Abstract
Purpose
The aim of this study was to investigate the fiber dynamics of plantarflexor and dorsiflexor muscles and their association with the net metabolic rate (NCw).
Methods
Metabolic, kinematic, kinetic, and electromyography measurements were made on seven young subjects while they walked on a force-plate instrumented treadmill at 1.00, 1.20, 1.40, 1.60, and 1.8 m/s for 1:30 min. The net metabolic rate was computed, and a one degree-of freedom EMG-driven approach was used to extract the force generation ability (Fability), and active force–length (fAL) and force–velocity (fV) multiplier of each muscle. A one-way (speeds) repeated measures ANOVA was performed for each muscle and a multiple linear regression model was used to explain NCw.
Results
Fability was significantly affected by gait speed for the GasMed and the SOL muscles. The decrease of Fability for the SOL and the GasMed was accompanied by a decrease in the force–velocity multiplier. The peak muscle force for the SOL increased for the lowest speed compared to the higher speed, and for the TibAnt increased at high speed compared to low speed. In addition, Fability fAL, and fV of the SOL predicted over 58% of NCw and FMax of the TibAnt accounts for 39.9% of the variance in NCw.
Conclusion
The increase of NCw with gait speed over the preferred walking speed can be partially explained by the decreasing capacity of the SOL muscle to produce muscle force and more specifically by the force–velocity relationship and an increase in muscle force for the TibAnt.
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Abbreviations
- CoW:
-
Metabolic cost of walking
- EMGs:
-
Surface electromyography
- F ability :
-
Force generation ability of a muscle
- f AL :
-
The active force–length multiplier
- f v :
-
The force–velocity multiplier
- GasLat:
-
Gastrocnemius lateralis
- GasMed:
-
Gastrocnemius medialis
- NC w :
-
Net metabolic rate
- SOL:
-
Soleus
- TibAnt:
-
Tibialis anterior
References
Anderson FC, Pandy MG (2003) Individual muscle contributions to support in normal walking. Gait Posture 17:159–169. https://doi.org/10.1016/S0966-6362(02)00073-5
Arnold EM, Hamner SR, Seth A et al (2013) How muscle fiber lengths and velocities affect muscle force generation as humans walk and run at different speeds. J Exp Biol 216:2150–2160. https://doi.org/10.1242/jeb.075697
Baxter JR, Hast MW (2019) Plantarflexor metabolics are sensitive to resting ankle angle and optimal fiber length in computational simulations of gait. Gait Posture 67:194–200. https://doi.org/10.1016/J.GAITPOST.2018.10.014
Besier TF, Sturnieks DL, Alderson JA, Lloyd DG (2003) Repeatability of gait data using a functional hip joint centre and a mean helical knee axis. J Biomech 36:1159–1168. https://doi.org/10.1016/S0021-9290(03)00087-3
Bhargava LJ, Pandy MG, Anderson FC (2004) A phenomenological model for estimating metabolic energy consumption in muscle contraction. J Biomech 37:81–88. https://doi.org/10.1016/S0021-9290(03)00239-2
Bohannon RW (1997) Comfortable and maximum walking speed of adults aged 20–79 years: reference values and determinants. Age Ageing 26:15–19. https://doi.org/10.1093/ageing/26.1.15
Buchanan TS, Lloyd DG, Manal K, Besier TF (2004) Neuromusculoskeletal modeling: estimation of muscle forces and joint moments and movements from measurements of neural command. J Appl Biomech 20:367–395. https://doi.org/10.1123/jab.20.4.367
Das Gupta S, Bobbert MF, Kistemaker DA (2019) The metabolic cost of walking in healthy young and older adults—a systematic review and meta analysis. Sci Rep 9:1–10. https://doi.org/10.1038/s41598-019-45602-4
Delp SL, Anderson FC, Arnold AS et al (2007) OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans Biomed Eng 54:1940–1950. https://doi.org/10.1109/TBME.2007.901024
Detrembleur C, Dierick F, Stoquart G et al (2003) Energy cost, mechanical work, and efficiency of hemiparetic walking. Gait Posture 18:47–55. https://doi.org/10.1016/S0966-6362(02)00193-5
Deusinger RH (1978) Biomechanics and energetics of muscular exercise, 1st edn. Oxford University
di Prampero PE, Atchou G, Brückner JC, Moia C (1986) The energetics of endurance running. Eur J Appl Physiol Occup Physiol 55:259–266. https://doi.org/10.1007/BF02343797
Donelan JM, Kram R, Kuo AD (2001) Mechanical and metabolic determinants of the preferred step width in human walking. Proc R Soc B Biol Sci 268:1985–1992. https://doi.org/10.1098/rspb.2001.1761
Farris DJ, Sawicki GS (2012) The mechanics and energetics of human walking and running: a joint level perspective. J R Soc Interface 9:110–118. https://doi.org/10.1098/rsif.2011.0182
Gerus P, Rao G, Berton E (2011) A method to characterize in vivo tendon force-strain relationship by combining ultrasonography, motion capture and loading rates. J Biomech 44:2333–2336. https://doi.org/10.1016/j.jbiomech.2011.05.021
Gerus P, Rao G, Berton E (2012) Subject-specific tendon-aponeurosis definition in hill-type model predicts higher muscle forces in dynamic tasks. PLoS ONE. https://doi.org/10.1371/journal.pone.0044406
Gerus P, Rao G, Berton E (2013) Ultrasound-based subject-specific parameters improve fascicle behaviour estimation in Hill-type muscle model. Comput Methods Biomech Biomed Engin 5842:37–41. https://doi.org/10.1080/10255842.2013.780047
Guidetti L, Meucci M, Bolletta F et al (2018) Validity, reliability and minimum detectable change of COSMED K5 portable gas exchange system in breath-by-breath mode. PLoS ONE 13:1–12. https://doi.org/10.1371/journal.pone.0209925
Homsher E, Kean CJ (1978) Skeletal muscle energetics and metabolism. Annu Rev Physiol 40:93–131
Homsher E, Mommaerts WFHM, Ricchiuti NV, Wallner A (1972) Activation heat, activation metabolism and tension-related heat in frog semitendinosus muscles. J Physiol 220:601–625. https://doi.org/10.1113/jphysiol.1972.sp009725
Hermens HJ, Freriks B, Disselhorst-Klug C, Rau G (2000) Development of recommendations for SEMG sensors and sensor placement procedures. J Electromyogr Kinesiol 10:361–374. https://doi.org/10.1016/S1050-6411(00)00027-4
Himann JE, Cunningham DA, Rechnitzer PA, Paterson DH (1988) Age-related changes in speed of walking. Med Sci Sports Exerc 20:161–166. https://doi.org/10.1249/00005768-198820020-00010
Kalron A, Menascu S, Frid L et al (2020) Physical activity in mild multiple sclerosis: contribution of perceived fatigue, energy cost, and speed of walking. Disabil Rehabil 42:1240–1246. https://doi.org/10.1080/09638288.2018.1519603
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680. https://doi.org/10.1126/science.220.4598.671
Koelewijn AD, Dorschky E, van den Bogert AJ (2018) A metabolic energy expenditure model with a continuous first derivative and its application to predictive simulations of gait. Comput Methods Biomech Biomed Engin 21:521–531. https://doi.org/10.1080/10255842.2018.1490954
Lai A, Lichtwark GA, Schache AG et al (2015) In vivo behavior of the human soleus muscle with increasing walking and running speeds. J Appl Physiol 118:1266–1275. https://doi.org/10.1152/japplphysiol.00128.2015
Lichtwark GA, Wilson AM (2008) Optimal muscle fascicle length and tendon stiffness for maximising gastrocnemius efficiency during human walking and running. J Theor Biol 252:662–673. https://doi.org/10.1016/j.jtbi.2008.01.018
Liu MQ, Anderson FC, Pandy MG, Delp SL (2006) Muscles that support the body also modulate forward progression during walking. J Biomech 39:2623–2630. https://doi.org/10.1016/j.jbiomech.2005.08.017
Lloyd DG, Besier TF (2003) An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. J Biomech 36:765–776. https://doi.org/10.1016/S0021-9290(03)00010-1
Manal K, Buchanan TS (2003) A one-parameter neural activation to muscle activation model: estimating isometric joint moments from electromyograms. J Biomech 36:1197–1202. https://doi.org/10.1016/S0021-9290(03)00152-0
Mian OS, Thom JM, Ardigò LP et al (2006) Metabolic cost, mechanical work, and efficiency during walking in young and older men. Acta Physiol 186:127–139. https://doi.org/10.1111/j.1748-1716.2006.01522.x
Mukaka MM (2012) Statistics corner: a guide to appropriate use of correlation coefficient in medical research. Malawi Med J 24:69–71
Neptune RR, Sasaki K (2005) Ankle plantar flexor force production is an important determinant of the preferred walk-to-run transition speed. J Exp Biol 208:799–808. https://doi.org/10.1242/jeb.01435
Neptune RR, Kautz SA, Zajac FE (2001) Contributions of the individual ankle plantar flexors to support, forward progression and swing initiation during walking. J Biomech 34:1387–1398. https://doi.org/10.1016/S0021-9290(01)00105-1
Reid KF, Fielding RA (2012) Skeletal muscle power: a critical determinant of physical functioning in older adults. Exerc Sport Sci Rev 40:4–12. https://doi.org/10.1097/JES.0b013e31823b5f13
Thomas SA, Vega D, Arellano CJ (2021) Do humans exploit the metabolic and mechanical benefits of arm swing across slow to fast walking speeds? J Biomech. https://doi.org/10.1016/j.jbiomech.2020.110181
Umberger BR (2010) Stance and swing phase costs in human walking. J R Soc Interface 7:1329–1340. https://doi.org/10.1098/rsif.2010.0084
Umberger BR, Gerritsen KGM, Martin PE (2003) A model of human muscle energy expenditure. Comput Methods Biomech Biomed Engin 6:99–111. https://doi.org/10.1080/1025584031000091678
Winby CR, Lloyd DG, Kirk TB (2008) Evaluation of different analytical methods for subject-specific scaling of musculotendon parameters. J Biomech 41:1682–1688. https://doi.org/10.1016/j.jbiomech.2008.03.008
Workman JM, Armstrong BW (1986) Metabolic cost of walking: equation and model. J Appl Physiol 61:1369–1374. https://doi.org/10.1152/jappl.1986.61.4.1369
Zajac F (1989) Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. Crit Rev Biomed Eng 17(4):359–411
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PG and EP conceived and designed the study. PG and EP conducted the experiments. PG and EP analyzed the data. All authors interpreted the results of study. PG drafted the manuscript and prepared figures/tables. All authors edited and revised the manuscript. All authors approved the final version of the manuscript.
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Communicated by Toshio Moritani.
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Gerus, P., Piche, E., Guérin, O. et al. How fiber dynamics of plantarflexor and dorsiflexor muscles based on EMG-driven approach can explain the metabolic cost at different gait speeds. Eur J Appl Physiol 122, 745–755 (2022). https://doi.org/10.1007/s00421-021-04881-4
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DOI: https://doi.org/10.1007/s00421-021-04881-4