Skip to main content
SpringerLink
Log in

Prediction of 1-year change in knee extension strength by neuromuscular properties in older adults

  • ORIGINAL ARTICLE
  • Published:
GeroScience Aims and scope Submit manuscript

Abstract

Improving muscle strength and preventing muscle weakness are important for older adults. The change in strength can be effectively explained by skeletal muscle mass and neural factors. Neural factors are important for older adults because the variation of neural components is greater in older than in young adults, and any decline in strength cannot solely be explained by a decrease in skeletal muscle mass. The purpose of the present study was to investigate whether skeletal muscle mass or motor unit firing properties could explain the change in muscle strength after 1 year. Thirty-eight older adults (75.0 ± 4.7 years, 156.6 ± 7.7 cm, 55.5 ± 9.4 kg, 26 women) performed maximum voluntary knee extension and their skeletal muscle mass was measured using a bioimpedance device. During a submaximal contraction task, high-density surface electromyography was recorded and the signals were decomposed into individual motor unit firing. As an index of motor unit firing properties, the slope and y-intercept (MU intercept) were calculated from the regression line between recruitment thresholds and firing rates in each participant. After 1 year, their maximum knee extension torque was evaluated again. A stepwise multiple regression linear model with sex and age as covariates indicated that MU intercept was a significant explanation with a negative association for the 1-year change in muscle strength (β =  − 0.493, p = 0.004), but not skeletal muscle mass (p = 0.364). The results suggest that neural components might be predictors of increasing and decreasing muscle strength rather than skeletal muscle mass.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data Availability

Data will be provided by the corresponding author upon request.

References

  1. Vanden Noven ML, et al. Motor variability during sustained contractions increases with cognitive demand in older adults. Front Aging Neurosci. 2014;6:97.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Degens H, Korhonen MT. Factors contributing to the variability in muscle ageing. Maturitas. 2012;73(3):197–201.

    Article  PubMed  Google Scholar 

  3. Hunter SK, Pereira HM, Keenan KG. The aging neuromuscular system and motor performance. J Appl Physiol. 2016;121(4):982–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Piasecki M, et al. Age-related neuromuscular changes affecting human vastus lateralis. J Physiol. 2016;594(16):4525–36.

    Article  CAS  PubMed  Google Scholar 

  5. Yoshiko A, et al. Applicability of the seated step test for assessing thigh muscle sarcopenia in older individuals. Exp Gerontol, 2023;112283.

  6. Chen LK, et al. Sarcopenia in Asia: consensus report of the Asian Working Group for Sarcopenia. J Am Med Dir Assoc. 2014;15(2):95–101.

    Article  PubMed  Google Scholar 

  7. Cruz-Jentoft AJ, et al. Sarcopenia: European consensus on definition and diagnosis: report of the European Working Group on Sarcopenia in Older People. Age Ageing. 2010;39(4):412–23.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Wilkinson DJ, Piasecki M, Atherton PJ. The age-related loss of skeletal muscle mass and function: measurement and physiology of muscle fibre atrophy and muscle fibre loss in humans. Ageing Res Rev. 2018;47:123–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Piasecki M, et al. Age-dependent motor unit remodelling in human limb muscles. Biogerontology. 2016;17(3):485–96.

    Article  PubMed  Google Scholar 

  10. Manini TM, Hong SL, Clark BC. Aging and muscle: a neuron’s perspective. Curr Opin Clin Nutr Metab Care. 2013;16(1):21–6.

    Article  CAS  PubMed  Google Scholar 

  11. Hirono T, et al. Motor unit firing patterns in older adults with low skeletal muscle mass. Arch Gerontol Geriatr. 2023;116:105151.

  12. Duchateau J, Hainaut K. Effects of immobilization on contractile properties, recruitment and firing rates of human motor units. J Physiol. 1990;422:55–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Inns TB, et al. Motor unit dysregulation following 15 days of unilateral lower limb immobilisation. J Physiol. 2022;600(21):4753–69.

    Article  CAS  PubMed  Google Scholar 

  14. Sirago G, et al. Loss of neuromuscular junction integrity and muscle atrophy in skeletal muscle disuse. Ageing Res Rev. 2023;83:101810.

    Article  CAS  PubMed  Google Scholar 

  15. Piasecki M, et al. Failure to expand the motor unit size to compensate for declining motor unit numbers distinguishes sarcopenic from non-sarcopenic older men. J Physiol. 2018;596(9):1627–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hirono T, et al. Effects of home-based bodyweight squat training on neuromuscular properties in community dwelling older adults. Aging Clin Exp Res. 2023;35(5):1043–53.

    Article  PubMed  Google Scholar 

  17. Kamen G, Knight CA. Training-related adaptations in motor unit discharge rate in young and older adults. J Gerontol A Biol Sci Med Sci. 2004;59(12):1334–8.

    Article  PubMed  Google Scholar 

  18. Moritani T, deVries HA. Potential for gross muscle hypertrophy in older men. J Gerontol. 1980;35(5):672–82.

    Article  CAS  PubMed  Google Scholar 

  19. Pearcey GEP, et al. Chronic resistance training: is it time to rethink the time course of neural contributions to strength gain? Eur J Appl Physiol. 2021;121(9):2413–22.

    Article  CAS  PubMed  Google Scholar 

  20. Duchateau J, Enoka RM. Human motor unit recordings: origins and insight into the integrated motor system. Brain Res. 2011;1409:42–61.

    Article  CAS  PubMed  Google Scholar 

  21. Holobar A, Zazula D. On the selection of the cost function for gradient-based decomposition of surface electromyograms. Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:4668–71.

    CAS  PubMed  Google Scholar 

  22. Holobar A, Zazula D. Correlation-based decomposition of surface electromyograms at low contraction forces. Med Biol Eng Comput. 2004;42(4):487–95.

    Article  CAS  PubMed  Google Scholar 

  23. Feeney DF, Mani D, Enoka RM. Variability in common synaptic input to motor neurons modulates both force steadiness and pegboard time in young and older adults. J Physiol. 2018;596(16):3793–806.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Peduzzi P, et al. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol. 1995;48(12):1503–10.

    Article  CAS  PubMed  Google Scholar 

  25. Holobar A, et al. Estimating motor unit discharge patterns from high-density surface electromyogram. Clin Neurophysiol. 2009;120(3):551–62.

    Article  PubMed  Google Scholar 

  26. Farina D, et al. Decoding the neural drive to muscles from the surface electromyogram. Clin Neurophysiol. 2010;121(10):1616–23.

    Article  PubMed  Google Scholar 

  27. Adam A, De Luca CJ. Firing rates of motor units in human vastus lateralis muscle during fatiguing isometric contractions. J Appl Physiol. 2005;99(1):268–80.

    Article  PubMed  Google Scholar 

  28. Fuglevand AJ, Winter DA, Patla AE. Models of recruitment and rate coding organization in motor-unit pools. J Neurophysiol. 1993;70(6):2470–88.

    Article  CAS  PubMed  Google Scholar 

  29. Hirono T, et al. Acute changes in motor unit discharge property after concentric versus eccentric contraction exercise in knee extensor. J Electromyogr Kinesiol. 2022;67:102704.

    Article  PubMed  Google Scholar 

  30. Okudaira M, et al. Longitudinal development of muscle strength and relationship with motor unit activity and muscle morphological characteristics in youth athletes. Exp Brain Res. 2023;241(4):1009–19.

    Article  PubMed  Google Scholar 

  31. Watanabe K, et al. Neuromuscular characteristics of front and back legs in junior fencers. Exp Brain Res. 2022;240(7–8):2085–96.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Clark LA, et al. Reduced neural excitability and activation contribute to clinically meaningful weakness in older adults. J Gerontol A Biol Sci Med Sci. 2021;76(4):692–702.

    Article  PubMed  Google Scholar 

  33. Clark BC, Manini TM. What is dynapenia? Nutrition. 2012;28(5):495–503.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Manini TM, Clark BC. Dynapenia and aging: an update. J Gerontol A Biol Sci Med Sci. 2012;67(1):28–40.

    Article  PubMed  Google Scholar 

  35. Fukumoto Y, et al. Skeletal muscle quality assessed from echo intensity is associated with muscle strength of middle-aged and elderly persons. Eur J Appl Physiol. 2012;112(4):1519–25.

    Article  PubMed  Google Scholar 

  36. Taniguchi M, et al. Increase in echo intensity and extracellular-to-intracellular water ratio is independently associated with muscle weakness in elderly women. Eur J Appl Physiol. 2017;117(10):2001–7.

    Article  PubMed  Google Scholar 

  37. Hughes VA, et al. Longitudinal muscle strength changes in older adults: influence of muscle mass, physical activity, and health. J Gerontol A Biol Sci Med Sci. 2001;56(5):B209–17.

    Article  CAS  PubMed  Google Scholar 

  38. Metter EJ, et al. Muscle quality and age: cross-sectional and longitudinal comparisons. J Gerontol A Biol Sci Med Sci. 1999;54(5):B207–18.

    Article  CAS  PubMed  Google Scholar 

  39. Martinez-Valdes E, et al. Tracking motor units longitudinally across experimental sessions with high-density surface electromyography. J Physiol. 2017;595(5):1479–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Del Vecchio A, et al. The increase in muscle force after 4 weeks of strength training is mediated by adaptations in motor unit recruitment and rate coding. J Physiol. 2019;597(7):1873–87.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Pucci AR, Griffin L, Cafarelli E. Maximal motor unit firing rates during isometric resistance training in men. Exp Physiol. 2006;91(1):171–8.

    Article  CAS  PubMed  Google Scholar 

  42. Rutherford OM, Jones DA. The role of learning and coordination in strength training. Eur J Appl Physiol Occup Physiol. 1986;55(1):100–5.

    Article  CAS  PubMed  Google Scholar 

  43. Semmler JG, Nordstrom MA. Motor unit discharge and force tremor in skill- and strength-trained individuals. Exp Brain Res. 1998;119(1):27–38.

    Article  CAS  PubMed  Google Scholar 

  44. Herda TJ. Resistance exercise training and the motor unit. Eur J Appl Physiol. 2022;122(9):2019–35.

    Article  PubMed  Google Scholar 

  45. Fukumoto Y, et al. Association of physical activity with age-related changes in muscle echo intensity in older adults: a 4-year longitudinal study. J Appl Physiol. 2018;125(5):1468–74.

    Article  CAS  PubMed  Google Scholar 

  46. Daly RM, et al. Association between changes in habitual physical activity and changes in bone density, muscle strength, and functional performance in elderly men and women. J Am Geriatr Soc. 2008;56(12):2252–60.

    Article  PubMed  Google Scholar 

  47. Greig CA, Botella J, Young A. The quadriceps strength of healthy elderly people remeasured after eight years. Muscle Nerve. 1993;16(1):6–10.

    Article  CAS  PubMed  Google Scholar 

  48. Van Vossel K, et al. Can muscle typology explain the inter-individual variability in resistance training adaptations? J Physiol. 2023;601(12):2307–27.

    Article  PubMed  Google Scholar 

  49. Piotrkiewicz M, et al. Age-related change in duration of afterhyperpolarization of human motoneurones. J Physiol. 2007;585(Pt 2):483–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Dalton BH, et al. Differential age-related changes in motor unit properties between elbow flexors and extensors. Acta Physiol (Oxf). 2010;200(1):45–55.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank Dr. Shun Kunugi, Dr. Masamichi Okudaira, Ms. Saeko Ueda, Dr. Akito Yoshiko, Ms. Yoko Kawakami, and lab. members for helping with data collection. We also appreciate Prof. Aleš Holobar of the University of Maribor, Slovenia, for supporting the analyses of motor unit firing properties using the DEMUSE tool.

Funding

This study was financially supported by a Grant-in-Aid from the Japan Society for the Promotion of Science Fellows (21J00674 and 22KJ2973).

Author information

Authors and Affiliations

  1. Laboratory of Neuromuscular Biomechanics, School of Health and Sport Sciences, Chukyo University, 101 Tokodachi, Kaizu-Cho, Toyota, Aichi, Japan

    Tetsuya Hirono, Ryosuke Takeda, Taichi Nishikawa & Kohei Watanabe

  2. Human Health Sciences, Graduate School of Medicine, Kyoto University, 53 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, Japan

    Tetsuya Hirono

  3. Graduate School of Health and Sport Sciences, Chukyo University, 101 Tokodachi, Kaizu-Cho, Toyota, Aichi, Japan

    Taichi Nishikawa

Authors
  1. Tetsuya Hirono
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ryosuke Takeda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Taichi Nishikawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kohei Watanabe
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tetsuya Hirono.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hirono, T., Takeda, R., Nishikawa, T. et al. Prediction of 1-year change in knee extension strength by neuromuscular properties in older adults. GeroScience 46, 2561–2569 (2024). https://doi.org/10.1007/s11357-023-01035-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11357-023-01035-6

Keywords

Search

Navigation