a treadmill with 1% slope more accurately reflects the O2 cost of running over ground than running on a treadmill at zero slope (Jones and Doust, 1996), the fundamental factors dictating energy cost of running on a flat surface are different from those factors dictating energy cost of running up a slope. Running up a slope at increasing speed will increase the energy cost of running in proportion to body mass, whereas running over ground at increasing speed increases energy cost of running in proportion to frontal surface area and drag coefficient. The current study demonstrates, however, that when 77 running on a treadmill with zero gradient, the O2 cost of running does not differ between males and females of different body mass. We have previously shown that in a group of highly-trained runners, those runners with a higher sLT have a lower Erun (Fletcher et al, 2009). This phenomenon is also demonstrated here in lesser-trained runners and, for the first time in female runners ( Figure 3.2). Our results are consistent with the findings of Pollock (1977) who suggest that runners with the lowest Erun are associated with the fastest running performance. However, in our study, the relationship between Erun and sLT was not statistically significant at the highest speeds tested (p=0.056 and p=0.086). Erun is influenced by a variety of factors, and while it is generally accepted that better distance runners are more economical when O2 cost is measured at an absolute speed, this isn’t necessarily the case when Erun is presented at similar relative intensities. Differences in O2 cost at a given speed between individuals are likely a result of the runners being tested at different relative speeds, and it is clear from the current results that Erun increases with relative intensity. Thus, faster runners running at a given absolute speed are probably running at a lower relative intensity than the slow runners. It seems logical to compare runners at the speed they would be competing in a long distance run. Thus, in order to elucidate any differences in Erun between males and females, then Erun should be measured at the same relative intensity. It could be argued that the current sample size is not sufficiently large to detect a difference between sexes in either RER or Erun and this is in fact true. To detect a between-group difference in RER of 0.03 at a given %sLT, it was estimated that a sample size of >140 per group would be required. Also, given our current data, between 63 and 252 runners per group would be needed to detect a difference in Erun of the magnitude presented in Table 3-2 at the measured 78 speeds. The magnitude of difference would be in the order of 3% and if the current results prevailed, females would have the higher Erun. A secondary purpose of this study was to evaluate the relationship between AT stiffness and Erun in both male and female runners. It has been shown previously that a stiff AT is associated with a lower Erun (Arampatzis et al, 2006, Fletcher et al, 2010). Furthermore, changes in AT stiffness are associated with changes in Erun (Fletcher et al, 2010), supporting that this is likely a cause and effect relationship. Here, we show a similar stiffness-Erun relationship, but only in the females and not in the males, and only when AT stiffness was measured at the lowest force ranges. Further, no clear demarcation between the male and female data is visible in this relationship. The possibility exists that the small range of Erun values and low n in the males precludes any significant relationship between Erun and stiffness to be shown. However, understanding how and why changes in AT stiffness are associated with changes in Erun are difficult to elucidate. We speculate that AT stiffness is finely tuned in order to minimize the shortening of the muscle in series with it. This reduces the muscle energy cost (Fletcher et al, 2013). It has been previously shown that energy cost is related to the amount and/or velocity of muscle shortening (Askew and Marsh, 1998) as well as the level of muscle activation, which is necessarily higher to achieve a given force when velocity of shortening is greater (Fletcher et al, 2013). During the stance phase of running, the AT will stretch, and subsequent passive recoil of the AT will contribute to positive mechanical work of the muscle-tendon unit at the end of the stance phase (Biewener and Roberts, 2000) decreasing the need for work contributed by the fascicles, which can remain near isometric (Hof et al, 2002, Lichtwark et al, 2007). For the same load or force exerted by a muscle, a stiffer tendon reduces the amount of energy storage 79 and return, but minimizes the energy cost of the muscle contraction since it reduces the amount of muscle shortening required to effect joint rotation, thereby reducing the metabolic cost. Ultimately, optimal AT stiffness is the stiffness which allows the maximal contribution of positive mechanical work by the tendon while keeping the muscle fascicle shortening velocity low during muscle activation. This keeps active muscle volume to a minimum (Barclay et al, 2008). It should be kept in mind, however, that we have only examined the mechanical properties of the tendon of one muscle group (the triceps surae), which does not solely dictate the Erun. Furthermore, Erun is influenced by a variety of factors (Saunders et al, 2004), tendon mechanical properties being just one of these. 3.6 Conclusion In conclusion, the main finding of this study was that when energy cost of running is normalized to body mass, at similar relative speeds of running, that no sex-specific differences in substrate use nor Erun exist among similarly trained runners. Furthermore, the stiffness of the Achilles tendon of females is lower than in males, but the relationship between Erun and Achilles tendon stiffness is not different between the sexes. 3.7 Author Contribution All experiments were performed at the Human Performance Lab at the University of Calgary, Calgary, Alberta, Canada. JRF and BRM were responsible for conception and design of the experimental protocol. JRF and TRP collected and analyzed the data. JRF and BRM were primarily responsible for interpreting the experimental data. JRF drafted the manuscript and JRF 80 and BRM revised it critically for important intellectual content. All authors approved the final draft of the article. 3.8 Acknowledgements and disclosures The authors would like to thank the subjects for their time and effort in completing the experimental protocol. JRF was supported by NSERC Canada. TRP was supported by the Prize for Undergraduate Research Excellence (PURE), University of Calgary. None of the authors report any conflicting interests. 81 3.9 Tables Table 3-1. Subject Characteristics. Age Height N Sex (years) (m) Mass VO2max sLT VO2 at sLT (kg) (ml•kg-1•min-1) (m•min-1) (% VO2max Male 11 35.3±0.8 1.77±0.04* 77.6±0.7* 55.5±0.8* 234±3* 89±1 Female 18 32.8±0.9 1.65±0.07 57.9±0.6 49.8±0.6 202±2 88±1 82 Table 3-2. Running Economy and RER. Sex N O2 cost ml•kg-1•km-1 sLT 75% 85% 95% Male 11 200±11 204±13 209±11 Female 18 209±18 212±17 215±17 RER sLT 75% 85% 95% Male 11 0.91±0.03 0.93±0.03 0.97±0.03 Female 18 0.90±0.03 0.92±0.03 0.96±0.03 Energy Cost kcal•kg-1•km-1 sLT 75% 85% 95% Male 11 1.01±0.06 1.04±0.07 1.07±0.07 Female 18 1.05±0.10 1.07±0.09 1.09±0.10 Values are mean ± standard deviation 83 Table 3-3. Tendon mechanical properties for males and females. 84 3.10 Figures Figure 3.1. O2 cost at the three measured relative speeds in both males and females. Speed is expressed relative to the speed at lactate threshold (sLT). Vertical bars represent SD. 85 Figure 3.2 Erun as a function of absolute running speed. Erun was measured at 75% (A), 85% (B) and 95% (C) of sLT. 86 Figure 3.3. The relationship between Achilles tendon (AT) stiffness and Erun for males and females. Closed and open squares represent each male and female subject, respectively. Erun is represented as mean ± SD for all measured speeds. 87 Chapter Four: Tendon compliance, muscle shortening and muscle energetics Jared R. Fletcher1, Erik M. Groves1,2, Ted R. Pfister1 & Brian R. MacIntosh1 1 Human Performance Laboratory, Faculty of Kinesiology, University of Calgary Calgary, AB Canada 2 Canadian Sport Institute-Calgary, Calgary, AB Canada With kind permission of Springer Science+Business Media Published: Fletcher J.R., Groves E.M., Pfister T.R., MacIntosh B.R. (2013). Can muscle shortening alone, explain the energy cost of muscle contraction in vivo? Eur J Appl Physiol 113:2313-2322. 88 4.1 Abstract Decreased whole-body energy cost of running has been associated with an increased Achilles tendon stiffness. It is usually assumed that this lower energy cost can be attributed to less muscle fascicle shortening with a stiffer tendon. Increased fiber shortening is an important determinant of muscle energetics in vitro. However, other factors, like increased muscle activation may be important when considering whole muscle energetics in vivo. To determine the effects of a small additional muscle shortening on skeletal muscle energy requirement, 19 subjects performed 30 plantarflexions on two separate occasions: isometric (ISO) and isokinetic (KIN, 6.98 rad•s-1), each with a target of 50% of maximum isometric torque. Medial gastrocnemius muscle fascicle length (Lf) was measured by ultrasound and rate of oxyhaemoglobin (HbO2) desaturation was measured during blood flow occlusion using nearinfrared spectroscopy. KIN resulted in significantly greater muscle shortening (23.8±1.3 mm) than ISO (18.3±1.0 mm, p<0.001, mean ±SEM), and greater shortening velocity (KIN=2.5±0.3 FL•s-1, ISO=1.1±0.1 FL•s-1, p<0.001). Rate of HbO2 desaturation was 19±7 %, greater in KIN than ISO p<0.01), despite 19±2% lower mean torque (p<0.001) and 9.8±1.6 Nm•s lower mean impulse per contraction (p<0.001) in KIN compared to ISO. Root mean square for EMG was significantly greater (p<0.05) during KIN (73±3%) than during ISO (63±2%). These results illustrate that muscle energy requirement is greater when muscle fascicle shortening and/or velocity of shortening is increased, and suggest that greater activation contributes to that increased energy requirement. 89 4.2 Introduction The energy cost (EC) of exercise is primarily determined by the EC of muscle contraction yet little is known regarding the factors affecting the EC of generating muscular force and/or work in humans. The mechanical properties of the major force-generating muscles of the lower limbs have been well investigated, but interpretation of these muscle mechanics in terms of energetics relies on extrapolation from in vitro studies, often using amphibian muscle. It has been shown that a stiff Achilles tendon (AT) is associated with lower EC of running (Arampatzis et al, 2006, Fletcher et al, 2010). Furthermore, changes in AT stiffness have been shown to relate to changes in economy of running (Fletcher et al, 2010), confirming that this is likely a cause and effect relationship. Ultimately, optimal AT stiffness is that which allows the muscles to operate relatively isometrically during contraction, while the length change of the entire muscle-tendon unit can be accommodated by the tendon alone. In keeping the muscle fascicles isometric, the force-lengthvelocity relationship of muscle is maximized (Askew and Marsh, 1998). Considering that during running the triceps surae muscles do not undergo substantial stretch prior to shortening (Lichtwark et al, 2007, Ishikawa et al, 2007), an optimally-tuned AT would result in less fiber shortening to achieve active joint rotation. Furthermore, the elastic energy storage and release of the AT may also contribute to reducing the energy cost of running; however, this effect is small. Given the data reported by Fletcher et al. (2010), and assuming an AT elongation during running of 10 mm (Farris et al, 2011) and a hysteresis of 7%, the elastic energy contribution of the AT is estimated to be between 5.4 and 5.7% of the energy cost of each stride, assuming an energy cost of running between 4.40 and 4.64 kJ·kg-1·km-1 and a stride length of 150% of standing height. It has typically been assumed that the lower EC of running associated with a stiff Achilles tendon 90 is due to reduced shortening of the fibers of the triceps surae muscles (Roberts et al, 1998, Alexander, 1991, Arampatzis et al, 2006); however, further explanation of this assertion is not given. Does this simply relate to the idea that shortening increases the energy cost of contraction as has been shown in maximally activated muscle (Hill, 1938, Fenn, 1923) or is something more involved? It has been acknowledged for many years that the heat liberated (ie. the energy) above that required for a purely isometric contraction, in vitro is proportional to the work done (Fenn, 1923), which is to say any increase in muscle shortening with a given load would result in a higher EC of contraction. However, work accomplished during a contraction has a complicated relationship with total EC in whole muscle in situ (Stainsby, 1982). Since much of what we know regarding the EC of muscle contraction has been performed in vitro at non-physiological temperatures, we wanted to investigate the relationship between muscle shortening and muscle group EC in humans at physiological temperatures. It is important to consider that during running, we are dealing with a voluntary contraction, where the force is a consequence of the controlled motion of the leg. Comparing the same movement of the leg with a more compliant tendon (where additional shortening is permitted) should reveal additional potential factors that could affect the energetics of muscle contraction. A more compliant tendon will require not only greater muscle fiber shortening but also greater velocity of fiber shortening for a given load if the leg movement is not different. Greater velocity of shortening would most likely also require increased activation or recruitment of motor units in order to achieve the same force. This increased recruitment can be illustrated by consideration of the force-velocity relationship (see Figure 4.1). 91 The force-length relationship could also play an important role here. If the more compliant tendon resulted in shortening of fibers on the ascending limb of the force-length relationship, additional activation would be needed to reach the required force for limb movement. By minimizing the magnitude of fiber shortening, a stiff Achilles tendon allows the muscles to operate near isometrically, and to remain near optimal length. In running, where the EC is determined mainly by the cost of producing force to support body weight (Kram and Taylor, 1990, Taylor et al, 1980), operating at non-optimal muscle lengths requires a greater level of muscle activation to generate the required force, and thus would elevate the EC of running (Roberts et al, 1998). A reduction in muscle activation, if muscle can operate close to its optimal length, should contribute to minimize the EC of contraction (Hogan et al, 1998, Bergstrom and Hultman, 1988, Heglund et al, 1982). Near-infrared spectroscopy (NIRS) offers an affordable, portable solution to measuring muscle oxygen uptake. A number of thorough reviews have been dedicated to the use of NIRS during exercise (Ferrari et al, 2004, Hamaoka et al, 2007, Neary, 2004, McCully and Hamaoka, 2000). When blood flow is occluded to the exercising muscle, the relative rate of change in oxyhaemoglobin (HbO2) to deoxyhaemoglobin (HHbO2) signals is considered a reflection of the rate of muscle oxygen uptake (Ding et al, 2001, Im et al, 2001). Thus, NIRS appears to provide an effective tool in examining the link between the EC of muscle contraction and in vivo muscle shortening. Combined with ultrasound to simultaneously measure fascicle shortening and tendon mechanical properties, the effects of these properties on the EC of contraction can be investigated. Despite a vast array of research examining the EC of running and/or the EC of muscle contraction in a wide range of conditions and species (Taylor et al, 1970, Taylor and Heglund, 92 1982, Sih and Stuhmiller, 2003), no studies to date have directly determined the effects of additional shortening on the EC of muscle contraction of human skeletal muscle at physiological temperatures. Therefore, the purpose of this study was to investigate the possible differences in the EC of contractions performed in vivo at physiological temperatures with minimal shortening, and for which extra shortening was allowed. It was hypothesized that when extra fiber shortening was permitted, a greater level of muscle activation would be required to achieve the target force and a greater EC of contraction would result. It seems logical to believe that if the hypothesis is supported, that the increased EC associated with greater activation and increased shortening can explain why optimally-tuned AT stiffness is associated with a reduced EC of contraction 4.3 Methods 4.3.1 Subjects Characteristics of the 19 triathletes (9 males, 10 females) who participated in the study are shown in Table 4-1. These subjects were chosen because at the time of the study, all subjects were in the pre-competition phase of their run training for either the 10 km or half-marathon race distance. We also anticipated a wide range of AT stiffness and EC in this group. The subjects gave their informed written consent to the experimental procedures, which were approved by the University of Calgary Conjoint Health Research Ethics Board. None of the subjects had neuromuscular or musculoskeletal injuries at the time of the study. All subjects were familiar with the measurement of AT stiffness from previous experiments, but were further familiarized with each measurement prior to data collection. All tests were performed on the same day for each subject. 93 4.3.2 Tendon Mechanical Properties The experimental set-up is shown in Figure 4.2. AT stiffness was determined according to Fletcher et al. (2010) and is briefly described here. Each subject performed ramp maximal voluntary isometric ankle plantarflexion contractions (MVC) on their right side. The subjects laid prone with their knee at 180º and their ankle at 90º. Before each MVC, the axis of rotation of the dynamometer (Biodex Medical Systems Inc., Shirley, NY, USA) was carefully aligned with the axis of rotation of the ankle joint. The shank and unshod foot were affixed to the dynamometer using Velcro straps. To further familiarize the subjects with the protocol and to locate at least one visually distinctive and persistent fascicle-aponeurosis cross-point, a warm-up consisting of 3-5 min of submaximal isometric plantarflexions was performed. Afterwards, the subjects performed three isometric ramp MVC plantarflexions, where they were instructed to gradually and continuously increase the measured torque until their voluntarily-elicited maximum torque generation. The subjects then attempted to maintain this torque for 2-3 seconds, such that the entire ramp MVC took 5-7 seconds to complete. Torque during the MVC was sampled at 100 Hz. The trial eliciting the highest torque was used for analysis. During each MVC, a 12.5 MHz linear array ultrasound probe (50mm, Philips Envisor, Philips Healthcare, Eindhoven, Netherlands) was used to visualize the deep aponeurosis of the medial gastrocnemius (MG). The ultrasound probe was placed on the MG muscle belly and secured using a custom-built apparatus. Ultrasound scans were captured at 49 Hz. To determine if the probe moved during the contraction, a point on the ultrasound images where a muscle fascicle attaches to the deep aponeurosis was identified both before and after a test contraction for each subject. This point was always in the same position following the test contraction. An external function generator (B-K Precision 3010, Dynascan Corp., Chicago, IL, USA) was 94 manually started at the initiation of the contraction and acted as a time-stamp for synchronization between image, NIRS and moment data collection. Ultrasound images were recorded and a clear echo point where a fascicle inserts into the deep aponeurosis was followed throughout the contraction and its displacement was measured using publicly-available image analysis software (ImageJ, NIH, Baltimore MD, USA). This displacement of a fascicle-aponeurosis junction was interpreted as tendon elongation during these MVCs. 4.3.3 Correction for joint rotation The amount of joint rotation during the MVC was measured according to Fletcher et al. (2010). This inevitable joint rotation would result in a lower resultant torque and would contribute erroneously to the apparent tendon elongation measured during the contraction (Muramatsu et al, 2001, Spoor et al, 1990). The resultant moment and apparent tendon elongation were corrected according to Fletcher et al. (2010). Ankle joint motion during the contraction was imaged at 24 Hz using a portable video camera (Canon GL1, Canon Inc., Tokyo, Japan). Joint angle change was determined by drawing two to four small dots on the medial aspect of the unshod right foot. From this, ankle joint angle could be calculated throughout the contraction using ImageJ. We assumed the moment about the ankle resulted in a force perpendicular to the foot. Any change in angle of the foot relative to the biodex lever will result in an underestimation of the ankle joint moment. To estimate this error, we measured the change in angle of the foot relative to the biodex lever, and the corrected moments were calculated as: MC = MM x cos(θ)-1 4-1 95 where MC and MM are the corrected and measured moments, respectively, and θ is the angle of the foot at peak moment. The corrected moments were used for further calculation of plantarflexion force. The moment arm of the AT was estimated using the tendon travel method (An et al, 1984) under in vivo conditions (Ito et al, 2000, Maganaris, 2000). The displacement of a fascicle-aponeurosis cross-point (dL, mm) caused by rotating the ankle from 5º of dorsiflexion to 5º of plantarflexion (dθ, rad) was calculated from the passive rotation. The AT moment arm was calculated as the ratio dL/dθ (mm·rad-1). Triceps surae force was calculated by dividing the ankle joint moment by the estimated AT moment arm. The measured force-elongation data were fitted to a quadratic equation: Force = A(dL)2+B(dL) 4-2 AT stiffness was defined as the force-elongation slope from 50 -100 % of maximal isometric plantarflexion force, calculated from the quadratic force-elongation relationship Force = A(dL)2+B(dL) 4-2). 4.3.4 Measurement of EC of contraction To evaluate the effects of muscle fascicle shortening, during brief contractions with specific target force, on MG EC, the subjects laid prone on the dynamometer in the same position as for the testing of AT mechanical properties. The ankle was affixed to the dynamometer and the ultrasound probe placed on the MG and the subject’s MVC was determined as described previously. 96 4.3.4.1 Testing Protocol Following the MVC trials, the subjects performed 30 plantarflexions at a frequency of 1 Hz, attempting to reach 50% of maximum torque with each brief contraction under two conditions (see below). This load was chosen as it is similar to the force exerted on the Achilles tendon during running at 3 m•s-1 (Kyröläinen et al, 2003), which is equivalent to approximately 84% of the speed associated with the lactate threshold for our subjects. Contractions were performed on an isokinetic dynamometer under two conditions, in random order: isometric (ISO) and isokinetic (KIN). Plantarflexion angular velocity was set at 6.98 rad•s-1 during KIN. Throughout the contractions, torque, angular velocity and position angle signals were collected from the dynamometer at 100 Hz using data acquisition software (WinDaq Pro+, DataQ Instruments Inc., Akron, OH, USA). The subjects received feedback on the magnitude of contractile torque from a monitor displaying the torque signal as % MVC in front of them. The maximal joint rotation allowed during KIN was set prior to the contractions based on an estimated additional AT elongation (dL) of 15 mm. The estimated additional joint rotation (dθ, rad) required for this elongation during KIN was estimated from the previously determined calculation of Achilles tendon moment arm (MA, mm•rad-1): dθ= dL • MA-1 4-3 This magnitude of elongation was chosen because based on pilot testing, this magnitude of AT elongation represented an increase of 50% of the magnitude of the non-corrected AT elongation during an isometric MVC. Based on a maximum plantarflexion force of 3000 N, this increased magnitude of elongation was estimated to represent an apparent increase in AT compliance of 40%. This increase in AT compliance is consistent with a 2.7% increase in the 97 Erun (Fletcher et al, 2010) and represents an increase of approximately 2 kcal•km-1 for the runners in this study. During both experimental conditions, the rate of haemoglobin (HbO2) desaturation was measured during blood flow occlusion using spatially resolved near-infrared spectroscopy (NIRS, PortaMon, Artinis, Zetten, The Netherlands) collected at 10 Hz. Blood flow occlusion was achieved by rapidly inflating a blood pressure cuff placed around the subject’s thigh. Cuff pressure was maintained at 240 mmHg for the duration of the contractions. Blood flow occlusion was confirmed by examining the change of HbO2 saturation and desaturation signals throughout the contraction protocol. In all cases, a symmetrical change for HbO2 saturation and desaturation existed, suggesting no change in total Hb implying that no additional saturated blood had entered the area during the contractions (Ryan et al, 2012). HbO2 desaturation was assumed to be proportional to energy use, the rate of which was expressed as AU•s-1. The NIRS device was positioned medially relative to the position of the ultrasound probe. HbO2 desaturation was calculated as the first derivative of the HbO2 desaturation signal using Matlab (ver. R2010a, Mathworks, Natick, MA, USA). Fascicle lengths were measured using ImageJ at rest and throughout the 10th, 15th, 20th, 25th and 30th contractions. Wherever possible, the same fascicle was measured throughout the contractions. Where this was not possible, a visually distinctive fascicle near the vicinity of the originally-measured fascicle was used. In a small number of cases, a complete MG fascicle could not be seen on the ultrasound image. In those cases, fascicle length was measured using linear extrapolation (Finni et al, 2001) by measuring the distance between superficial and deep aponeuroses and dividing this distance by the sine of resting pennation angle (Austin et al, 2010). 98 Internal muscle work (J) was calculated as the integral: Work = Σ(F x dLf) 4-4 from rest to peak force generation, where dLf is the change in MG fascicle length, and F is the mean plantarflexion force over the time-course of dL. It was assumed that dL was the same for all triceps surae muscles during the contractions. This was considered a better assumption than estimating the MG muscle contribution to plantarflexor force based on the physiological crosssection of individual triceps surae muscles. Power (W) was calculated as muscle work divided by duration of positive work. The level of muscle activation was assessed using surface electromyography (EMG) throughout the contractions. Prior to the contractions, a 4 cm x 8 cm area on the skin over the muscle belly of the lateral gastrocnemius (LG) and soleus (SOL) muscles, as well as over the head of the fibula were shaved and cleaned with alcohol. Two EMG electrodes (Norotrode 20 bipolar Ag-AgCl electrodes, Myotronics Inc, Kent, WA, USA, inter-electrode distance: 22±1 mm), were affixed longitudinally to the shaved area over each muscle oriented along the direction of the muscle fibers, as confirmed for each muscle by ultrasound. A single electrode over the head of the fibula served as a ground. EMG of the MG was not possible due to space limitations on the muscle as a result of the ultrasound probe and NIRS device. EMG data were recorded at 2048 Hz using the NeXus-10 Biotrace+ (version 1.16) Wireless Biofeedback System (Mindmedia, Roermond-Herten, The Netherlands). To reduce noise and signal artifact, the signal was filtered through a 5th order Butterworth filter (high and low-pass filter of 20 and 500 Hz, respectively). EMG amplitude was calculated as the root mean square (RMS) of the raw EMG signal. This RMS was interpreted as the level of muscle activation; an accumulation of recruitment and rate coding. EMG RMS of the LG and SOL were evaluated on the same 99 contractions as the fascicle measurements for each experimental condition. In a subset of subjects on a separate day (n=4, 26±3 years 1.65±0.04 m, 61.3 ± 12 kg), the EMG amplitudes of the MG and LG were evaluated, without the use of NIRS or ultrasound, in order to determine whether the EMG amplitude of LG provided an appropriate estimate of MG activation during the experimental trials where the NIRS and ultrasound probe were placed on the MG. EMG amplitude was measured and calculated as described above, during a ramp MVC, and during similar ISO and KIN trials. To confirm whether EMG amplitude increased equally as a function of load (expressed as %MVC), the EMG amplitude was also collected while the subjects attempted to maintain a constant isometric torque at 30, 40 and 50% of MVC. 4.3.5 Statistics Data are presented as mean ± standard error of the mean (SEM) and were analyzed using SPSS analysis software (SPSS Inc. v15.0, Chicago, IL, USA). A two-way repeated measures ANOVA was performed to examine the condition x contraction number for the following: torque, impulse, MG shortening length, MG shortening velocity and for EMG RMS. No significant interactions were found, so statistical comparison of these variables refers to main effects. Paired t-tests were used to test for differences between conditions for HbO2 desaturation, Pearson product-moment correlations were used to identify relationships between HbO2 desaturation. The a priori level of statistical significance was set at alpha <0.05. 4.4 Results AT stiffness was 151 ± 66 N·mm-1. dLROM was 15.5 ± 2.2 mm. The calculated ankle range of motion during KIN was 23.4 ± 3.1 deg. Average torque during ISO was 56.2±5.1 Nm 100 (52.6 ± 2.1 % MVC). Average torque during KIN was 34.5 ± 2.2 Nm (33.4 ± 1.4 % MVC), significantly lower than during ISO (p<0.001) and substantially less than the target. Mean impulse was also significantly greater for ISO (19.6 ± 1.9 Nm•s) compared to KIN (9.8 ± 0.8 Nm•s, p<0.001). Mean MG fascicle length (Lf) measured prior to the contractions was 55 ± 2 mm. Lf at peak torque during the MVC trial was 32± 2 mm. Mean Lf at peak torque was significantly greater (p<0.01) for ISO (38 ± 1 mm) compared to KIN (32 ± 2 mm). The ISO contractions were of significantly longer duration (0.33 ± 0.03 s) compared to KIN (0.19 ± 0.01 s). Mean shortening velocity for ISO was 1.13 ± 0.13 Lf•s-1 and for KIN was 2.48 ± 0.31 Lf•s-1. This difference was significant (p<0.001). Combining the results of fascicle shortening and force to estimate internal muscle work between conditions, ISO resulted in significantly more work compared to KIN (ISO = 32.2 ± 5.3 JŸcontraction-1, KIN = 19.9 ± 1.8 JŸcontraction-1, p<0.05); however, the rate of performing that work (ie. power) was not different (p>0.05) between conditions (ISO = 98 ± 13 WŸcontraction-1, KIN = 117 ± 14 WŸcontraction-1). Despite a lower mean torque and impulse in KIN, the mean rate of HbO2 desaturation was significantly greater in KIN (p<0.01). KIN resulted in a 18.6 ± 6.5 % greater HbO2 desaturation (p<0.01) and required a shorter period of time to reach the maximum rate of HbO2 desaturation (Figure 4.3). The contraction number at which maximum rate of HbO2 desaturation occurred was, on average, the 12th contraction (range: 6th to18th contraction) for ISO and the 9th contraction (range: 6th to 13th) for KIN. Results of the paired t-test revealed this contraction number to be significantly fewer for KIN than ISO (p=0.03). Taken together, these results 101 suggest a greater rate of energy use and thus a greater EC of contraction in KIN. Results for rate of HbO2 desaturation and mean torque are shown in Figure 4.4. Combining these results, the energy required to maintain a given torque (HbO2 desaturation•impulse-1) was greater in KIN (6.2 ± 0.6 AU•Nm-1•s-1) compared to ISO (2.4 ± 0.3 AU•Nm-1•s-1). This represents a difference in EC approaching 160%. HbO2 desaturation was also significantly related to the average amount of shortening during ISO and KIN conditions (Figure 4.5) and the average velocity of shortening (Figure 4.6). In a separate series of measurements, EMG of MG and LG was measured to determine if LG EMG changed in a similar way as EMG of MG. On average, the EMG amplitude of MG was 2-fold larger than that of LG and the relationship between EMG amplitudes of the MG and LG during the submaximal steady-state and maximal contractions was significant (r2=0.752, p<0.0001). Furthermore, the change in EMG amplitude from 30-100% MVC (as evaluated from the slopes of the EMG amplitude-%MVC relationships) was not different between MG and LG (p=0.478). This confirms that changes in EMG amplitude in LG during the trials could be interpreted to represent changes in EMG amplitude of MG. Mean RMS amplitude during the MVC for LG and SOL was 0.570 V and 0.907 V, respectively. Figure 4.7 shows EMG RMS data, presented relative to the EMG RMS amplitude measured during the isometric MVC. Twoway repeated-measures ANOVA revealed no significant effect of contraction number on EMG RMS amplitude; however there was a significant main effect of experimental condition on EMG amplitude. EMG amplitude in KIN was significantly higher than ISO (p<0.05). 102 4.5 Discussion The purpose of this study was to investigate the effects of additional MG fascicle shortening on the EC of muscle contraction. The main finding in this study was that when greater MG fascicle shortening was imposed, the rate of muscle oxygen uptake increased. We assume this measured oxygen uptake is proportional to the total EC of the muscle contractions; that is, any anaerobic energy utilization would increase in proportion with the increases in oxygen uptake. The additional shortening and EC during KIN may help to explain the reported benefit of a stiff Achilles tendon in reducing the whole-body EC of running (Arampatzis et al, 2006, Albracht and Arampatzis, 2006, Fletcher et al, 2010). Given the current data, we propose that the explanation for the increased EC in vivo at physiological temperatures is more complex than simply explaining EC on the basis of extra shortening. Early reports by Fenn (1923, 1924) would suggest the EC of maximally-activated muscle is proportional to the amount of work done; however, in this situation the load was constant and work was proportional to shortening. Given that the EC of achieving a target force is greater than that of maintaining it (Russ et al, 2002, Foley and Meyer, 2005), we speculated that it was the additional muscle shortening in KIN which contributed to, but is not the sole factor in the elevated EC. Here, we now demonstrate that it is not the amount of work performed per se which dictates the EC of muscle contraction, since the EC during KIN was significantly higher than during ISO, despite more work performed in the latter condition. Rather, the EC of voluntary muscle contraction performed in vivo is determined by a combination of muscle shortening, shortening velocity and level of motor unit recruitment. The amount and velocity of shortening are dictated by the mechanics of joint movement and the mechanical properties of the tendon. However, the muscle’s in vivo force-length and 103 force-velocity relationships dictate the magnitude of activation required to achieve a given shortening (Praagman et al, 2006). The force-velocity relationship dictates that force production for a given level of activation is maximal when that force can be developed isometrically (Biewener, 1998, Gabaldon et al, 2008, Roberts et al, 1997) and decreases as shortening velocity increases. It has been suggested that the EC of contraction in vivo should be related not only to the amount of fiber shortening and the shortening velocity but also the level of motor unit activation (Stainsby and Lambert. 1979). In fact, Stainsby and Lambert (1979) suggest that the major determinant of metabolic cost of contraction in voluntary movement should be motor unit recruitment. This notion is consistent with the observed (RMS) EMG during cycling, which has a minimum at a unique cadence associated with a given power (MacIntosh et al, 2000), and this cadence is closely related to the optimal cadence for best efficiency (Coast and Welch, 1985). Load, shortening and velocity of shortening have less impact on the magnitude of energy requirement (Stainsby and Lambert, 1979). For submaximal contractions like those imposed during the present study, the level of activation (as measured by EMG) needed to generate a given (target) force can be minimized when the fascicles are allowed to develop force isometrically. This is illustrated in Figure 4.1. In our data (Figure 4.7), 50 % of MVC was achieved in ISO with just 50% of maximal (RMS) EMG but for KIN, the required level of activation increased to above 80%. It is presumed that the EC of contraction during KIN was greater compared to ISO as a result of the increased rate and amount of MG activation required to achieve the target force in the face of increased fascicle shortening and shortening velocity during the KIN contractions. 104 The additional fascicle shortening during KIN also impacts force because the muscle is operating at a different place on its force-length relationship (Gordon et al, 1966, Maganaris, 2003). For a given muscle force required to perform the task (eg. running a particular speed and supporting body weight), the level of activation can be minimized if the muscle is operating near optimal length. In keeping the level of activation to a minimum, active muscle volume to generate the required force is minimized and so is the considerable cost of muscle activation associated with ion pumping (Heglund et al. 1982; Hogan et al. 1998). The EC of running is determined primarily by the force of supporting the athlete’s body weight and the time course of generating this force (Kram and Taylor, 1990). When the speed of running is increased, the EC is elevated because the required force is developed more rapidly (Roberts et al. 1998). It has previously been shown that in maximally activated voluntary isometric contraction, the MG muscle is on the ascending limb of the force-length relationship at anatomical ankle joint angles (Maganaris, 2003), with the highest force at +20° dorsiflexion. Assuming this joint configuration corresponds with optimal sarcomere length, sarcomere length during a MVC at a neutral ankle angle of 90° can be estimated as approximately 83% of optimal gastrocnemius sarcomere length during MVC (Maganaris, 2003). The current study measured fascicle length for maximal (31.7 mm) and submaximal (37.8 mm) contractions at an ankle angle of 90°. Assuming an optimal sarcomere length during maximal activation of 2.6 µm at the short side of the plateau (Herzog and ter Keurs, 1988), then it is estimated that the sarcomere lengths were 2.57 µm for ISO and 2.19 µm for KIN. The EC of the force impulse is increased at short muscle length (de Haan et al, 1986). This increase occurs because the energy for activation (ion pumping) is independent of length (Homsher et al, 1972, Woledge et al, 1985), and energy for 105 force development is proportional to force. However, the small differences we observed in estimated sarcomere length would indicate that this effect was minor. Despite greater muscle work performed during the ISO contractions, KIN resulted in a significantly greater EC of contraction compared to ISO. However, the EC of contraction was directly related to the amount and rate of MG fascicle shortening. The rate of energy use has been shown previously to relate to the rate of muscle shortening (Fenn, 1924, Hill, 1938, He et al, 2000). However, it is clear from the above data that this is not the only factor which dictates the EC of in vivo voluntary muscle contraction. In spite of greater muscle work in ISO, the EC was significantly reduced in this condition compared to KIN. Despite greater force per contraction in ISO, muscle shortening under this condition was less than in KIN. Heglund et al. (1982) demonstrated that the energetic cost of locomotion is related to the rate at which muscles are turned on and off, such that a faster rate of activation is associated with an elevated metabolic cost. These results suggest that short-duration contractions (such as those seen in KIN) may require a higher amount of total energy as a result of ion pumping associated with each activation cycle. The present results support this notion, since more EMG was observed, indicating more motor unit activation. This probably relates to differences in sarcomere length and the impact of the force-velocity relationship. The contractions by the MG fascicles during running are nearly isometric (Ishikawa et al, 2007) thus, the results of the current study may be relevant to running, and may explain why a stiffer tendon helps to minimize the EC of whole-body locomotion. As indicated by several authors, a more compliant Achilles tendon would require a greater amount of fascicle shortening (Fletcher et al, 2010, Arampatzis et al, 2006, Albracht and Arampatzis, 2006). However, it should be acknowledged that additional related factors contribute to the increased energy cost. 106 This includes increased velocity of shortening for a given joint movement and increased activation of the involved muscles. We observed that with additional shortening and a similar target force, there was increased velocity of shortening and a greater level of motor unit recruitment. This increased recruitment would contribute to the elevated EC of contraction. 4.6 Conclusions In conclusion, the results of the current investigation confirm previous reports that the EC of muscle contraction is related to the amount and rate of muscle shortening. Furthermore, these results may explain why the EC of running is elevated when Achilles tendon compliance is increased, since a greater amount and rate of shortening are required for force transmission under these conditions. According to the in vivo force-length and force-velocity relationships of skeletal muscle, this shortening and velocity will impact the EC, not simply because of the greater shortening, but because increased muscle activation is required to permit similar force development when shortening velocity is greater. 4.7 Author Contributions All experiments were performed at the Human Performance Lab at the University of Calgary, Calgary, Alberta, Canada. JRF and BRM were responsible for conception and design of the experimental protocol. JRF and EMG collected and analyzed the NIRS data, while JRF and TRP collected and analyzed the ultrasound data. JRF and BRM were primarily responsible for interpreting the experimental data. JRF drafted the manuscript and JRF and BRM revised it critically for important intellectual content. All authors approved the final draft of the article. 107 4.8 Ethical standards The authors declare that the experiments comply with current Canadian laws and all experimental procedures were approved by the University of Calgary Conjoint Health Research Ethics Board. 4.9 Conflict of Interest statement The authors report no commercial involvement which may bias the process of data collection, reporting and/or interpretation. 4.10 Acknowledgements This study was supported by the Natural Sciences and Engineering Research Council of Canada and the Prize for Undergraduate Research Excellence (PURE) Award. 108 4.11 Tables Table 4-1. Subject characteristics. Table 1. Subject Characteristics Sex N Male Female 9 10 Age Height Mass VO2max (years) 36.8 ± 2.9 33.6 ± 3.2 (m) 1.76 ± 0.02* 1.67 ± 0.03 (kg) 76.5 ± 2.6* 58.4 ± 1.8 (ml•kg-1•min-1) 53.6 ± 2.7 47.7 ± 2.1 Values are mean ±  SEM * significantly different between males and females (p<0.05) 1 109 4.12 Figures Figure 4.1. The effect of greater shortening velocity on muscle activation to achieve a target force. The force-velocity relationship, scaled to activation (Chow and Darling, 1999). The short dashed and solid lines represent 50% and 85% of maximal motor unit activation, respectively. The long dashed line represents maximal activation. When force can be generated isometrically, target force can be achieved with minimal motor unit activation, as shown by open square. When shortening is permitted, additional motor unit activation is required (filled square). 110 Figure 4.2. Experimental set-up. 111 Figure 4.3. Representative tracing for one subject of HbO2 desaturation as measured by NIRS. Dashed and solid lines represent ISO and KIN conditions, respectively. The rate of HbO2 desaturation was significantly faster in KIN. 112 Figure 4.4. Mean maximum rate of muscle oxygen uptake (maximum HbO2) compared to mean torque during the experimental conditions. Values are mean ± SEM. Despite a lower mean torque in KIN, energy use was significantly greater than ISO. 113 Figure 4.5. The relationship between the rate of energy use to maintain a given torque (HbO2•impulse-1, AU•Nm•s-1) and magnitude of MG muscle fascicle shortening (cm). The open diamonds represent measurements made during the ISO condition. The filled squares represent those measurements made during the KIN condition. When combined together, the relationship was significant (r2=0.21, p=0.004). Because of similar values between subjects, some data points are over-lapped. 114 Figure 4.6. The relationship between the rate of energy use to maintain a given torque (HbO2•impulse-1, AU•Nm-1•s-1) and shortening velocity (Lf•s-1). The open diamonds represent measurements made during the ISO condition. The filled squares represent those measurements made during the KIN condition. When combined together, the relationship was significant (r2=0.38, p<0.001). Because of similar values between subjects, some data points are over-lapped. 115 Figure 4.7. EMG amplitude for LG (top) and SOL (bottom) for both experimental conditions expressed relative to EMG amplitude measured during the isometric MVC (100% MVC). One-way repeated measures ANOVA revealed a significant effect of experimental condition on EMG amplitude, with KIN resulting in a greater EMG amplitude compared to ISO. 116 Chapter Five: ESTIMATES OF ACHILLES TENDON MOMENT ARM LENGTH AT DIFFERENT ANKLE JOINT ANGLES: EFFECT OF PASSIVE MOMENT Jared R. Fletcher1 and Brian R. MacIntosh1 1 Human Performance Laboratory, Faculty of Kinesiology, University of Calgary Calgary, AB Canada 117 5.1 Abstract Estimating active muscle forces in vivo is typically done via measurement of joint moment and dividing by the muscle’s moment arm (MA). The length of the MA can be estimated noninvasively using ultrasound, and the tendon excursion (TE) method. The main assumption with the TE method, however, is that the force acting on the tendon during passive rotation is constant. However, passive force changes through the range of motion, and MA length is underestimated. Therefore, we attempted to account for passive force on the measurement of Achilles tendon (AT) MA length using the TE method in 12 male and female runners. Tendon excursion (dL) was measured using ultrasound while the ankle was passively rotated at 0.17 rad•s-1. Using the TE method, MA length was calculated at 5° intervals as the ratio of dL to joint rotation (dϴ, radians) from 70° to 115°. At the same time, passive moment (MP) was measured by a dynamometer. The dL attributable to MP was calculated by monitoring dL during a ramp isometric maximum contraction. MP was 7.6±2.7 Nm at 70° and decreased exponentially from 70°-90°. dL attributable to MP was 4.8 ± 3.0 mm at 70°. This resulted in MP-corrected MA lengths that were significantly larger than uncorrected MA lengths. The coefficients of variation for uncorrected and corrected MA lengths were 11.0 ± 3.8% and 8.4 ± 3.3%, respectively. Correcting for MP yielded more reliable measures of MA length compared to the traditional TE method, where passive forces at the ankle are not considered. 118 5.2 Introduction Since direct measurement of muscle forces in vivo is highly invasive (Fukashiro et al, 1993), muscle forces are typically estimated from measurement of joint moments (An et al, 1984, Fukunaga et al, 2001). Estimating muscle forces from joint moments requires knowledge of the muscle/tendon moment arm (MA) length, the perpendicular distance from the joint centre of rotation to the line of action of the muscle (Pandy, 1999). Knowledge of any change in MA with respect to joint angle (Maganaris, 2000, Rugg et al, 1990, Visser et al, 1990) allows a more precise estimate of muscle force and has significance in muscle modeling, but often, a constant MA length is used to estimate muscle forces since determining MA is difficult to measure in cadavers and hard to determine with accuracy in vivo (Buchanan et al, 2005). Therefore, knowing the angle-specific muscle MA is of importance. Two non-invasive methods to estimate muscle or tendon MA length have become popular in recent years. Magnetic resonance imaging (MRI) allows a high quality image of the joint, from which an apparent centre of rotation (COR) can be estimated, and the perpendicular distance from the estimated centre of rotation to the line of action of the muscle can be measured (Rugg et al, 1990). The Achilles tendon MA can also be measured in three-dimensions across ankle angles using MR-imaging (Sheehan, 2012). However, due to limited accessibility and considerable time and costs associated with its use, MRI may not be a viable option in many cases. Consequently, a series of hybrid measurements based on the COR method have been developed, including measuring the perpendicular distance of the apparent COR to AT midpoint using ultrasonography and motion analysis (Manal et al, 2010) or simply measuring this perpendicular distance directly (Scholz et al, 2008). Further, the actual COR of the joint is still 119 estimated when using this method. Consequently, no true gold standard in the measurement of ankle joint MA length exists. The tendon excursion (TE) method uses ultrasonography to calculate MA length by measuring the displacement of the tendon (dL) with respect to the angular rotation of the joint (dθ) (Ito et al, 2000, Maganaris, 2000). This approach does not require knowledge of the joint’s centre of rotation, approximation of which may introduce error into the estimation of MA (Klein et al, 1996). The TE method is based on the principle of virtual work (An et al, 1984), which assumes that a constant force is applied to the tendon. A constant force will allow a constant tendon length, so measured tendon excursion represents a true displacement. In vivo, it is assumed that when no external moment acts on the joint, such as during a passive rotation of the ankle joint, the tendon MA is calculated as the ratio of dL to dθ. Given these conditions, the TE method may offer a better estimate of MA length compared to the COR method since estimates of the joint centre of rotation is not required. Given that tendons are compliant, and are stretched when a force is applied, the principle of virtual work is violated when passive force changes as occurs at dorsiflexion angles (Fath et al, 2010). These passive forces and the corresponding elongation of the tendon could be accounted for to allow an accurate, and potentially more valid MA length determination using the TE method. This technique is likely to be more time and cost-effective compared to the COR method. Therefore, the purpose of the current investigation was to compare the AT MA length using the TE method to a novel TE method, accounting for passive forces on the AT. We hypothesize that the calculated MA would be considerably larger than when passive forces are not accounted for, over the range of motion where passive force is exerted. This occurs because 120 any change in passive force during the measurement would prevent detection of tendon translation, making the MA appear smaller. Furthermore, a secondary objective of this study was to quantify the test-retest reliability of the TE method, using the corrected measurement of dL. 5.3 Methods All subjects (4 males and 8 females) were distance runners, training at least five times per week. Mean (± sd) age, height and weight for all subjects were 33.8 ± 10.2 years, 168.6 ± 11.3 cm, 63.1 ± 15.6 kg, respectively. We chose distance runners as subjects as they had previously participated in studies involving the measurement of AT moment arm using the TE method (Fletcher et al, 2013). While we recognize that the AT mechanical properties of this cohort were likely different than the general population, it has been previously shown that the passive stiffness of the ankle joint is independent of the AT mechanical properties (Kubo et al, 2001). Thus, we felt our results are generalizable to lesser-trained populations. All subjects gave their informed written consent to participate in the experimental procedures, which were approved by a University of Calgary Research Ethics Board. None of the subjects had neuromuscular or musculoskeletal injuries. The estimate of each subject’s Achilles tendon (AT) MA length was performed using a dynamometer (Biodex Medical Systems Inc., Shirley, NY, USA) while the subjects laid prone with their knee fully extended. The shank and unshod foot were affixed to the dynamometer using Velcro straps, with the ankle at 90◦. Ankle angle was defined as the angle of the foot relative to the long axis of the tibia. The axis of rotation of the dynamometer was carefully aligned with the assumed axis of rotation of the ankle 121 joint; the dynamometer axis aligning with the midpoint of the axis aligning the medial and lateral malleoli. The MA of the AT was estimated using the TE method (Ito et al, 2000, Maganaris, 2000). While the subject’s ankle joint was passively rotated at 0.17 rad·s-1 from maximum ankle plantarflexion to maximum ankle dorsiflexion, a 12.5 MHz linear array ultrasound probe (50mm, Philips Envisor, Philips Healthcare, Eindhoven, Netherlands) was used to visualize the medial gastrocnemius muscle (MG) fascicles close to the myotendinous junction. Ultrasound scans were recorded at 49 Hz. A clear fascicle-aponeurosis intersection near the myotendinous junction was followed throughout the passive trials and its displacement was measured using ImageJ, (NIH, Baltimore, MD, USA). A fascicle-aponeurosis intersection was chosen in favor of the myotendinous junction since it offered better contrast resolution on the ultrasound images (and thus a more spatially distinct point to manually track, compared to the relatively large, hyperechoic myotendinous junction). The displacement of a fascicle-aponeurosis junction was assumed to represent tendon translation (dL). In order to familiarize the subject with the procedure and to minimize the effect of time-dependent deformation, including conditioning effects (Hoang et al, 2007), the ankle was moved through the range of motion at least four times prior to beginning data collection. To monitor muscle activation during the passive trial, two pairs of surface electromyography (sEMG) electrodes (Norotrode 20 bipolar Ag-AgCl electrodes, Myotronics Inc, Kent, WA, USA, inter-electrode distance: 22±1 mm), were affixed over the soleus and lateral gastrocnemius muscles, along the direction of the muscle fibers, as confirmed by a longitudinal ultrasound scan. An electrode over the head of the fibula served as ground. EMG of the MG was not possible due to the space occupied by the ultrasound probe. EMG data were 122 recorded at 2048 Hz using the NeXus-10 Biotrace+ (version 1.16) Wireless Biofeedback System (Mindmedia, Roermond-Herten, The Netherlands). Muscle activation (or lack thereof) was considered by evaluating the raw EMG signal throughout the passive trial. It was intended that if at any point during the trial the EMG amplitude rose 3 SDs above resting EMG amplitude, the passive trial would be repeated until EMG amplitude remained at baseline. Based on this criterion, no trials were rejected. For each subject, a series of MA measurements using the TE method were made throughout the ankle range of motion, from maximum plantarflexion to maximum dorsiflexion. All dL measurements were done in this direction to avoid variability due to hysteresis effects. MA lengths were calculated over several 7-8◦ intervals (dθ), from maximum dorsiflexion to maximum plantarflexion, in order to allow consideration of AT MA length estimates where passive forces could be considered. In all cases, the number of total MA estimates per subject was between 6 and 8. Passive moment and joint angular displacement data were sampled at 100 Hz. The dL/dθ data for each subject were fitted to a 3rd -order polynomial equation across the measured range of motion: dL = aθ3 + bθ2 + cθ + d 5-1 where θ is joint angle and a, b, c and d are constants. A typical example for one subject is shown in Figure 5.1. 5.3.1 Correction for passive force The moment measured by the dynamometer (MM) during the passive rotation trial is shown in Figure 5.2 and consists of the moment due to the force of gravity (MG), on the 123 dynamometer footplate, the moment due to the weight of the foot (MF) and any passive muscle moment (MP): MM = MG+MF+MP 5-2 MG was measured by passively rotating the footplate alone, prior the start of data collection. MM measured from maximum plantarflexion to maximum dorsiflexion was fitted to a third-order polynomial equation, similar to dL = aθ3 + bθ2 + cθ + d 5-1. MG was subtracted from MM at equivalent ankle joint angles. Thus, subtracting MG from MM = MG+MF+MP 5-2 gives: MM-MG = MF+MP 5-3 There is no easy way to accurately estimate MF, so an alternative method was used to isolate MP. In a subset of the original subjects (n=4), MP was calculated while the ankle was rotated throughout the subject’s range of motion in a horizontal position where gravity would have no effect on the measured moment. MP was then calculated from 90◦ to end range of motion in dorsiflexion. This range was used because there was no apparent MP at angles greater than 90◦. In this horizontal position, it was assumed gravity was not affecting the measured moment. However, it was found that passively rotating the footplate through the range of motion gave a small constant offset moment. This moment was subtracted from the measured moment to obtain the true MP. This MP is referred to as the ‘horizontal’ measure. Considering that MP in the vertical position should be equal to MP in the horizontal position, any difference between these measures should be equal to MF. To estimate MP with the subject lying prone, we used linear regression of horizontal Mp vs Mp+Mf calculated from MM-MG = MF+MP 5-3 with the data of the 4 subjects. This equation was then 124 used to estimate MP across ankle angles of 70° to 90° for all subjects. Tendon elongation attributable to MP was estimated by having the subject perform two 7s isometric maximal voluntary contractions (MVC) immediately following the passive trials. While we recognize only a weak voluntary contraction above MP is required to correct for MP, the MVC was performed as part of other tendon mechanical testing (eg. Fletcher et al, 2013, Fletcher et al, 2010). The MVCs were performed with the knee fully extended and the ankle angle at 90°. The MVC eliciting the greatest isometric moment was used for further analysis. Throughout the MVC, a clear fascicle-aponeurosis cross point was tracked on the ultrasound image throughout the MVC. Moment and dL data were fitted to a quadratic regression equation, after correcting for ankle joint rotation (Fletcher et al, 2010): Moment = AdL2 + BdL 5-4 where A and B are constants. Since any MP present during the TE method would contribute to tendon elongation, the dL attributable to MP was estimated from Moment = AdL2 + BdL 5-4 and was added to the measured dL during the passive trial. This was considered the MP-corrected dL for calculation of the corrected MA. To compare corrected and uncorrected MA lengths at a given ankle angle, MA lengths were calculated at 5◦ intervals, from 70-115◦ by fitting each subject’s corrected and uncorrected dL -ankle joint angle relationship, using a third-order polynomial equation, an example of which is shown in Figure 5.1. Test-retest reliability as well as the coefficient of variation for both corrected and uncorrected MA lengths were assessed by having all subjects repeat the study protocol and all analyses were repeated, using both approaches (corrected and uncorrected). To assess the validity of the tendon travel method, 8 (6 males, 2 females) of the 12 subjects later returned to 125 the laboratory. A retest of the Achilles tendon moment arm length using the tendon travel method was done as described above. Achilles tendon moment arm length was also determined with the ankle joint at 90° by measuring the mean of the lateral and medial horizontal distances from the most prominent tip of the malleolus to the posterior aspect of the Achilles tendon using measurement calipers, to the nearest 0.25 mm. A similar method has been well described by Scholz et al. (2008). 5.3.2 Statistics and data analysis Values are presented as mean ± standard deviation, unless otherwise indicated. A twoway repeated measures analysis of variance (ANOVA) was used to assess differences between corrected and uncorrected MA lengths (condition) across ankle angles. A one-way repeatedmeasures ANOVA was used to assess differences in MP and dL as a function of ankle angle. Where a significant effect was found, Tukey’s post hoc multiple comparisons test was used to assess differences in MA length across ankle angles. Test-retest reliability for corrected and uncorrected MA lengths were assessed via intraclass correlation coefficient (ICC) and coefficient of variation (CV) according to Hopkins (2000). Specifically, CVs for test-retest data were obtained for both conditions at each measured angle for each subject and averaged across all subjects in order to assess CVs across angles and conditions. All statistical analyses were performed using GraphPad Prism version 6.01 for Windows (GraphPad Software, La Jolla, CA, USA, www.graphpad.com). Statistical significance was considered P<0.05. 126 5.4 Results The vertical MP, calculated from the linear regression equation of the vertical and horizontal protocols is shown in Figure 5.3. Vertical MP was not significantly different than horizontal MP up to 12 Nm (the greatest MP measured in our subjects). The typical error of vertical (MP + MF) compared to horizontal MP, measured across 80 estimates of MP was 0.58 Nm (95% C.I. = 0.49 to 0.70 Nm), suggesting a negligible effect of MF on MP. The mean passive moment data for all subjects are shown in Figure 5.4. One-way ANOVA revealed a significant difference in MP across measured ankle angles (p<0.001), indicating that passive moment increased significantly at angles less than 90° (r2=0.989, p<0.0001). The tendon elongation attributable to passive moment is also shown in Figure 5.4. As elongation was calculated from passive moment, elongation only increased at angles less than 90°. Figure 5.5 shows the apparent and MP-corrected MA length measurements as a function of ankle joint angle. A two-way repeated measures ANOVA revealed a significant condition x angle interaction (p<0.0001). The corrected MA lengths were 41%, 38%, 25%, 8% and <1% higher at each of the measured angles from 70° to 90°, respectively. The measurement of MA length at 90° using the TE method (35.2±4.8 mm) was not significantly different from the MA length measured using calipers (35.0±4.6 mm, p=0.344). Comparing CVs across angles and conditions revealed no significant angle x condition interaction and no angle main effects; however, there was a significant main effect of condition (p<0.03); the CVs were lower in the corrected condition (8.4 ± 3.3%) compared to the uncorrected condition (11.0 ± 3.8%). 127 5.5 Discussion In estimating the AT MA length non-invasively, the TE method may be preferred as it is less costly and less time-consuming compared to the COR method that uses magnetic resonance imaging. However, the main assumption to the TE method in estimating AT MA length is that any moment acting to stretch the tendon during passive rotation remains constant. At ankle angles greater than 90°, the TE assumption appears valid since no appreciable MP is present during plantarflexion. However, at ankle angles less than 90°, it is obvious that passive forces change during the passive rotation (Fath et al, 2010, Kubo et al, 2001). By measuring the moment-dL relationship during an MVC, this study is the first to account for tendon stretch during the passive rotation and therefore we show here how passive forces affect the estimation of AT MA length using the TE method. It is also important to recognize that an MVC is not required in order to account for tendon translation during this corrected TE method. However, an MVC is routinely performed as part of additional measurements of AT mechanical properties or muscle energetics, so our novel method of determining AT MA length can be performed from data which are already routinely collected. In scenarios where knowledge of the AT MA length is required in order to assess muscle mechanics or energetics, we feel that by developing a method which does not require additional measurements, equipment or data collection is valuable. It has been previously demonstrated using both the TE and COR methods that MA length changes as a function of ankle angle (Fukunaga et al, 1996, Maganaris et al, 2000, Maganaris et al, 1998, Fath et al, 2013, Fath et al, 2010). Specifically, MA length is thought to increase as the ankle is rotated from dorsiflexion to plantarflexion, as a result of the anatomical configuration of 128 the calcaneus. During plantarflexion, there is significant postero-anterior and distal translation of the calcaneus (Leardini et al, 1999), which has been used to explain the apparent increase in moment arm length. However, the calcaneus also rotates as it translates to the posterior, so the length of the MA may not change. Thus, the possibility exists, at least from an anatomical perspective, that AT MA lengths remain relatively constant throughout the ankle joint range of motion. Here we show that when accounting for the effects of MP on dL, AT MA remains constant over the joint range of motion (see Figure 5.5). The relationship between MA length and ankle angle we show here is also similar to the MA lengths determined in three dimensions by Hashizume et al. (2012), who demonstrate that when AT MA lengths are determined in three dimensions, the MA length remains constant across ankle angles. These authors demonstrate that determining AT MA length using the COR method from 2D MR-images results in an overestimation of the AT MA length, a result of the difference in the antero-posterior position of the lateral and medial malleoli. 2D estimates of AT MA length using the COR method has also been shown to be greater than AT MA length estimates using the TE method (Fath et al, 2010, Maganaris, 2004), thus making the TE method more in line with estimates using 3D MRimaging. We estimated the ‘additional’ dL attributable to MP from the Moment-dL relationship measured during each subject’s MVC (Moment = AdL2 + BdL 5-4). The relevant passive moments (shown in Figure 5.4) develop within the toe region of the moment-dL curve, where a small change in MP results in a large change in dL. Thus, a small error in MP would result in a larger error in dL, and therefore corrected MA length estimate. From our data, an over-estimation in MP of 1 Nm would result in an additional 129 dL of 0.6 mm. At angles near maximal dorsiflexion, where uncorrected dL is relatively small, this would contribute substantially to the estimated corrected MA length. The effect of underestimating AT MA length at large angles of dorsiflexion could have important implications for muscle force estimates from joint moments, particularly during the gait cycle where forces are developed in the range of ankle angles where MP is present and AT MA length is substantially underestimated. For example, Cavanagh et al. (1977) have demonstrated the ankle joint angle during the stance phase of running is in a dorsiflexed position. Since large forces are developed during stance (Giddings et al, 2000), underestimating the AT MA length will substantially overestimate the required muscle forces during this portion of the running stride. This effect would have a profound impact on the estimates of muscle energetics during running. We have to consider that our AT MA lengths are smaller than those previously reported using the TE method. This may be the result of our chosen subject pool of endurance runners. These runners are short, compared to subjects in previous studies (Fath et al, 2010, Sheehan, 2012), which may account partially for the smaller AT MA lengths measured here. A short AT MA may also confer an advantage to distance runners in particular, since a short AT moment arm is associated with a lower energy cost of running (Scholz et al, 2008). Shorter moment arms provide an advantage to runners by requiring smaller muscle shortening velocity to achieve a given joint angular velocity (Nagano and Komura, 2003). This effect may be substantial in theory, given the relatively large angular velocities at the ankle joint during submaximal running (KyroLAINEN et al, 2001). MP is significantly higher during AT loading compared to unloading (Fath et al, 2010). Although this effect would be small (1-3 Nm at ankle angles where MP is present), we wanted to 130 avoid the between-subject variability in tendon hysteresis and therefore, only measured MP during AT loading. Because of the viscoelastic nature of the tendon, as the AT is unloaded, the displacement of the tendon-aponeurosis junction is reduced for a given joint rotation. These effects would lead to an under-estimation of MA length and would be dependent on joint position; hysteresis effects being greatest late in the unloading phase. Thus, in measuring tendon displacement during loading, we have avoided this source of error. From a practical standpoint, to avoid this source of error, it may be most appropriate to estimate MA length using the TE method in the same direction at which the tendon mechanical behaviour is to be evaluated. For example, in estimating AT MA length for the purposes of evaluating AT stiffness, tendon translation should be measured while increasing AT load. Further, since we show MA length does not change significantly as a function of ankle angle, estimates of MP can be ignored if MA length is measured where no passive moments are present (ie. at and beyond the 90° ankle angle). It has been previously reported that the CV of MA length using the TE method is 4.59.7% (Fath et al, 2010). The range of CV is primarily dependent on the differentiation technique: differentiating over a large dθ produces smaller CV than differentiation over a small dθ. Furthermore, larger CVs are reported at large dorsiflexion angles. These authors found the most reliable MA length measurements were when the dL-dθ data were differentiated over 20-30◦ increments. The current study’s MA length CVs were 8.4 ± 3.3% for the MP-corrected condition and 11.0 ± 3.8% in the apparent condition. These CVs are larger than those previously reported, since in the current study MA length was differentiated over a relatively small dθ of between 710◦. When differentiating dL-dθ, the estimated MA length is the average MA length over the differentiated dθ. Thus finding the MA length associated with a specific joint angle is not 131 possible when differentiating over a large dθ. It is only by sacrificing reliability and differentiating over a small dθ is it possible to assess average MA lengths at a specific ankle joint angle using the TE method. In fitting our dL/dθ data to a 3rd order polynomial equation, we are able to reliably assess MA length at a specific joint angle. This would be particularly important when estimating the in vivo force-length relationship from moment data, where knowing the MA length at a specific joint angle is warranted. However, since in this study we show MA length does not change appreciably as a function of ankle angle, measurement of AT MA length need only be done once, at an angle where MP is not present. Also, since MA length does not change appreciably, dL-dθ can be differentiated over relatively large dθ, thus minimizing the error of AT MA measurements. If the correction for MP during the TE method was accurate, our estimated MA lengths should represent the horizontal MA length. Given no difference in estimated and directlymeasured MA lengths at 90° in a subset of our subjects, and the reported values over a wide range of subjects (Fath et al, 2013, Fath et al, 2010, Maganaris, 2004, Fukunaga et al, 1996, Spoor et al, 1990), we believe the uncorrected MA lengths were underestimated at dorsiflexion angles and by accounting for MP, resulted in a reliable estimate of AT MA lengths., Therefore, we believe the MP-corrected TE method presented here represents a quick, reliable and accurate estimation of AT moment arm lengths across the ankle joint range of motion. 5.6 Conclusion In conclusion, the TE method offers a quick, affordable alternative to the COR method when estimating MA length; however, it is only when no passive forces are acting to stretch the tendon is the TE method valid, unless this is quantified and accounted for. Here, we have 132 corrected for these passive forces non-invasively, through-out the range of motion. Since we show that MA length does not change as a function of ankle angle, it seems sufficient to measure MA length using the TE method at an ankle angle where MP is negligible, or measuring the MA length manually. Both methods avoid the considerable time and financial costs associated with magnetic resonance imaging. 5.7 Author Contributions All experiments were performed at the Human Performance Lab at the University of Calgary, Calgary, Alberta, Canada. JRF and BRM were responsible for conception and design of the experimental protocol. JRF collected and analyzed the data. JRF and BRM were primarily responsible for interpreting the experimental data. JRF drafted the manuscript and JRF and BRM revised it critically for important intellectual content. All authors approved the final draft of the article. 5.8 Conflict of Interest statement None of the authors report any conflicting interests. 5.9 Acknowledgements The authors would like to thank the subjects for their time and effort in completing the experimental protocol. JRF was supported by NSERC Canada. 133 5.10 Figures Figure 5.1 Calculation of the uncorrected and corrected Achilles tendon MA length from joint angle and tendon displacement. Data are from a representative subject. Uncorrected and corrected displacement during tendon loading is shown as closed and open circles, respectively. Uncorrected and corrected MA lengths were calculated as the first derivative of dL/dθ at all measured intervals, the limits of which are shown as open and closed circles, respectively. Additionally, a third order polynomial was fitted to these dL/dθ data, and differentiated at 5◦ intervals from 70◦-115◦. Solid and dashed lines show the third-order polynomial fit to uncorrected and corrected MA lengths, respectively. Arrows show the angle at which MA lengths were differentiated. 134 Figure 5.2. Calculation of passive moment influencing the estimate of Achilles tendon MA length. Data are from the same subject as shown in Figure 5.1. Vertical line shows anatomical-neutral ankle angle (90°). Negative moment shown on the y-axis represents plantarflexion moment (dorsiflexed position). Measured moment (MM) is shown in red, along with the 3rd-order polynomial fit of MM (solid line). MF+MP (long dashed line) was developed by subtracting MG from MM. The short dashed line represents MP. 135 Figure 5.3. Estimates of TP from practical and criterion experimental approaches. MF was significantly lower at 70° and 75° using the criterion approach compared to the practical approach. This resulted in a higher estimated MP using the criterion vs practical approach. 136 Figure 5.4. Passive Moment (A) and corrected elongation (B) for all subjects as a function of ankle joint angle. Values are mean ± sd. Ankle angles less than 90◦ indicate dorsiflexion angles. Passive moment shown on the y-axis indicates passive moment in the plantarflexion direction. 137 Figure 5.5. Corrected (solid line) vs uncorrected (dashed line) Achilles tendon MA estimates as a function of ankle angle. Values are mean ± sd. Two-way repeated measures ANOVA revealed a significant condition x angle effect (p<0.01). *Significantly different UN vs COR. 138 Chapter Six: Achilles tendon strain energy in distance running: consider the muscle energy cost Jared R. Fletcher1 and Brian R. MacIntosh1 1 Human Performance Laboratory, Faculty of Kinesiology, University of Calgary Calgary, AB Canada Reproduced in whole under the terms of the American Physiological Society (APS) Copyright Agreement. APS permits whole published articles to be reproduced without charge in dissertations and posted to thesis repositories. Full citation is required. Published: Fletcher, J.R. and MacIntosh, B.R. Achilles tendon strain energy in distance running: consider the muscle energy cost. Journal of Applied Physiology. Article in Press, Nov 13, 2014. DOI: 10.1152/japplphysiol.00732.2014 139 6.1 Abstract The return of tendon strain energy is thought to contribute to reducing the energy cost of running (Erun). However, this may not be consistent with the notion that increased Achilles tendon (AT) stiffness is associated with a lower Erun. Therefore, the purpose of this study was to quantify the potential for AT strain energy return relative to Erun for male and female runners of different abilities. A total of 46 long distance runners (18 elite male (EM), 12 trained male (TM) and 16 trained female, (TF) participated in this study. Erun was determined by indirect calorimetry at 75, 85 and 95% of the speed at lactate threshold (sLT) and energy cost per stride at each speed was estimated from previously reported stride length (SL)-speed relationships. AT force during running was estimated from reported vertical ground reaction force (Fz)-speed relationships, assuming an AT:Fz moment arm ratio of 1.5. AT elongation was quantified during a maximal voluntary isometric contraction using ultrasound. Muscle energy cost was conservatively estimated on the basis of AT force and estimated crossbridge mechanics and energetics. Significant group differences existed in sLT (EM>TM>TF, p<0.001). A significant group x speed interaction was found in the energy storage/release per stride (TM>TF>EM, p<0.001), the latter ranging from 10-70 JŸstride-1. At all speeds and in all groups, estimated muscle energy cost exceeded energy return (p<0.001). These results show that during distance running, the muscle energy cost is substantially higher than the strain energy release from the AT. 140 6.2 Introduction It has been generally accepted that a primary role of the muscle-tendon unit in the lower limbs during running is the storage and release of tendon strain energy (Alexander, 1991, Alexander, 1984). This storage and release of tendon strain energy is thought to be an important factor in keeping the energy cost of running (Erun) at a low value. During running, the Achilles tendon (AT) is stretched, storing strain energy. A portion of this strain energy is returned during the subsequent shortening phase thereby reducing the work required by the muscle (Cavagna et al, 1968, Voigt et al, 1995). There have been several suggestions that the elastic recoil provided by the AT contributes a significant portion of the energy for propulsion (Arampatzis et al, 2006, Hof et al, 2002, Lichtwark and Wilson, 2008); however, no study to date has considered the energy cost of muscle contraction for the muscle in series with the tendon releasing energy. This energy cost would be necessary for the tendon strain energy storage to occur. Estimates of strain energy storage of the AT are typically performed by either directly or indirectly measuring AT elongation as a function of AT force (Shadwick, 1990). The area under the force-elongation curve is considered AT energy storage. Considering estimates of tendon hysteresis, or measuring the tendon translation as a function of force during force decline provides a measure of AT energy release. AT energy storage can be estimated in vivo by combining simultaneous measurements of AT elongation and torque using ultrasonography and dynamometry, respectively. The amount of AT strain energy storage/release varies as a function of AT stiffness. For a given AT force, energy storage would be proportional to elongation; a stiffer tendon will store less energy. Considering that it has been demonstrated that AT stiffness is higher in trained compared to untrained runners (Hobara et al, 2008, Albracht and Arampatzis, 2006) and 141 changes in AT stiffness are associated with changes in Erun (Fletcher et al, 2010, Arampatzis et al, 2006), more highly trained runners should have a lower capacity for AT energy storage/release and yet this deficiency is associated with a lower Erun. These recent results seem difficult to rationalize with the notion that the AT serves to store and release energy. Thus, it appears as though the energy-saving contribution of the stiffer Achilles tendon cannot solely be a result of stored strain energy return. Ker et al. (1987) have estimated that the strain energy stored in the Achilles tendon during running at 4.5 m·s-1 was 35 J•step-1 and this was considered to contribute a substantial proportion of energy to the total Erun. However the estimated AT forces required (4700 N) to store this amount of energy is near published estimates of maximum isometric force of 50006000 N (Fletcher et al, 2010, Albracht and Arampatzis, 2013). A similar amount of energy storage has been reported in human hopping (38 J), contributing approximately 16% of the total mechanical work of the hop (Lichtwark and Wilson, 2005). Using a buckle-type transducer to measure AT force directly, Fukashiro et al. (1995) found a smaller amount of tendon strain energy was stored (6-7 J•jump-1) in squat and countermovement jumps, and this represented 1723% of total calf muscle work; a result of a lower measured AT force (<2300 N) during the jumps. It would be expected that AT energy storage during running would be smaller than that found in hopping, as a result of lower vertical ground reaction forces (Cavanagh and Lafortune, 1980) and therefore lower AT force. AT force can be estimated during running from ground reaction forces, and the ratio of the resultant ground reaction forces and AT moment arms (Ker et al, 1987). Considering Erun of trained and elite male and female distance runners measured in our lab (Fletcher et al, 2010, Fletcher et al, 2013) and others (Shaw et al, 2013) is within the range 142 of 4.2-4.6 J·kg-1·m-1, the energy storage/release of the AT would appear to be a small proportion of the total Erun. To date however, no study has evaluated estimates of the AT energy storage and release during running from measurements of AT elongation and moment arm using ultrasound. Furthermore, no study has considered that in order to store energy in a tendon, the muscle in series with that tendon must contract, using additional energy. This muscle energy cost would be a portion of the Erun, effectively reducing the value of storing energy in the tendon and may be the reason that improved economy is associated with a stiffer Achilles tendon. A recently proposed alternative view of the role of tendons is in reducing the energy cost of muscle contraction by minimizing the amount and/or velocity of muscle shortening (Fletcher et al, 2013). The tendon, if optimally stiff, may allow the muscle fascicles to remain nearly isometric during running, while muscle-tendon unit lengthening and shortening can be accommodated primarily by the tendon (Lichtwark et al, 2007, Ishikawa et al, 2007). In keeping the muscle fascicle shortening velocity low, the muscle can operate near its optimal sarcomere length (Askew and Marsh, 1998), and the required level of muscle activation is minimized (Fletcher et al, 2013). Considering that during running the triceps surae muscle fibers do not undergo substantial stretch prior to shortening (Lichtwark et al, 2007, Ishikawa et al, 2007), an optimally-tuned AT would result in less fiber shortening to achieve active joint rotation. As a result, the joint range of motion can be accommodated primarily by the tendon, keeping the energy cost of the contraction low (Roberts et al, 1997). However, in order to do so, AT stiffness must be appropriately tuned to minimize the amount of fascicle shortening. With less than optimal stiffness, the amount and/or velocity of muscle fiber shortening is increased and this shortening will increase muscle energy cost (Fletcher et al, 2013), elevating Erun. 143 Therefore, the purpose of this study was three-fold: 1. To estimate the proportion of strain energy storage/release of the AT relative to total Erun. 2. To estimate the energy cost of muscle contraction required by the muscle in series with the AT. 3. To determine whether the contribution of strain energy storage/release differed between runners of different abilities and/or of different sexes, because it is known that the mechanical properties of the AT would differ. 6.3 Methods A total of 46 distance runners participated in this study. Some of these runners had previously participated in studies performed in our lab (Fletcher et al, 2013, n=18, Fletcher et al, 2010, n= 8). Those data were combined with new data to address the specific research questions posed here. The runners gave their informed written consent to participate in the experimental procedures, which were approved by the University of Calgary Conjoint Health Research Ethics Board. Runners were divided into either “elite” or ‘trained’ based on the highest level of competition achieved. Subjects were considered to be elite male runners (n=18) if they had competed at a National Championship (1500m or longer, including marathon and cross country race distances) within 6 months of the study and running a minimum of 6 times per week, with a minimum average training volume of 70 km·wk-1 of running for at least six weeks prior to participating in the study. Trained runners (n=28, 16 males and 12 females) were those runners not meeting the inclusion criteria for the elite group, but participating in run training a minimum of five times per week, with a minimum average training volume of 40 km·wk-1, during the six 144 weeks prior to the beginning of the study. All runners were following a similar periodized training plan for either the 10 km or half-marathon race distance and were free of any neuromuscular or musculoskeletal injuries at the time of the study. Subject characteristics are shown in Table 6-1. 6.3.1 Experimental Protocol The subjects visited the lab on two separate occasions. On the first visit, an incremental exercise test to exhaustion was performed on a treadmill (Woodway Pro, Woodway USA, Waukeshka, WA) to determine the subject’s maximal oxygen uptake (𝑉𝑂2 max) and speed associated with the lactate threshold (sLT). 𝑉𝑂2 max   and sLT were determined based on methods used previously in our lab (Fletcher et al, 2010, Fletcher et al, 2009). sLT was defined as the speed at the stage preceding that which elicited a [BLa-] increase greater than 1 mM. All tests were terminated due to volitional exhaustion. 𝑉𝑂2 max  was defined as the highest 30 s average 𝑉𝑂2 during the test. All subjects attained 𝑉𝑂2 max based on primary or secondary criteria previously reported from our lab (Fletcher et al, 2013). Between 48-72 hours following the 𝑉𝑂2 max testing session, the subjects returned to the lab for determination of AT stiffness and Erun. AT stiffness of the right AT was determined on a dynamometer (Biodex System 3, Shirley NY USA) as described previously (Fletcher et al, 2010, Fletcher et al, 2013). The subjects performed three isometric ramp maximal voluntary contractions (MVC) of the right plantarflexors. Moment elicited during the MVC was sampled at 100 Hz. The trial eliciting the highest moment was used for analysis. During each MVC, a 12.5 MHz linear array ultrasound probe (50mm, Philips Envisor, Philips Healthcare, Eindhoven, Netherlands) was used to visualize the medial gastrocnemius muscle (MG) fascicles at 49 Hz, 145 close to the AT. The ultrasound probe was placed on the MG muscle belly, near the myotendinous junction, and secured using a custom-built apparatus. AT tendon elongation was estimated by the displacement of an insertion of a fascicle into the deep aponeurosis, measured using ImageJ, (NIH, Baltimore, MD, USA) during the MVC. Measured moments and AT elongations were corrected for joint rotation (Fletcher et al, 2010, Fletcher et al, 2013) and AT moment arm length at the 90° ankle angle was estimated using the tendon travel method (Ito et al, 2000). AT force was calculated by dividing the ankle joint moment by the estimated AT moment arm. AT Force (F) - elongation (dL) data were fitted to a quadratic regression equation using: F = AdL2 + BdL 6-1 where A and B are constants. AT stiffness was defined as the slope of the fitted F-dL equation from 50-100% of maximum isometric plantarflexion force. AT force during running was estimated from the assumed vertical ground reaction forces (Fz) during running, estimated as a function of running speed and body mass (Keller et al, 1996), assuming the Fz moment arm at peak Fz was 1.5x greater than the AT moment arm (Giddings et al, 2000). The Fz represents the major component (>90%) of the resultant ground reaction forces during steady-state running (Kram et al, 1998); however, we acknowledge that by not considering the horizontal forces, we have underestimated the required AT force during running. AT energy storage during running was estimated from the area under the measured AT force-dL curve Figure 6.1. AT energy release was estimated assuming an AT hysteresis of 10% (Finni et al, 2012). Immediately following the measurement of AT force and elongation, Erun was measured. After a 10 minute warm up at 8 km•hr-1 for the females and 9.6 km•hr-1 for the males, the 146 subjects ran at 75, 85 and 95% sLT for 5 minutes each, with a 5 minute standing rest period between speeds. The steady-state 𝑉𝑂2 , defined as the average 𝑉𝑂2 over the final 2 minutes of each stage, was used to calculate Erun. Erun (J·stride-1) was calculated similar to Fletcher et al. (2010): Erun = 𝑉𝑂2 x (5.1583 x RER +15.972) x S-1 x SL 6-2 where 𝑉𝑂2 is measured in mL·min-1, speed (S) is measured in m·min-1, and stride length (SL) is measured in m. SL, defined as the distance between successive contacts of the same foot, was estimated for each subject at each speed based on the previously reported SL-speed relationship for both males and females (Cavanagh and Kram, 1989). Consequently, the estimate of AT energy storage and release is double that measured for one tendon because it includes two footstrikes per stride. 6.3.2 Muscle energy cost In order for the AT to store and release strain energy during ground contact, active muscle contraction must occur because the triceps surae muscles are in series with the Achilles tendon. The energy cost of this muscle contraction was calculated by first estimating the number of active in-parallel crossbridges (per half-sarcomere) required to produce the AT force and this was multiplied by the number of cross-bridge cycles expected for the estimated shortening during each stance phase and the number of half-sarcomeres in series. To estimate the number of crossbridges required in parallel, AT force was divided by the estimated force per crossbridge of 3 pN, (Rome et al, 1999, Finer et al, 1994). The number of half-sarcomeres in series was estimated as the ratio of medial gastrocnemius fascicle length during ground contact to half-sarcomere length. The MG fascicle length during ground contact 147 was calculated for each subject based on the fascicle length-force relationship for each subject measured during the MVC, as described previously by Fletcher et al (2013). Sarcomere lengths at rest and during running were calculated based on force-fascicle length values from Maganaris (2003) and assuming an optimal sarcomere length during maximal activation of 2.6 µm at the short side of the plateau region of the force-sarcomere length relationship (Herzog and ter Keurs, 1988). The number of crossbridge cycles was estimated from the amount of shortening within each half-sarcomere. This was estimated from the magnitude of MG fascicle shortening from rest to stance and accounting for shortening due to anticipated joint rotation. To estimate the number of cross-bridge cycles required to accommodate this magnitude of shortening, we assumed the filaments move 12 nm with each crossbridge cycle (Barclay et al, 2010). The amount of joint rotation during stance was considered 10° (Lichtwark et al, 2007). The estimated fascicle shortening due to joint rotation was calculated for each subject as the product of AT moment arm length (mm) and joint rotation (rad), based on the tendon travel method, as described by Fletcher et al. (2010). The shortening due to joint rotation was added to the shortening due to force-dependent stretch of the tendon to calculate total shortening during the stance phase of running. We further assumed an ATP cost to crossbridge cycle ratio of 1:1 and an energy release of 48 kJ•mol-1 ATP (Homsher and Kean, 1978). 6.3.3 Statistics Values are presented as mean ± standard deviation (sd), unless otherwise indicated. A two-way ANOVA (group x speed) with repeated measures (speed) was used to test for differences in Erun, energy storage/return, sLT, and muscle energy cost. When there was no significant interaction and a significant main effect was found, Tukey’s post hoc test was used to 148 detect significant differences between the three speeds. Pearson product-moment correlation analysis was used to examine the relationship between AT stiffness and strain energy contribution to Erun. All analyses were performed using GraphPad Prism version 6.01 for Windows (GraphPad Software, La Jolla, CA, USA, www.graphpad.com). The a priori level of statistical significance was set at alpha <0.05. 6.4 Results As shown in Table 6-1, sLT was significantly different between groups (p<0.001). Consequently, since we estimated SL from running speed, a significant group x speed interaction existed in SL (p<0.001). Mean SL ranged between 2.34±0.23 m at 75% sLT and 2.86±0.30 m at 95% sLT in EM. SL in TM and TF across speeds was 2.11±0.10 m to 2.57±0.31 m and 1.93±0.16 m to 2.34±0.21 m, respectively. Erun for all groups at all measured speeds is shown in Figure 6.2. There was no significant group x speed interaction; however, there was a significant main effect of speed on Erun (p<0.001); Erun increased with relative speed. Maximum isometric force was 5180±1998 N in EM, 3528±1196 N in TM and 2151±759 N in TF. One-way ANOVA revealed a significant effect of group for maximum isometric force (p<0.001). Furthermore, AT stiffness was significantly different between groups (p<0.001); AT stiffness was 408±128 N•mm-1 in EM, 188±62 N•mm-1 in TM and 135±57 N•mm-1 in TF. Figure 6.3 shows the mean (±sd) F-dL curves for all groups across the force range expected for running, Mean Fz, estimated from running speed and body mass increased as a function of running speed and was significantly lower at all speeds in TF compared to TM and EM (Table 6-2. Fz (N) as a function of % sLT. Despite running speeds being higher in EM 149 compared to TM, significant differences in body mass between EM and TM resulted in no differences in estimated Fz during running. Since AT force was estimated from previously reported Fz-speed relationships and known body mass, AT force increased as a function of running speed. AT force across all measured speeds was lower in TF (range: 1520 to 1877 N) compared to either EM (2200 to 2734 N) or TM (2200 to 2724 N). The highest estimated AT force (ie. at 95% sLT) was 59±20% of maximum calculated isometric force in EM. This was lower than either TM (84±28%) or TF (97±34%). In five of the 16 subjects (31%) in TF, estimated AT force was greater than the maximum calculated isometric force. Estimated AT force was greater than maximum isometric force in 3 of 12 subjects in TM (25%). In none of the subjects in EM was estimated AT force greater than maximum isometric force. Resting fascicle length was 55.1±10.9 mm in EM, 53.9±4.8 mm in TM and 57.2±7.9 mm in TF. Assuming a common sarcomere length of 2.60 µm at the short side of the plateau during maximal activation, these fascicle lengths corresponded to an average resting sarcomere length of 3.35±0.90 µm in EM, 4.11±1.07 µm in TM and 3.94±1.01 µm in TF. Estimated sarcomere lengths at rest were significantly shorter in EM (p<0.03) compared to either TM or TF. Fascicle length at MVC was 37.4±10.1 mm in EM. This was significantly longer (p<0.05) than the measured fascicle lengths at MVC of either TM (32.4±10.7 mm) or TF (31.2±7.7 mm), respectively. Estimated fascicle shortening during running increased as a function of speed, but was significantly less (p<0.001) in EM (14.0±2.8 to 15.9±3.3 mm across speeds) compared to either TM (23.3±6.6 to 27.3±8.0 mm) or TF (23.9±6.1 to 28.0±7.5 mm). Estimated sarcomere lengths during running were not different between groups at any of the measured speeds (p=0.48), but decreased as a function of relative speed (p<0.001); a result of 150 higher estimated AT forces, and thus fascicle shortening as a function of relative running speed. Average running sarcomere lengths across groups was 2.74±0.53 µm, 2.62±0.52 µm and 2.50±0.52 µm at 75, 85 and 95% sLT, respectively. Combining the results for Erun and SL, the estimated energy cost per stride revealed a significant group x speed interaction (p<0.001). These results are shown in Figure 6.4 and suggest that across all speeds, the energy cost per stride was lowest in TF and highest in EM. Given the estimated AT forces during running, we estimated the AT energy release (J•stride-1) from the area under the measured F-dL curve. The amount of energy released is shown in Figure 6.5. AT energy release increased as a function of relative running speed (p<0.001) and was significantly higher in TM compared to either EM or TF (p<0.02). AT energy release was not related to sLT (Figure 6.6). A portion of the energy cost per stride consists of the energy cost of triceps surae muscle contraction, which is necessary to allow energy storage in the AT to occur. Estimating this energy cost from AT forces and sarcomere shortening during the stance phase of running revealed a significant group x speed interaction (<0.001). Values for muscle energy cost are presented in Figure 6.5. 6.5 Discussion Based on our above-mentioned aims and assumptions, we show here that: 1. The amount of strain energy release from the AT was 10-70 J•stride-1 across male and female runners of different performance capabilities. 151 2. The range of energy cost of muscle contraction in order for this AT energy storage/release to occur across all subjects and running speeds is estimated to be 90 to 525 J•stride-1. 3. AT energy release was significantly higher in TM compared to either EM or TF; however the estimated muscle energy cost required for this energy release to occur was lowest in EM. 6.5.1 Erun between groups We were previously unable to detect a difference in Erun between similarly-trained male and female runners (Fletcher et al, 2013) and here we also were unable to detect a significant difference between trained and elite male and female runners at similar relative intensities of running. These results are consistent with the notion that when expressed at the same relative intensity of running (ie. %sLT), male and female runners show similar values of Erun. Our results also further confirm that Erun increases as a function of relative speed (Fletcher et al, 2009, Shaw et al, 2013). 6.5.2 Tendon strain energy release The amount of tendon strain energy storage in each stride was calculated from the measured tendon stiffness and the estimated AT force during running, which in turn was based on estimates of Fz, at the speed at which each subject ran. In order to estimate AT force from Fz, we assumed a fixed moment arm length for Fz as a function of AT moment arm length. It is well-established, however that this length is not fixed during ground contact and is different between rear and mid/forefoot strikers (Cavanagh and Lafortune, 1980). However, without having directly-measured the magnitude and location of ground reaction 152 forces of our subjects during running, we have used previously-reported values for the average Fz moment arm length during ground contact. We specifically chose a fixed Fz:AT moment arm ratio of 1.5 since in most cases, a ratio larger than this resulted in AT force greater than the maximum isometric force for TM and TF. This ratio is also consistent with previous literature (Giddings et al, 2000). Since some fascicle shortening would have occurred during ground contact, it seems unlikely that AT force during running would be near the measured maximum isometric force and certainly would not exceed it. The force-velocity relationship would preclude this possibility. As a result, we have very likely over-estimated the AT force during running, particularly in TF, where 5 of 16 subjects’ AT force was greater than maximum isometric force. Over-estimating AT force would result in both an overestimate of the AT energy release per stride as well as the muscle energy cost for this AT release to occur. It has previously been shown that the Achilles tendon releases 1.3 J•step-1 during walking (Maganaris and Paul, 2002), and up to 38 J per jump during continuous, one-legged hopping (Lichtwark and Wilson, 2005). In the latter case, estimated AT forces during hopping approached 5000 N, which explains the larger AT energy storage compared to the estimates made here, where AT forces at the highest measured speeds were <2800 N. In spite of these high forces during hopping, tendon strain energy return represented a small portion of the total mechanical work (<16%), and energy would have been required for muscle contraction in order to achieve this energy return. Although many studies have presented estimates of energy storage and release from the Achilles tendon, none have considered the need for simultaneous muscle contraction to allow the tendon stretch and energy release to occur. Here, we provide an estimate for that muscle energy 153 cost. This muscle energy cost represents a portion of the total metabolic cost of running and is necessary for tendon strain energy storage and release to occur. The energy cost of this muscle contraction is directly proportional to muscle shortening, so a compliant tendon increases this energy cost. 6.5.3 Estimates of muscle energy cost to allow for tendon strain energy storage In accounting for the energy cost of muscle contraction to allow for AT energy storage/release, these results demonstrate that the return of elastic strain energy is less than the muscle energy cost required for storage/release to occur. This brings into question the relevance of tendon strain energy return alone contributing significantly to reducing the metabolic cost of running. As an extreme, assuming (unrealistically) that maximum isometric force is generated during each stride and a conservative estimate of AT hysteresis of 10% (Finni et al, 2012), the average maximum possible amount of energy released from the AT is 34 J·stride-1 in TF to 71 J·stride-1 in EM; clearly a small portion of the total metabolic cost (500-900 J·stride-1). Any shortening during Fz would contribute to elevate muscle energy cost above our estimate, as a result of the muscle’s force-length-velocity relationships and subsequent increases in the required level of muscle activation (Fletcher et al, 2013) and due to shortening-induced increase in cross-bridge turnover (Barclay et al, 2010). Presumably, this shortening-induced energy cost would be higher in the lesser-trained subjects (Sano et al, 2013), given their relatively compliant tendons, and greater relative force required to generate Fz. Here we have calculated this shortening-induced energy cost to be on average more than 8-fold higher than the energy 154 released from the stretched AT. Therefore, a considerable muscle energy cost can be saved if muscle-tendon unit shortening can be accommodated by the AT alone and MG fascicle shortening is minimized as would be the case with a stiff AT. It seems unlikely that sarcomere length is longer than we have estimated, particularly for those who activate near maximum. If the true sarcomere length is shorter, then more sarcomeres will be needed in series and this will increase the estimate of the energy required. To further highlight our conservative estimate of the shortening-induced muscle energy cost, our estimated force per crossbridge (3 pN) is the force associated with the isometric crossbridge force. The force per crossbridge would be considerably lower during shortening (Barclay et al. 2010). For example, if the force per cross-bridge was actually 2 pN, 30% more cross-bridges would need to be engaged and the energy cost would be 30% higher. A similarly-conservative estimate of the muscle energy cost is the estimated crossbridge step size (12 nm), which is the stepsize at which efficiency is maximal. Stepsize decreases at slow shortening velocities (Barclay et al, 2010), thus any error associated with the reduction in stepsize from 12 nm would result in a proportional increase in the estimated muscle energy cost. We also recognize some variability in our estimated AT forces during running as a result of estimating these forces from the Fz-speed relationship (Keller et al, 1996). The variability associated with estimating Fz from running speed is in the order of 10-20%. Using mean values for our subjects, overestimating Fz by 10% would result in both an over-estimated muscle energy cost and AT strain energy release of 15%. A similar underestimation in Fz would result in a 17% lower muscle energy cost and tendon strain energy release; however, in our subjects, it is likely we have overestimated Fz in some and underestimated Fz in others. Furthermore, by considering 155 only the Fz moment arm length, rather than the resultant ground reaction force moment arm in the estimated AT force during running, we have further underestimated the required AT force. Given the magnitude of the horizontal ground reaction force represents <10% of the Fz (Kram et al, 1998), we feel this error is relatively minor. However, we acknowledge that we may have underestimated the required AT force during running. This results in an underestimate of both the tendon strain energy and the muscle energy cost. Therefore, we acknowledge a very conservative estimate of the muscle energy cost to allow tendon strain energy storage to occur. It is well established that much of the required length change of the muscle-tendon unit during running can be taken up by stretch of a relatively compliant in-series tendon, thus allowing the active muscle fascicles to shorten to a lesser extent and at a slower velocity (Fukunaga et al, 2001, Ishikawa et al, 2007). This minimizes the required level of muscle activation to achieve the target force as a result of the muscle’s force-velocity relationship. In so doing, the muscle energy cost is reduced considerably (Fletcher et al, 2013). Relative to the total metabolic cost per stride, the amount of tendon strain energy that is stored and released per stride is small. Our estimates shown here demonstrate that this energy release is on average less than 42 J per stride. Therefore, we argue that the energy savings in reducing the muscle energy cost more than makes up for decreased energy storage and release when the tendon’s mechanical properties are optimal. If mechanical properties are optimal, the role of a tendon connected in-series with the muscle is to minimize muscle fascicle shortening by taking up much of the length change required by the whole muscle-tendon unit (Roberts, 2002). This effect serves to minimize muscle metabolic cost to a much greater extent than the storage and release of tendon strain 156 energy; by keeping fibre shortening to a minimum, the muscle’s force-length-velocity properties and muscle activation can be optimized (Fletcher et al, 2013). The range of sarcomere lengths used during locomotion corresponds to the plateau region of the relevant force-length curves (Ishikawa et al, 2007). This maximizes the amount of force for a given level of activation, or conversely as would be the case in steady-state submaximal running, minimizing the required level of activation for a given force. The fact that we saw no difference in estimated sarcomere lengths between groups suggests similar sarcomere lengths are achieved to run at a common relative speed. 6.6 Conclusion From our estimates of tendon strain energy storage and release and muscle energy cost for this storage/release to occur, we conclude that the amount of tendon strain energy released represents a very small portion of the total metabolic cost to run a given speed. Furthermore, this energy return comes at a considerable muscle energy cost. Therefore, reducing muscle energy cost through reductions in muscle fascicle shortening during running even if this means less energy return from the tendon, contributes to an improved economy of running. 6.7 Author Contributions All experiments were performed at the Human Performance Lab at the University of Calgary, Calgary, Alberta, Canada. JRF and BRM were responsible for conception and design of the experimental protocol. JRF collected and analyzed the data. JRF and BRM were responsible for interpreting the experimental and theoretical data. JRF drafted the manuscript and JRF and 157 BRM revised it critically for important intellectual content. All authors approved the final draft of the article. 6.8 Acknowledgements and disclosures The authors would like to thank Dr. Chris J. Barclay, PhD for his insights regarding the estimates of muscle energy cost during running and the subjects for their time and effort in completing the experimental protocol. JRF was supported by NSERC Canada. None of the authors report any conflicting interests. 158 6.9 Tables Table 6-1. Subject Characteristics. 159 Table 6-2. Fz (N) as a function of % sLT. 160 6.10 Figures Figure 6.1. Estimated Achilles tendon energy storage and release during running. Short dashed line represents the average EM force-AT elongation curve measured during the isometric MVC. Solid and short-dashed lines represent the F-dL of loading and unloading during running, respectively. The area under the unloading curve represents the AT energy release. The area enclosed by the loading and unloaded curves represents the estimated AT energy lost as heat (assuming AT hysteresis = 10%). 161 Figure 6.2. Erun at the three measured relative speeds for all groups. No group x speed interaction existed; however, there was a significant main effect of speed on Erun (p<0.001). 162 Figure 6.3. Average F-dL curves for all groups during running. Solid and dashed lines represent the mean and sd of the second-order polynomial (F = AdL2 + BdL 6-1) for all groups, respectively. 163 Figure 6.4. Comparison of the energy cost per stride for all groups and across relative speeds. A significant group x speed interaction existed in the energy cost per stride (p<0.001). 164 Figure 6.5. AT energy release (filled bars) relative to estimated muscle energy cost required to allow AT energy storage to occur (hashed bars) for all groups and all measured running speeds. AT energy release and muscle energy cost increased across speeds in all groups (p<0.001). 165 Figure 6.6. Relationship between AT energy release and 95% sLT. Filled, grey and open squares represent EM, TM and TF subjects, respectively. When assessed across all groups the relationship was not significant (dashed line, r2 = 0.01). The relationship was similar when assessed across the other two measured speeds (75% and 85% sLT). 166 Chapter Seven: Changes in Achilles tendon stiffness and energy cost following a prolonged run in trained distance runners Jared R Fletcher & Brian R MacIntosh Human Performance Lab, Faculty of Kinesiology, University of Calgary Calgary, AB Canada 167 7.1 Abstract The Achilles tendon (AT) has viscoelastic properties; thus during prolonged running the magnitude of AT length change may increase over time. Assuming the same AT force and hysteresis during running, additional lengthening should increase tendon strain energy return. However, AT elongation might also affect the magnitude of triceps surae (TS) muscle shortening. Any additional muscle shortening for the same duration at the same force would increase velocity of shortening and require greater activation and elevate the energy cost contraction. Therefore, we aimed to quantify the tendon strain energy return and muscle energy cost necessary to allow energy storage to occur prior to and following prolonged running. 14 trained male (n=10) and female (n=4) distance runners (24±4 years, 1.72±0.09 m, 61±10 kg, 𝑉𝑂2 max   64.6±5.8 ml•kg-1•min-1) ran 90 minutes (RUN) at approximately 85% of lactate threshold speed (sLT). Immediately prior to and following RUN, AT stiffness and running energy cost (Erun) at 85% sLT were determined. AT energy return was calculated from dynamometry and ultrasound. TS energy cost was estimated on the basis of AT force and assumed crossbridge mechanics and energetics. Following RUN, AT stiffness was reduced from 332±168 N•mm-1 to 304±151 N•mm-1 (p<0.012). Erun increased from 4.56±0.32 J•kg-1•m-1 to 4.62±0.32 J•kg-1•m-1 (p=0.044). Estimated AT energy return was not different following RUN (p=0.061). Estimated TS muscle energy cost increased significantly by 11.8±12.3 J•stride-1, (p=0.0034), accounting for the post-RUN increase in Erun (8.6±14.5 J•stride-1). These results demonstrate that a prolonged, submaximal run can significantly reduce AT stiffness and increase Erun in trained runners, and it appears that the elevated TS energy cost contributes substantially to the elevated Erun. 168 7.2 Introduction The energy cost of running (Erun) is one of the key determinants of distance running performance (Di Prampero, Atchou, Brückner et al. 1986). Erun is primarily determined by the energy cost of generating the force needed to support body weight for the duration of the stride (Taylor, Heglund, McMahon et al. 1980, Kram and Taylor. 1990) and there is growing evidence to suggest that mechanical properties of the tendons of the major force-generating muscles that are active during running greatly influence Erun. It has been suggested that the mechanical properties of tendon allow the muscle to operate near-isometrically (Lichtwark, Bougoulias and Wilson. 2007, Ishikawa, Pakaslahti and Komi. 2007), thus minimizing the magnitude and velocity of muscle shortening during contraction (Askew and Marsh. 1998). This minimized shortening magnitude and shortening velocity will minimize the required level of muscle activation needed to generate the necessary force, due to the muscle’s force-velocity relationship (Fletcher, Groves, Pfister et al. 2013, Roberts. 2002). The Achilles tendon (AT) undergoes rapid lengthening and shortening during the stance phase of running, taking up much of the length change of the whole muscle-tendon unit, preventing the need for muscle fibre lengthening and keeping to a minimum the triceps surae (TS) muscle fascicle shortening (Lichtwark, Bougoulias and Wilson. 2007, Ishikawa, Pakaslahti and Komi. 2007, Roberts. 2002). Hypothetically then, the muscle energy cost is lowest when the AT is mechanically ‘tuned’ to allow near-isometric muscle fascicle force generation during the stance phase and the whole length change of the muscle-tendon unit can be accommodated by the AT alone, greatly reducing the amount of shortening required by the muscle fascicles (Alexander. 1991). 169 However, the AT demonstrates viscoelastic properties; when cyclically loaded, such as during running of sufficient duration, the AT may develop dynamic creep (Hawkins, Lum, Gaydos et al. 2009), resulting in greater length change for a given load. During running, assuming similar kinematics at the same running speed, a reduction in AT stiffness would be expected to move the mechanical properties away from the energetically-optimal AT properties; the reduction in AT stiffness may result in increased fascicle length change. This additional fascicle shortening would require additional muscle energy (Fletcher, Groves, Pfister et al. 2013). This increased muscle energy cost would contribute to an elevated Erun. Bouts of long distance running of 60-90 minutes have been shown to acutely elevate Erun in moderately-trained runners (Hunter and Smith. 2007, Xu and Montgomery. 1995). Increases in Erun have also been shown following half-marathon and marathon runs (Peltonen, Cronin, Stenroth et al. 2012). Repetitive contractions, like those performed during running, have been shown to decrease AT stiffness (Kay and Blazevich. 2009, Kubo, Kanehisa, Kawakami et al. 2001a, Kubo, Kanehisa, Kawakami et al. 2001b). Furthermore, we have previously shown that changes in AT stiffness correlate with a changes in Erun, (Fletcher, Esau and Macintosh. 2010). Thus, it seems logical to hypothesize that a submaximal run of sufficient duration will cause a decrease in AT stiffness and a corresponding increase in Erun. No study to date, however, has related any change in Erun to changes in estimated muscle energy cost. Farris et al. (2011) measured AT stiffness in recreational runners before and following a 30 minute run at 12 kmŸhr-1. At this speed, the average stride length is approximately 2.25 m (Cavanagh and Kram. 1989a), representing approximately 2667 strides over the course of the run. A single bout of running did not elicit a change in AT stiffness in these runners, but the duration (# of AT loading cycles) may not have been sufficient to elicit a change in AT stiffness. 170 Similar results have been shown in lesser-trained runners following half-marathon (21.2 km) and marathon (42.2 km) runs whereby AT stiffness measured 1 hour following the half or fullmarathon run was not changed (Peltonen, Cronin, Stenroth et al. 2012). However, an average post-run increase in Erun of 6% was observed, suggesting the post-run increase in Erun was not dependent on changes in AT stiffness. It is unknown if a similar lack of change in AT stiffness can be demonstrated in a run of sufficient duration (ie. greater than 30 mins) in trained runners, where it was anticipated the run would be as fast or faster than that which can be sustained during a marathon (Peltonen, Cronin, Stenroth et al. 2012). It seems likely that the combination of a sufficiently fast run and a high number of loading cycles is required to elicit a significant change in AT stiffness. This hypothesis is consistent with the results of Arampatzis et al. (2007) who provided evidence that a threshold of applied AT strain existed in order to induce any mechanical changes in the tendon. If significant dynamic creep does occur during/following a prolonged run, this implies that for any muscle-tendon unit length during stance, AT elongation would be greater, necessitating greater TS fascicle shortening. This additional fascicle shortening is expected to be accompanied by an elevated energy cost and it is unclear whether this elevated muscle energy cost is sufficient to contribute to the expected increase in whole-body Erun. To date, the change in AT energy storage and release relative to the change in the associated muscle energy cost has not been described. Therefore, the purpose of this study was to investigate the changes in AT stiffness and muscle energetics prior to and immediately following a typical long-distance training run of 90 minutes. 171 7.3 Methods 14 trained male (n=10) and female (n=4) distance runners participated in this study. It was anticipated that the male and female runners would demonstrate a range of values for Erun and AT stiffness. Subject characteristics are shown in Table 1. All runners were training regularly at least 6 times per week and following a similar, periodized training plan. None of the runners had any neuromuscular or musculoskeletal injuries at the time of the study. The runners gave their informed written consent to the experimental procedure, which was approved by the University of Calgary Conjoint Health Research Ethics Board. The subjects visited the lab on two separate occasions. The experimental protocol is shown in Figure 7.1. During the first visit, an incremental test to exhaustion was performed on a treadmill (Woodway Pro, Woodway USA, Waukeshka, WA) to determine the subject’s maximal oxygen uptake (𝑉𝑂2 max) and speed associated with the lactate threshold (sLT). The methods used to determine 𝑉𝑂2 max and sLT were similar to ones used previously in our lab (Fletcher, Pfister and MacIntosh. 2013, Fletcher, Esau and Macintosh. 2010, Fletcher, Esau and MacIntosh. 2009). All incremental tests were terminated due to volitional exhaustion and all subjects attained 𝑉𝑂2 max based on primary or secondary criteria (Fletcher, Pfister and MacIntosh. 2013). Between 48 and 96 hours following the first laboratory visit, the subjects returned to the lab for determination of AT stiffness and Erun before and following the prolonged, 90 minute run (RUN). RUN was performed on an outdoor road surface over level ground under similar weather conditions (14-19° C, 38-54% relative humidity) in the subject’s own running shoes at a pace corresponding to approximately 85% of sLT. The speed and heart rate associated with 85% sLT were determined from the incremental test to ensure consistent relative pacing between 172 subjects. Each subject wore a heart rate monitor (Suunto t6c, Oy, Finland) during RUN and was instructed to maintain the pre-instructed heart rate throughout. Since these were experienced runners, they were also given information prior to RUN regarding the approximate target speed (minutes•km-1 and minutes•mile-1). Following RUN, heart rate data were downloaded to a computer using the manufacturer’s software (Suunto Team Manager, Suunto, Oy, Finland). The average (±2 sd) heart rate was calculated and only those runs where the average heart rate was within ± 3 beats•min-1 of the target heart rate were used for subsequent analysis for AT stiffness and Erun following RUN. In total, five RUN trials (4 men and 1 woman) had to be repeated 7 days later based on the above exclusion criterion. Average heart rate during RUN was used to estimate approximate run speed, from the heart rate-speed (below sLT) relationship determined during the incremental test. 7.3.1 Determination of AT Stiffness Prior to and immediately following RUN, AT stiffness was determined on the subject’s right leg as described previously (Fletcher, Pfister and MacIntosh. 2013, Fletcher, Esau and Macintosh. 2010). Three isometric ramp maximal voluntary contractions (MVC) of the right plantarflexors were performed, the highest of which was used for subsequent analysis. The first MVC was performed less than 2 minutes following the completion of RUN in all subjects. During the MVC, a 12.5 MHz linear array ultrasound probe (50mm, Philips Envisor, Philips Healthcare, Eindhoven, Netherlands) was used to visualize the medial gastrocnemius muscle (MG) fascicles near the myotendinous junction. This was done to reduce any impact of aponeurosis compliance on the measurement of tendon elongation; however, we acknowledge the measured elongation may represent (some) aponeurosis elongation along with tendon 173 elongation. It should be noted that Arampatzis et al. (2005) demonstrated that measurements of tendon elongation can be made at any point along the aponeurosis without compromise. The probe was secured using a custom-built apparatus. AT tendon elongation was estimated by the displacement of an insertion of a fascicle into the deep aponeurosis as close as possible to the myotendinous junction, measured using ImageJ, (NIH, Baltimore, MD, USA) during the MVC. Measured moments and AT elongations were corrected for joint rotation detected by video analysis and the known passive joint angle-tendon translation relationship (Fletcher, Esau and Macintosh. 2010, Fletcher, Groves, Pfister et al. 2013). AT moment arm length at the 90° ankle angle was estimated using the tendon travel method (Ito, Akima and Fukunaga. 2000). AT force was calculated by dividing the ankle joint moment by the estimated AT moment arm. AT Force (F)-elongation (dL) data were fitted to a quadratic regression equation using: F = AdL2 + BdL 7-1 where A and B are constants. In order to account for any difference in MVC force prior to and following RUN, AT stiffness was defined as the slope of the fitted F-dL equation from 50-100% of maximum isometric plantarflexion force prior to RUN. 7.3.2 AT energy storage/release and muscle energy cost AT energy storage and release, as well as muscle energy cost to allow AT energy storage to occur were estimated according to Fletcher and MacIntosh (2014). Briefly, AT energy storage was calculated as the area under the F-dL curve during running, where F was estimated from the assumed average peak vertical ground reaction forces and running speed (Keller, Weisberger, Ray et al. 1996) and assuming the moment arm of Fz during the stance phase was 1.5x greater than the AT moment arm (Giddings, Beaupre, Whalen et al. 2000). We 174 acknowledge that this length is not fixed during the stance phase and is different between rear and midfoot strikers (Cavanagh and Lafortune. 1980). However, we chose a fixed Fz:AT moment arm ratio since in some cases, a ratio larger than this resulted in an estimated AT force during running which was greater than the maximum isometric force. It seems unlikely that the AT force during running would be near the maximum isometric force, we likely have overestimated the AT force during running. This over-estimation of the AT force would result in both an over-estimation of the AT energy release as well as of the estimated muscle energy cost. The corresponding tendon length change (dL) during running was estimated from the measured tendon stiffness and expected joint rotation. AT energy storage was calculated and AT energy release (2 footstrikes per stride), was estimated assuming an AT hysteresis of 10% (Finni, Peltonen, Stenroth et al. 2012). TS muscle energy cost for AT storage/release to occur was calculated from the estimated MG force and fascicle shortening during two sequential footstrikes (one stride). The energy cost corresponds to the energy associated with cross-bridge cycling, which was estimated from the number of in-parallel crossbridges required to produce the AT force, assuming 3 pN per crossbridge (Finer, Simmons and Spudich. 1994, Rome, Cook, Syme et al. 1999), and the number of cross-bridge cycles required to accommodate the half-sarcomere shortening. This value was multiplied by the estimated number of half-sarcomeres in series and doubled to represent a complete stride. The number of half-sarcomeres in series was estimated by dividing an assumed sarcomere length into the known fascicle length. A linear increase in force as a function of fascicle length, with no evidence of a plateau (Maganaris. 2003) suggests that during maximal contractions, the medial gastrocnemius operates on the ascending limb of the forcelength relationship. Thus, the longest sarcomere length could only be at the short end of the 175 plateau. We assumed this sarcomere length to be 2.6 µm at MVC (Herzog, Read and ter Keurs. 1991). 7.3.3 Measurement of Erun Immediately following the measurement of AT stiffness, the measurement of Erun was performed by having the subjects run at 85% sLT for five minutes on a motorized treadmill (Woodway Pro, Woodway USA, Waukesha, WI). Prior to RUN, a 5 minute warm up at 133 m•min-1 for the females and 160 m•min-1 for the males was performed. No cool-down was performed following RUN. 𝑉𝑂2 was measured throughout the 5 minute run using a metabolic measurement cart (TrueMax 2400, Parvomedics, Salt Lake City, UT). The cart was calibrated before and after each session with a two-point calibration using room air and a gas mixture of known composition (4% CO2, 16% O2) and a manual 3-L syringe. According to the manufacturer, the accuracy of this system is 0.03% and 0.1% for O2 and CO2, respectively and ±2% for volume. Erun was calculated from the steady-state 𝑉𝑂! and respiratory exchange ratio (RER) over the final 2 minutes of the 5 minute stage. Erun was expressed in units of energy (J•kg-1•m-1), as described previously (Fletcher, Pfister and MacIntosh. 2013, Fletcher, Esau and Macintosh. 2010, Shaw, Ingham, Fudge et al. 2013, Shaw, Ingham and Folland. 2014). 7.3.4 Statistics Values are presented as mean ± sd unless otherwise indicated. Two-tailed paired t-tests were used to test for differences between pre and post-run values for Erun, MVC force, AT stiffness, AT energy release and muscle energy cost. One-way ANOVA was used to test for 176 differences in dL across absolute force levels. Linear regression analysis was used to examine the relationship between AT stiffness and Erun, prior to and following the run as well as to examine the relationships between the change in AT stiffness, and the change in Erun, and the change in AT energy release and muscle energy cost following RUN. All analyses were performed using GraphPad Prism version 6.04 for Windows (GraphPad Software, La Jolla, CA, USA, www.graphpad.com). The a priori level of statistical significance was set at alpha <0.05. 7.4 Results Mean heart rate during RUN was 139±9 b•min-1, equivalent to heart rate at 83.6±4.1 % sLT. This corresponds to an approximate run speed of 214.1±13.7 m•min-1. This speed was not different than the anticipated speed associated with 85% sLT (218.2±17.9 m•min-1, p=0.175). The approximate run distance was 19.3±1.2 km. Erun prior to and following RUN is shown in Figure 7.2. Erun increased following RUN in 10 of the 14 subjects. Following RUN, Erun was significantly higher (p=0.044) compared to Erun measured prior to RUN. This represents a mean increase in Erun of 0.06±0.10 J•kg-1•m-1 (1.3%) following RUN. MVC force was reduced by 3.0±5.7% following RUN, from 4489±2013 N to 4333±1917 N. The 95% C.I. for the difference (POST-PRE) was -312 to 1 N. This difference was not significant (p=0.0512). The F-dL relationship prior to and following RUN is shown in Error! Reference source not found. AT stiffness prior to and following RUN are shown in Figure 7.4. AT stiffness was reduced following RUN by 28.5±36.5 N•mm-1. This reduction in AT stiffness was significant (p=0.009). 177 The relationship between the change in Erun and the change in AT stiffness as a result of RUN is shown in Figure 7.5. This relationship was significant (r2=0.430, p=0.011), suggesting that the increase in Erun was associated with a decrease in AT stiffness. Any change in dL and/or force following RUN would result in a change in the amount of AT energy storage/release. Prior to RUN, we estimated this amount of strain energy release to be 21.6±9.0 J•stride-1. Following RUN, AT energy release appeared to be higher (23.5±8.0 J•stride-1), but this increase was not significant (p=0.061, 95% C.I. for the difference = -0.1 to 2.8 J•stride-1). The estimated TS muscle energy cost to allow AT energy storage/release to occur was significantly elevated (p=0.0034) following RUN, from 163.5±61.1 J•stride-1 to 175.2±62.7 J•stride-1. This change in estimated TS muscle energy cost was significantly related to the change in Erun following RUN (r2=0.368, p=0.023), suggesting that nearly 40% of the variance in Erun can be accounted for by variability in muscle energy cost. This relationship is shown in Figure 7.6. 7.5 Discussion The results of the current study demonstrate that a prolonged, submaximal run similar to that regularly performed in training by distance runners can elicit a small but significant increase in Erun and a reduction in AT stiffness. The changes in Erun and AT stiffness as a result of this RUN were significantly related, confirming previous reports that a change in AT stiffness is associated with a change in Erun in highly-trained distance runners (Fletcher, Esau and Macintosh. 2010). Considering the expectation for decreased stiffness in the current paper, combined with our attempt to manipulate AT stiffness via a prolonged run, there is justification for assuming that this is a cause and effect relationship. 178 Changes in Erun following prolonged running have been demonstrated in highly-trained runners previously (Petersen, Hansen, Aagaard et al. 2007). Specifically, it is reported that runners had a 5.2% (Brueckner, Atchou, Capelli et al. 1991) increase in Erun following a marathon run at 273 m•min-1. Here, we observed a smaller change in Erun (1.3%) following a 90 min run at a slower speed (214 m•min-1). It seems logical to consider that the change in Erun should be dependent on speed and distance run. Brueckner et al. (1991) have previously estimated that Erun increases as a function of distance run, by approximately 0.08 %•km-1. Here, we show a similar change of 0.07±0.12 %•km-1. Our slightly smaller observed change in Erun may have been a result of the familiarity to the task, as this is a run typically performed during training by these runners. It cannot be overlooked, however, that the observed change in Erun is small. The magnitude of difference between pre and post-RUN values for Erun (1.3%) is smaller than the typical error in measurement of Erun of highly-trained runners reported in our lab (Fletcher, Esau and MacIntosh. 2009) and others (Shaw, Ingham, Fudge et al. 2013). Furthermore, the smallest worthwhile change (SWC) in Erun in highly-trained runners has been reported to be 2.7% when Erun is expressed in terms of energy (Shaw, Ingham, Fudge et al. 2013). However, data from our laboratory for trained and elite male and female distance runners suggest the SWC in Erun is between 0.8% and 1.1% (Fletcher and MacIntosh. 2014); this SWC is smaller than the magnitude of change we report in Erun following RUN. Although we must be cautious in the interpretation that the changes seen post-RUN are ‘real’ and ‘worthwhile’ and not simply related to testing error and typical variation of Erun, the fact that the difference did reach significance, probably because of the consistency of measurement, is evidence that it is real. 179 It is interesting to consider the context of a progressive increase in Erun and its impact on performance of a record-breaking marathon performance. There was a recent Viewpoint (Joyner, Ruiz and Lucia. 2011) that presented the question: “Who and When” with reference to breaking that record. In the associated Discussion, there was a general consensus that current physiological measures of elite runners are consistent with performance of a 2 hour marathon. However, if there is a progressive increase in Erun in these runners, then clearly a slow-down would be required if a constant energy cost was the requirement for elite performance. Also, the estimate of heat generation did not consider a progressive increase in Erun (Fletcher, Esau, Holash et al. 2011). A similar change in Erun of 0.07% per km over the course of a world-class marathon (eg. 2 hours 5 minutes) would equate to a near 4 minute (3 minute and 41 seconds) increase in race time. It may be more difficult than we think to break the 2 hour barrier. We specifically chose to perform the runs when the outdoor ambient temperature and relative humidity were relatively low in order to prevent any heat-associated increases in Erun. Body temperature increase would rise primarily from metabolic heat generation, for which we estimate 90±10 kJ/kg based on our mean Erun values. This amount of additional heat would necessitate 2.2±0.6 L of sweat in order to lose heat by evaporation alone (for subjects ranging in mass from 45 to 73 kg). Much of this heat loss would also occur via conduction and convection, so this is an overestimate. Our subjects may have had a reduction of body mass over the course of the run so their Erun should have been divided by a smaller body mass. If we assume a weight change of 0.75 kg following the run, then the post-Erun is 2.5% higher than the Erun measured prior to RUN. We also observed a significant decrease in AT stiffness following RUN which was larger than the technical error in AT stiffness we have previously reported (Fletcher, Esau and 180 Macintosh. 2010). The reduced AT stiffness was a result of a greater AT elongation at any given absolute force post-RUN without a significant reduction in MVC force (p=0.0512). Even if this had reached significance, the difference was only 3%. This finding is contrary to previous prolonged running studies which report a significant decline in MVC following prolonged running. These reductions include a 17% reduction following two hours of treadmill running at 75% 𝑉𝑂2 max (Saldanha, Nordlund Ekblom and Thorstensson. 2008) and a 30% reduction in plantarflexor force after a run of 24 hours, with no significant reduction in plantarflexor force after 4-hours of running (Martin, Kerherve, Messonnier et al. 2010). It is likely that two or more mechanisms were responsible for the non-significant reduction in MVC force with our runners. First, contrary to previous reports which measured MVC force following runs of longer duration, our run was of a shorter duration and at a constrained submaximal intensity; the run may not have been of sufficient duration and/or intensity to elicit a significant reduction in MVC force. Therefore, a reduction in force as a consequence of central fatigue mechanisms would not have been expected. Furthermore, as these runners were highly-trained and RUN was typically performed (weekly) as part of their regular training, they may have adapted to perform a run of this duration without fatigue. We also observed a significant increase in dL at all absolute forces following RUN. Given the same AT forces throughout the run, an increased dL is indicative of dynamic creep of the tendon. We estimate, based on the duration of RUN and the average stride length at the RUN speed (Cavanagh and Kram. 1989b), approximately 7500 AT loading cycles during RUN. This should be sufficient to elicit dynamic creep in the AT (De Zee, Bojsen-Moller and Voigt. 2000, Hawkins, Lum, Gaydos et al. 2009) and therefore a significant reduction in AT stiffness postRUN. Previous studies which could not demonstrate a significant reduction in AT stiffness post181 run may not have been of sufficient duration (Farris, Trewartha and McGuigan. 2011) or speed (Peltonen, Cronin, Stenroth et al. 2012) to cause dynamic creep since a minimum threshold may need to be achieved in order to elicit any change in AT mechanical properties (Arampatzis, Karamanidis and Albracht. 2007, Lichtwark, Creswell and Newsham West. 2013). Based on previous studies on dynamic creep of the AT (De Zee, Bojsen-Moller and Voigt. 2000, Hawkins, Lum, Gaydos et al. 2009), the increased dL should occur early on in the run and reach a steady-state after approximately 30-35 minutes (De Zee, Bojsen-Moller and Voigt. 2000). It would appear that when the contractions are of a sufficient magnitude to cause dynamic creep, this occurs early, but when the contractions are only of marginal magnitude, it may require more loadings to impact the stiffness. This may help explain the apparently-contrary observations in AT stiffness following RUN between the current study and previously published work. In the only other study to examine the change in Erun and AT stiffness following a prolonged run, Peltonen et al. (2012) could not show a significant change in AT stiffness despite a 7% increase in Erun (measured as O2 cost) following a marathon run. However, upon reexamination of the data presented in that study, and in removing one potential outlier, a similar significant relationship (r2=0.441, p=0.036) emerges between the change in Erun and the change in AT stiffness. This relationship is similar in magnitude to our current results (shown in Figure 5) as well as those previously reported from our lab in highly-trained runners (r2=0.523, Fletcher, Esau and Macintosh. 2010). In previous studies (Farris, Trewartha and McGuigan. 2011, Lichtwark, Creswell and Newsham West. 2013, Peltonen, Cronin, Stenroth et al. 2012), the RUN may not have been of sufficient speed and/or duration to elicit a significant reduction in AT stiffness. This suggestion has been proposed previously (Lichtwark, Creswell and Newsham West. 2013). 182 A greater dL without a change in AT force would result in a greater storage of AT strain energy during ground contact. However, in order to compensate for the additional dL, and assuming similar kinematics pre and post-RUN (Nicol, Komi and Marconnet. 1991b), an increase in muscle fascicle shortening is necessary. This additional shortening would come at a significant muscle energy cost, which we have estimated following a prolonged run here for the first time based on cross-bridge kinetics and energetics, similar to our previous estimates during steady-state running (Fletcher and MacIntosh. 2014). Our estimates of the storage/release of tendon strain energy revealed a small (3.7 J•stride1 ) but non-significant (95% CI: -0.1 to 2.8 J•stride-1) increase. This lack of significant difference may relate to the subtraction of 10% of the stored energy to estimate energy return. The additional dL resulted in a significant increase in the estimated muscle energy cost of nearly 12 J•stride-1. Therefore, we conclude that the storage and release of tendon strain energy by the AT is relatively less important. Rather, the limited lengthening of the AT during running serves to minimize muscle fascicle shortening. Less shortening should minimize the muscle energy cost (Fletcher, Groves, Pfister et al. 2013, Alexander. 1991, Roberts. 2002). From our correlation analyses, we show the elevated Erun post-RUN was associated with a post-RUN decrease in AT stiffness (Figure 5). This relationship is similar in magnitude and direction to the one we have previously shown following 8 weeks of isometric training in highly-trained runners, further suggesting a change in AT stiffness is associated with a change in Erun (Fletcher, Esau and Macintosh. 2010). We acknowledge, however, that other factors such as changes in kinematics and/or kinetics may also contribute to an elevated Erun (Hunter and Smith. 2007, Candau, Belli, Millet et al. 1998, Nicol, Komi and Marconnet. 1991a). 183 It has recently been demonstrated, in attempts to simulate running muscle mechanics (in frog muscle) that the energy cost of shortening contractions was nearly triple the energy cost of isometric force production (Holt, Roberts and Askew. 2014). These results were obtained in spite of the fact that the force during the shortening was substantially less than the force during the isometric contraction. Considering the need to have similar force production during running, additional motor unit recruitment would be needed in the shortening condition as suggested by the results of Fletcher et al. (2013), resulting in even higher energy cost difference. To further support the relationship between AT stiffness and Erun, we demonstrate here that the change in Erun is also associated with a change in the estimated muscle energy cost. This additional muscle energy cost is a result of the predicted increase in muscle fascicle shortening following RUN. This additional shortening is a consequence of the mechanical fatigue of the AT. 7.6 Conclusion In conclusion, the current results demonstrate dynamic creep of the AT during a prolonged run below the lactate threshold in trained male and female distance runners. The change in AT mechanical properties following RUN appears to have a small but significant effect on muscle energy cost and the associated Erun. Calculations suggest that this effect is greater than the additional tendon strain energy release from the AT. These results further support the notion that a mechanically-optimal AT minimizes Erun in trained distance runners by minimizing muscle fascicle shortening. 184 7.7 Author Contributions All experiments were performed at the Human Performance Lab at the University of Calgary, Calgary, Alberta, Canada. JRF and BRM were responsible for conception and design of the experimental protocol. JRF collected and analyzed the data. JRF and BRM were primarily responsible for interpreting the experimental and theoretical data. JRF drafted the manuscript and JRF and BRM revised it critically for important intellectual content. All authors approved the final draft of the article. 7.8 Acknowledgements and disclosures The authors would like to thank Dr. Chris J. Barclay, PhD for his insights regarding the estimates of muscle energy cost during running and the subjects for their time and dedication in completing the experimental protocol. JRF was supported by NSERC Canada. None of the authors report any conflicting interests. 185 7.9 Tables Table 7-1. Subject characteristics. Age Height Mass VO2max sLT (years) (m) (kg) (ml•kg-1•min-1) (m•min-1) 24.4±5.8 1.72±0.09 61.1±10.0 64.6±5.8 257±21 N 14 Values are mean ± standard deviation 186 7.10 Figures Figure 7.1. Experimental protocol. 187 Figure 7.2. Erun prior to (PRE) and following (POST) RUN. Black lines represent individual subject responses. 188 Figure 7.3 AT force-elongation curve prior to and following RUN. dL is shown at the same relative force level (shown at 20% MVC-PRE force increments). At all relative forces, assessed, dL was significantly higher POST-RUN (p<0.001). 50% MVC-PRE is also shown to demonstrate the change in AT stiffness (slope from 50-100% MVC). 189 Figure 7.4. AT stiffness measured prior to (PRE) and following (POST) RUN. Solid lines represent individual subject responses. AT stiffness was significantly lower (p=0.009) following RUN. 190 Figure 7.5. Relationship between the change in Erun and AT stiffness following RUN. The solid line represents the linear relationship between Erun and stiffness (r2=0.430, p=0.011). Dashed lines represent the 95% confidence interval for the relationship. Note that the relationship crosses the abscissa at 0% change in stiffness. 191 Figure 7.6. Relationship between the change in Erun and TS muscle energy cost following RUN. The solid line represents the linear relationship between Erun and muscle energy cost (r2=0.368, p=0.0213). Dashed lines represent the 95% confidence interval for the relationship. Note that the relationship crosses the abscissa at 0 % change in muscle energy cost. 192 Chapter Eight: Conclusions and Future Directions 8.1 Conclusions The main findings from chapter 3 were that when energy cost of running is normalized to body mass, at similar relative speeds of running, no sex-specific differences in substrate use nor in Erun existed among similarly trained runners. Furthermore, the stiffness of the AT of women is lower than in men, but the relationship between Erun and AT stiffness was not different between the sexes. The results of the chapter 4 confirm previous reports that the EC of muscle contraction is related to the amount and rate of muscle shortening. Furthermore, these results may explain why the EC of running is elevated when Achilles tendon compliance is increased, since a greater amount and rate of shortening are required for force transmission under these conditions. According to the in vivo force–length and force–velocity relationships of skeletal muscle, this shortening and velocity will impact the EC, not simply because of the greater shortening, but because increased muscle activation is required to permit similar force development when shortening velocity is greater. Investigation into consideration for passive forces during the measurement of AT moment arm revealed that the tendon excursion method to estimate AT moment arm offers a quick, affordable alternative to the COR method when estimating MA length; however, it is only when no passive forces are acting to stretch the tendon is the TE method valid, unless this is quantified and accounted for. We have corrected for these passive forces non-invasively, throughout the range of motion. Since we show that MA length does not change as a function of ankle angle, it seems sufficient to measure MA length using the TE method at an ankle angle where passive 193 moments are negligible, or measuring the moment arm length manually. Both methods avoid the considerable time and financial costs associated with magnetic resonance imaging. Furthermore, these data have allowed us to reconsider the shape of the in vivo force-fascicle length relationship as a result of us considering the effect of AT moment arm length change (or lack thereof) and passive forces throughout the ankle range of motion. From our estimates of tendon strain energy storage and release and muscle energy cost for this storage/release to occur, we conclude that the amount of tendon strain energy released represents a very small portion of the total metabolic cost to run a given speed. Furthermore, this energy return comes at a considerable muscle energy cost. Therefore, reducing muscle energy cost through reductions in muscle fascicle shortening during running even if this means less energy return from the tendon, contributes to an improved economy of running. Lastly, measurements of Erun and AT stiffness made immediately prior to and following a prolonged run demonstrated significant mechanical fatigue of the AT in trained male and female distance runners during the run. The change in AT mechanical properties following RUN have a significant impact on the change in Erun and, using methods employed in Chapter 6, the associated muscle energy cost. This additional muscle energy cost following the run was greater than the estimated additional tendon strain energy release from the AT. Taken together, results from this thesis support the notion that a relatively stiff AT minimizes Erun in trained distance runners by reducing muscle fascicle shortening. Further, changes in AT mechanical properties away from the apparently optimal mechanical properties significantly elevate the muscle energy cost and Erun,. This elevated energy cost is primarily associated with an elevated muscle shortening and associated increases in the level of motor unit recruitment to achieve the required force during running. 194 8.2 Future Directions 8.2.1 How quickly does AT stiffness get ‘tuned’ to be optimal? We have proposed here that the AT stiffness is optimally-tuned in order to reduce muscle fascicle shortening and/or shortening velocity. In reality, whether the AT reaches an optimal level during running remains unclear. What is more clear though is that this additional muscle shortening is associated with a higher in vivo rate of energy use and level of activation (Chapter 4) and greater estimated triceps surae muscle energy cost (Chapter 6). Departure from this apparently-optimal AT stiffness further results in an elevated TS muscle energy cost and Erun (Chapter 7). To date, however, very little is known regarding how the AT stiffness is tuned in order to optimize muscle function across a range of walking and running speeds. Lichtwark and Wilson (2008) have proposed that there exists an optimal combination of muscle fascicle length and AT stiffness which reduces the estimated energy cost of contraction. We further know that fascicle length and tendon stiffness differ between individuals of different sporting histories (Arampatzis et al, 2007, Muraoka et al, 2005, Kongsgaard et al, 2005), yet it is unclear whether these optimal combinations defines the sport or the combination of optimal muscle architecture and tendon mechanical properties are a result of long-term training. Therefore, further longitudinal examination of how these properties are adapted to training is required in order to support the hypothesis that muscle and tendon properties are tuned to reduce muscle energy cost. 195 8.2.2 Validation of muscle energetics model Another area of investigation involves validation of the muscle energetics model presented in Chapter 6. This model was developed using previously reported values for similarly trained runners as those in our study. However, very little running kinetics or energetics data are available for elite runners like those assessed in Chapter 6. Thus, future research may focus on the direct measurement of muscle fascicle and tendon length change during running. Combined with kinematic and kinetic analysis of the stance phase, and level of muscle activation, a more reliable and potentially valid model of muscle energetics during running can be described. 8.2.3 Direct measurement of force-length-velocity relationship It has been suggested that variables that alter muscle force production are probably more suitable for explaining variation in Erun (Martin and Morgan, 1992). These variables include the muscle force-length and force-velocity relationships as well as the level of activation of the muscles involved in running. In isolated muscles, the force-length and force-velocity relationships are well-investigated, fundamental properties of skeletal muscle, but little is known about how these relate to in vivo contractile responses and in particular how they relate to Erun. Within this thesis, we have attempted to expand upon this suggestion. Future research then could be concerned with measuring the force-length and force-velocity relationships of the triceps surae, in combination with estimates or direct measurement of fascicle length change during running. In doing so, a greater understanding on the optimal conditions for economical force generation can be considered. Furthermore, results from Chapter 7 confirm that an acute change in AT stiffness as a result of a prolonged run can occur and this affects the estimated muscle energy cost. This 196 mechanical fatigue could result in the muscle operating at a less-than-optimal portion of the force-length and/or force-velocity relationships. This effect has not been investigated in trained runners. The shifts in the force-length-velocity relationships as a result of skeletal muscle fatigue have been well-examined in situ (Rijkelijkhuizen et al, 2005, Butterfield and Herzog, 2005, De Ruiter et al, 1999, Curtin and Edman, 1994, Ameredes et al, 1992, Devrome and MacIntosh, 2007, Jones et al, 2006); however, to date these properties have not been fully examined under in vivo conditions of skeletal muscle fatigue. Maganaris et al. (2002) showed that following a series of just ten ramp contractions up to 80% MVC, MG fascicle length had shortened from 34 mm to 30 mm. The authors contest that this fascicle shortening would correspond to a reduction in the average operating range of individual sarcomeres. If the MG muscle operates on the ascending limb of the force-length relationship (Herzog et al, 1991, Maganaris, 2003), the contraction-induced shortening would shift the average operating length of the sarcomeres further down the ascending limb and away from the optimal myofilament overlap on the nonfatigued, maximally-activated force-length curve, thus reducing the contractile force generated for a given level of activation. The effect of shorter fascicles as a result of repetitive contractions further compounds the force-generating capacity of the muscle. Presumably, these contractions would cause fatigue, resulting in a shift down and to the right of the force-length curve. Thus, the level of activation required to generate the necessary active force at short fascicle lengths becomes even greater. This has important implications as it relates to skeletal muscle function and energetics in vivo. 197 8.2.4 Estimates of role of tendon stiffness in other muscles relevant during running It has previously been shown that in a group of trained distance runners, the most economical runners displayed a higher stiffness of the triceps surae (TS) tendon compared to the less economical runners (Arampatzis et al, 2006, Fletcher et al, 2010). The former study demonstrated the opposite to be true in the vastus lateralis (VL) tendon – that the most economical runners had a lower VL tendon stiffness compared to the less economical runners (Arampatzis et al., 2006). This suggests to us that the roles of these two muscles in minimizing the energy cost during running are different. The reason for these apparently contrary observations with respect to the impact of tendon stiffness on Erun is not obvious, however. The lengthening of a tendon for energy storage is relevant in stretch-shortening cycles where a substantial pre-stretch of the tendon occurs early in a contraction. A compliant tendon allows more energy conversion of either kinetic or gravitational energy to potential energy. This energy can subsequently be released upon shortening so the muscle shortening can be reduced. A compliant tendon may also help by allowing the tendon to lengthen during the stretch phase of the SSC thereby keeping fascicle shortening velocity low. This permits high active force to be generated with relatively little muscle activation. If tendon compliance is optimal, muscle energy cost can be reduced because the fascicles are shortening at the appropriate velocity (Askew and Marsh, 1998, Gabaldon et al, 2008). 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Fletcher, Erik M. Groves, Ted R. Pfister, Brian R. MacIntosh DOI 10.1007/s00421-013-2665-0 Print ISSN 1439-6319 Online ISSN 1439-6327 Journal no. 00421 Your project Requestor: Jared R Fletcher jrfletch@ucalgary.ca University: the University of Calgary, Calgary, AB Canada Purpose: Dissertation/Thesis With reference to your request to reuse material in which Springer Science+Business Media controls the copyright, our permission is granted free of charge under the following conditions: Springer material . represents original material which does not carry references to other sources (if material in question refers with a credit to another source, authorization from that source is required as well); . requires full credit (Springer and the original publisher, book/journal title, chapter/article title, volume, year of publication, page, name(s) of author(s), original copyright notice) to the publication in which the material was originally published by adding: "With kind permission of 238 Springer Science+Business Media"; . may not be altered in any manner. 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Permission free of charge does not prejudice any rights we might have to charge for reproduction of our copyrighted material in the future. Rights and Permissions Springer Science+Business Media 239 Permission for use of Figure 2.5 This is a License Agreement between Jared R Fletcher ("You") and Oxford University Press ("Oxford University Press") provided by Copyright Clearance Center ("CCC"). The license consists of your order details, the terms and conditions provided by Oxford University Press, and the payment terms and conditions. All payments must be made in full to CCC. For payment instructions, please see information listed at the bottom of this form. License Number 3511070082586 License date Nov 16, 2014 Licensed content publisher Oxford University Press Licensed content publication Integrative and Comparative Biology Licensed content title Partitioning the Metabolic Cost of Human Running: A Task-by-Task Approach: Licensed content author Christopher J. Arellano, Rodger Kram Licensed content date 05/16/2014 Type of Use Thesis/Dissertation Institution name University of Calgary Title of your work Changes in tendon compliance and muscle energetics of in vivo human skeletal muscle Publisher of your work n/a Expected publication date Dec 2014 Permissions cost 0.00 USD Value added tax 0.00 USD Total 0.00 USD Total 0.00 USD 240 Permission for use of Figure 2.7 License Number 3511060209246 License date Nov 16, 2014 Licensed content publisher The American Association for the Advancement of Science Licensed content publication Science Licensed content title Built for jumping: the design of the frog muscular system Licensed content author GJ Lutz, LC Rome Licensed content date Jan 21, 1994 Volume number 263 Issue number 5145 Type of Use Thesis / Dissertation Requestor type Scientist/individual at a research institution Format Print and electronic Portion Figure Number of figures/tables 1 Order reference number None Title of your thesis / dissertation Changes in tendon compliance and muscle energetics of in vivo human skeletal muscle Expected completion date Dec 2014 Estimated size (pages) 240 Total 0.00 USD 241