Tensiomyography: from muscle assessment to talent identification tool

Dražen Čular, Matej Babić, Damir Zubac, Ana Kezić, Iva Macan, Leonardo Alexandre Peyré-Tartaruga, Francesco Ceccarini, Johnny Padulo, Dražen Čular, Matej Babić, Damir Zubac, Ana Kezić, Iva Macan, Leonardo Alexandre Peyré-Tartaruga, Francesco Ceccarini, Johnny Padulo

Abstract

Introduction: Tensiomyography (TMG) is a non-invasive and cost-effective tool that is gaining popularity in fields such as sports science, physical therapy, and medicine. In this narrative review, we examine the different applications of TMG and its strengths and limitations, including its use as a tool for sport talent identification and development. Methods: In the course of crafting this narrative review, an exhaustive literature search was carried out. Our exploration spanned several renowned scientific databases, such as PubMed, Scopus, Web of Science, and ResearchGate. The materials we sourced for our review included a broad spectrum of both experimental and non-experimental articles, all focusing on TMG. The experimental articles featured varied research designs including randomized controlled trials, quasi-experiments, as well as pre-post studies. As for the non-experimental articles, they encompassed a mix of case-control, cross-sectional, and cohort studies. Importantly, all articles included in our review were written in English and had been published in peer-reviewed journals. The assortment of studies considered provided a holistic view of the existing body of knowledge on TMG, and formed the basis of our comprehensive narrative review. Results: A total of 34 studies were included in the review, organized into three sections: 1) assessing muscle contractile properties of young athletes, 2) using TMG in the talent identification and development process and 3) Future research and perspectives. According to data presented here, the most consistent TMG parameters for determining muscle contractile properties are radial muscle belly displacement, contraction time, and delay time. Biopsy findings from the vastus lateralis (VL) confirmed TMG as a valid tool for estimating the ratio of myosin heavy chain (%MHC-I). Conclusion: TMGs ability to estimate the ratio of %MHC-I has the potential to aid in the selection of athletes with the muscle characteristics best suited for a particular sport, eliminating the need for more invasive procedures. However, more research is warranted to fully understand TMG's potential and its reliability when used with young athletes. Importantly, the use of TMG technology in this process can positively impact health status, reducing the frequency and severity of injuries and the duration of recovery, and subsequently can reduce drop out rates among youth athletes. Future studies should look at twin youth athletes, as a model capable of discriminating between the influence of hereditary factors vs. environmental factors, in therms of muscle contractility and TMG's potential for instance.

Keywords: MHC ratio; muscle assessment; muscle fiber composition; non-invasive method; talent identification.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2023 Čular, Babić, Zubac, Kezić, Macan, Peyré-Tartaruga, Ceccarini and Padulo.

Figures

FIGURE 1
FIGURE 1
(A) Illustration of the tensiomyography (TMG) equipment. (B) Example of a classic wave of twitch response with all its parameters.

References

    1. Alentorn-Geli E., Alvarez-Diaz P., Ramon S., Marin M., Steinbacher G., Boffa J. J., et al. (2015). Assessment of neuromuscular risk factors for anterior cruciate ligament injury through tensiomyography in male soccer players. Knee Surg. Sports Traumatol. Arthrosc. 23 (9), 2508–2513. 10.1007/s00167-014-3018-1
    1. Allen S. V., Vandenbogaerde T. J., Hopkins W. G. (2014). Career performance trajectories of olympic swimmers: Benchmarks for talent development. Eur. J. Sport Sci. 14 (7), 643–651. 10.1080/17461391.2014.893020
    1. Babić M., Čular D., Zubac D. (2023). Hereditary capabilities of lower-limb contractile properties. A Co-twin study; case of youth track and field champions. Med. Sport. (in press). 10.23736/S0025-7826.23.04270-9
    1. Breitbach S., Tug S., Simon P. (2014). Conventional and genetic talent identification in sports: Will recent developments trace talent? Sports Med. 44 (11), 1489–1503. 10.1007/s40279-014-0221-7
    1. Brown J. (2002). Sports talent: How to identify and develop outstanding athletes. Champaign, IL: Human Kinetics.
    1. Bryner R. W., Ullrich I., Sauers J., Donley D., Hornsby G., Kolar M., et al. (1999). Effects of resistance vs. aerobic training combined with an 800 calorie liquid diet on lean body mass and resting metabolic rate. J. Am. Coll. Nutr. 18 (2), 115–121. 10.1080/07315724.1999.10718838
    1. Dahmane R., Djordjevic S., Simunic B., Valencic V. (2005). Spatial fiber type distribution in normal human muscle: Histochemical and tensiomyographical evaluation. J. Biomech. 38 (12), 2451–2459. 10.1016/j.jbiomech.2004.10.020
    1. Dahmane R., Valencic V., Knez N., Erzen I. (2001). Evaluation of the ability to make non-invasive estimation of muscle contractile properties on the basis of the muscle belly response. Med. Biol. Eng. Comput. 39 (1), 51–55. 10.1007/BF02345266
    1. Fry A. C. (2004). The role of resistance exercise intensity on muscle fibre adaptations. Sports Med. 34 (10), 663–679. 10.2165/00007256-200434100-00004
    1. Garcia-Mayor R. V., Andrade M. A., Rios M., Lage M., Dieguez C., Casanueva F. F. (1997). Serum leptin levels in normal children: Relationship to age, gender, body mass index, pituitary-gonadal hormones, and pubertal stage. J. Clin. Endocrinol. Metab. 82, 2849–2855. 10.1210/jcem.82.9.4235
    1. Gillen Z. M., Shoemaker M. E., Cramer J. T. (2022). Electromyographic and mechanomyographic responses during isokinetic leg extensions in children versus adolescents. J. Sci. Sport Med. 1-10. 10.1007/s42978-022-00193-x
    1. Höner O., Votteler A., Schmid M., Schultz F., Roth K. (2015). Psychometric properties of the motor diagnostics in the German football talent identification and development programme. J. Sports Sci. 33 (2), 145–159. 10.1080/02640414.2014.928416
    1. Koz D., Fraser-Thomas J., Baker J. (2012). Accuracy of professional sports drafts in predicting career potential. Scand. J. Med. Sci. Sports. 22 (4), 64–69. 10.1111/j.1600-0838.2011.01408.x
    1. Kraemer W. J., Marchitelli L., Gordon S. E., Harman E., Dziados J. E., Mello R., et al. (1990). Hormonal and growth factor responses to heavy resistance exercise protocols. J. Appl. Physiol. 69 (4), 1442–1450. 10.1152/jappl.1990.69.4.1442
    1. Loturco I., Pereira L. A., Kobal R., Kitamura K., Ramírez-Campillo R., Zanetti V., et al. (2016). Muscle contraction velocity: A suitable approach to analyze the functional adaptations in elite soccer players. J. Sports Sci. Med. 15 (3), 483–491.
    1. MacDougall J. D., Tuxen D., Sale D. G., Moroz J. R., Sutton J. R. (1998). Arterial blood pressure response to heavy resistance exercise. J. Appl. Physiol. 84 (4), 785–790. 10.1152/jappl.1985.58.3.785
    1. McDermott G., Burnett A. F., Robertson S. J. (2015). Reliability and validity of the loughborough soccer passing test in adolescent males: Implications for talent identification. Int. J. Sports Sci. Coach. 10 (2–3), 515–527. 10.1260/1747-9541.10.2-3.515
    1. Mostafavi M., Nikseresht F., Resch J. E., Barnes L., Boukhechba M. (2021). Collision prediction and prevention in contact sports using RFID tags and haptic feedback. Springer Int. Publ., 400–406. 10.48550/arXiv.2102.03453
    1. Padulo J., Buglione A., Larion A., Esposito F., Doria C., Čular D., et al. (2023). Energy cost differences between marathon runners and soccer players: Constant versus shuttle running. Front. Physiol. 14, 1159228. 10.3389/fphys.2023.1159228
    1. Peyré-Tartaruga L. A., Coertjens M. (2018). Locomotion as a powerful model to study integrative Physiology: Efficiency, economy, and power relationship. Front. Physiol. 11 (9), 1789. 10.3389/fphys.2018.01789
    1. Pišot R., Narici M. V., Šimunič B., De Boer M., Seynnes O., Jurdana M., et al. (2008). Whole muscle contractile parameters and thickness loss during 35-day bed rest. Eur. J. Appl. Physiol. 104 (2), 409–414. 10.1007/s00421-008-0698-6
    1. Pregelj S., Šimunič B. (2018). Effects of 8-week electrical muscle stimulation on the muscle contractile properties in adolescent girls. Ann. Kines. 9 (2), 105–120. 10.35469/ak.2018.172
    1. Sánchez-Sánchez J., Bishop D., García-Unanue J., Ubago-Guisado E., Hernando E., López-Fernández J., et al. (2018). Effect of a repeated sprint ability test on the muscle contractile properties in elite futsal players. Sci. Rep. 8 (1), 17284. 10.1038/s41598-018-35345-z
    1. Šimunič B., Degens H., Rittweger J., Narici M., Mekjavić I., Pišot R. (2011). Noninvasive estimation of myosin heavy chain composition in human skeletal muscle. Med. Sci. Sports Exerc. 43 (9), 1619–1625. 10.1249/MSS.0b013e31821522d0
    1. Šimunič B., Degens H., Završnik J., Koren K., Volmut T., Pišot R. (2017). Tensiomyographic assessment of muscle contractile properties in 9-to 14-year old children. Int. J. Sports Med. 38 (9), 659–665. 10.1055/s-0043-110679
    1. Šimunič B., Koren K., Rittweger J., Lazzer S., Reggiani C., Rejc E., et al. (2019). Tensiomyography detects early hallmarks of bed-rest-induced atrophy before changes in muscle architecture. J. Appl. Physiol. 126 (4), 815–822. 10.1152/japplphysiol.00880.2018
    1. Šimunič B., Pišot R., Rittweger J., Degens H. (2018). Age-related slowing of contractile properties differs between power, endurance, and nonathletes: A tensiomyographic assessment. J. Gerontol. A. Biol. Sci. Med. Sci. 73 (12), 1602–1608. 10.1093/gerona/gly069
    1. Sözen H., Cè E., Bisconti A. V., Rampichini S., Longo S., Coratella G., et al. (2019). Differences in electromechanical delay components induced by sex, age and physical activity level: New insights from a combined electromyographic, mechanomyographic and force approach. Sport Sci. Health. 15, 623–633. 10.1007/s11332-019-00563-z
    1. Till K., Baker J. (2020). Challenges and [possible] solutions to optimizing talent identification and development in sport. Front. Psychol. 15 (11), 664. 10.3389/fpsyg.2020.00664
    1. Vaeyens R., Malina R. M., Janssens M., Van Renterghem B., Bourgois J., Vrijens J., et al. (2006). A multidisciplinary selection model for youth soccer: The ghent youth soccer project. Br. J. Sports Med. 40 (11), 928–934. 10.1136/bjsm.2006.029652
    1. Wiewelhove T., Raeder C., de Paula Símola R. A., Schneider C., Döweling A., Ferrauti A. (2017). Tensiomyographic markers are not sensitive for monitoring muscle fatigue in elite youth athletes: A pilot study. Front. Physiol. 8, 406. 10.3389/fphys.2017.00406
    1. Yoshimura A., Kunugi S., Hirono T., Nojima H., Ueda S., Holobar A., et al. (2022). Association of muscle strength with muscle thickness and motor unit firing pattern of vastus lateralis muscle in youth athletes. Int. J. Sports Physiol. 17 (12), 1725–1731. 10.1123/ijspp.2022-0094
    1. Završnik J., Pišot R., Šimunič B., Kokol P., Blažun Vošner H. (2017). Biomechanical characteristics of skeletal muscles and associations between running speed and contraction time in 8-to 13-year-old children. Int. J. Med. Res. 45 (1), 231–245. 10.1177/0300060516687212
    1. Zubac D., Paravlić A., Koren K., Felicita U., Šimunič B. (2019). Plyometric exercise improves jumping performance and skeletal muscle contractile properties in seniors. J. Musculoskelet. Neuronal Interact. 19 (1), 38–49.
    1. Zubac D., Šimunič B. (2017). Skeletal muscle contraction time and tone decrease after 8 weeks of plyometric training. J. Strength Cond. Res. 31 (6), 1610–1619. 10.1519/JSC.0000000000001626

Source: PubMed

Patrocinadores e Colaboradores

Condições médicas

Intervenções de drogas

3
Se inscrever