Contrasting Age Effects on Complexity of Tracking Force and Force Fluctuations During Monorhythmic Contraction

in Journal of Aging and Physical Activity

Click name to view affiliation

Yi-Ching Chen
Search for other papers by Yi-Ching Chen in
Current site
Google Scholar
PubMed
Close
,
I-Chen Lin
Search for other papers by I-Chen Lin in
Current site
Google Scholar
PubMed
Close
,
Yen-Ting Lin
Search for other papers by Yen-Ting Lin in
Current site
Google Scholar
PubMed
Close
,
Wei-Min Huang
Search for other papers by Wei-Min Huang in
Current site
Google Scholar
PubMed
Close
,
Chien-Chun Huang
Search for other papers by Chien-Chun Huang in
Current site
Google Scholar
PubMed
Close
, and
Ing-Shiou Hwang
Search for other papers by Ing-Shiou Hwang in
Current site
Google Scholar
PubMed
Close
Restricted access

This study contrasted the stochastic force component between young and older adults, who performed pursuit tracking/compensatory tracking by exerting in-phase/antiphase forces to match a sinusoidal target. Tracking force was decomposed into the force component containing the target frequency and the nontarget force fluctuations (stochastic component). Older adults with inferior task performance had higher complexity (entropy across time; p = .005) in total force. For older adults, task errors were negatively correlated with force fluctuation complexity (pursuit tracking: r = −.527 to −.551; compensatory tracking: r = −.626 to −.750). Notwithstanding an age-related increase in total force complexity (p = .004), older adults exhibited lower complexity of the stochastic force component than young adults did (low frequency: p = .017; high frequency: p = .035). Those older adults with a higher complexity of stochastic force had better task performance due to the underlying use of a richer gradation strategy to compensate for impaired oscillatory control.

Chen is with the Department of Physical Therapy, College of Medical Science and Technology, Chung Shan Medical University, Taichung City, Taiwan; and Physical Therapy Room, Chung Shan Medical University Hospital, Taichung City, Taiwan. I.-C. Lin and Hwang are with the Department of Physical Therapy, College of Medicine, National Cheng Kung University, Tainan City, Taiwan. Y.T. Lin is with Physical Education Office, Asian University, Taichung City, Taiwan. W.-M. Huang is with the Department of Management Information System, National Chung Cheng University, Chiayi City, Taiwan. C.-C. Huang is with Medical Device Innovation Center, National Cheng Kung University, Tainan City, Taiwan. Hwang is also with the Institute of Allied Health Sciences, College of Medicine, National Cheng Kung University, Tainan City, Taiwan.

Hwang (ishwang@mail.ncku.edu.tw) is corresponding author.
  • Collapse
  • Expand
  • Baltes, P.B., & Lindenberger, U. (1997). Emergence of a powerful connection between sensory and cognitive functions across the adult life span: A new window to the study of cognitive aging? Psychology and Aging, 12(1), 1221. PubMed ID: 9100264 doi:10.1037/0882-7974.12.1.12

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bartzokis, G. (2004). Age-related myelin breakdown: A developmental model of cognitive decline and Alzheimer’s disease. Neurobiology of Aging, 25(1), 518. PubMed ID: 14675724 doi:10.1016/j.neurobiolaging.2003.03.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Challis, J.H. (2006). Aging, regularity and variability in maximum isometric moments. Journal of Biomechanics, 39(8), 15431546. PubMed ID: 15946669 doi:10.1016/j.jbiomech.2005.04.008

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, Y.C., Lin, L.L., & Hwang, I.S. (2018). Novel behavioral and neural evidences for age-related changes in force complexity. The Journals of Gerontology. Series A, Biological Sciences & Medical Sciences, 73(8), 9971002. PubMed ID: 29471388 doi:10.1093/gerona/gly025

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, Y.C., Lin, L.L., Lin, Y.T., Hu, C.L., & Hwang, I.S. (2017). Variations in static force control and motor unit behavior with error amplification feedback in the elderly. Frontiers in Human Neuroscience, 11, 538. PubMed ID: 29167637 doi:10.3389/fnhum.2017.00538

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chenxi, L., Chen, Y., Li, Y., Wang, J., & Liu, T. (2016). Complexity analysis of brain activity in attention-deficit/hyperactivity disorder: A multiscale entropy analysis. Brain Research Bulletin, 124, 1220. PubMed ID: 26995277 doi:10.1016/j.brainresbull.2016.03.007

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Costa, M., Goldberger, A.L., & Peng, C.K. (2002). Multiscale entropy analysis of complex physiologic time series. Physical Review Letters, 89(6), 068102. PubMed ID: 12190613 doi:10.1103/PhysRevLett.89.068102

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duarte, M., & Sternad, D. (2008). Complexity of human postural control in young and older adults during prolonged standing. Experimental Brain Research, 191(3), 265276. PubMed ID: 18696056 doi:10.1007/s00221-008-1521-7

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, C.Y., Su, J.H., & Hwang, I.S. (2014). Rate control and quality assurance during rhythmic force tracking. Behavioural Brain Research, 259, 186195. PubMed ID: 24269498 doi:10.1016/j.bbr.2013.11.019

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kelso, J.S. (1997). Dynamic patterns: The self-organization of brain and behavior. Cambridge, MA: MIT Press.

  • Kennedy, D.M., & Christou, E.A. (2011). Greater amount of visual information exacerbates force control in older adults during constant isometric contractions. Experimental Brain Research, 213(4), 351361. PubMed ID: 21800256 doi:10.1007/s00221-011-2777-x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krampe, R.T., Engbert, R., & Kliegl, R. (2001). Age-specific problems in rhythmic timing. Psychology and Aging, 16(1), 1230. PubMed ID: 11302361 doi:10.1037/0882-7974.16.1.12

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kurz, M.J., & Stergiou, N. (2003). The aging human neuromuscular system expresses less certainty for selecting joint kinematics during gait. Neuroscience Letters, 348(3), 155158. PubMed ID: 12932817 doi:10.1016/S0304-3940(03)00736-5

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merchant, H., Pérez, O., Zarco, W., & Gámez, J. (2013). Interval tuning in the primate medial premotor cortex as a general timing mechanism. The Journal of Neuroscience, 33(21), 90829096. PubMed ID: 23699519 doi:10.1523/JNEUROSCI.5513-12.2013

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miall, R.C., Weir, D.J., & Stein, J.F. (1985). Visuomotor tracking with delayed visual feedback. Neuroscience, 16(3), 511520. PubMed ID: 4094689 doi:10.1016/0306-4522(85)90189-7

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miall, R.C., Weir, D.J., & Stein, J.F. (1986). Manual tracking of visual targets by trained monkeys. Behavioural Brain Research, 20, 185201. PubMed ID: 3730133 doi:10.1016/0166-4328(86)90003-3

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, S., & Newell, K.M. (2012). Aging, neuromuscular decline, and the change in physiological and behavioral complexity of upper-limb movement dynamics. Journal of Aging Research, 2012, 114. PubMed ID: 22900179 doi:10.1155/2012/891218

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nemati, S., Edwards, B.A., Lee, J., Pittman-Polletta, B., Butler, J.P., & Malhotra, A. (2013). Respiration and heart rate complexity: Effects of age and gender assessed by band-limited transfer entropy. Respiratory Physiology & Neurobiology, 189(1), 2733. PubMed ID: 23811194 doi:10.1016/j.resp.2013.06.016

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ofori, E., Samson, J.M., & Sosnoff, J.J. (2010). Age-related differences in force variability and visual display. Experimental Brain Research, 203(2), 299306. PubMed ID: 20352199 doi:10.1007/s00221-010-2229-z

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pasalar, S., Roitman, A.V., & Ebner, T.J. (2005). Effects of speeds and force fields on submovements during circular manual tracking in humans. Experimental Brain Research, 163(2), 214225. PubMed ID: 15668793 doi:10.1007/s00221-004-2169-6

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pereira, M., Sobolewski, A., & Millán, J.D.R. (2017). Action monitoring cortical activity coupled to submovements. eNeuro, 4(5), ENEURO.0241-17.2017. doi:10.1523/ENEURO.0241-17.2017

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rey-Robert, B., Temprado, J.J., & Berton, E. (2011). Aging and changes in complexity in the neurobehavioral system. Medicina, 47(1), 110. doi:10.3390/medicina47010001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sleimen-Malkoun, R., Temprado, J.J., & Hong, S.L. (2014). Aging induced loss of complexity and dedifferentiation: Consequences for coordination dynamics within and between brain, muscular and behavioral levels. Frontiers in Aging Neuroscience, 6, 140. PubMed ID: 25018731 doi:10.3389/fnagi.2014.00140

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Slifkin, A.B., Vaillancourt, D.E., & Newell, K.M. (2000). Intermittency in the control of continuous force production. Journal of Neurophysiology, 84(4), 17081718. PubMed ID: 11024063 doi:10.1152/jn.2000.84.4.1708

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sosnoff, J.J., & Newell, K.M. (2008). Age-related loss of adaptability to fast time scales in motor variability. The Journals of Gerontology. Series B, Psychological Sciences & Social Sciences, 63(6), P344P352. doi:10.1093/geronb/63.6.P344

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stirling, L.A., Lipsitz, L.A., Qureshi, M., Kelty-Stephen, D.G., Goldberger, A.L., & Costa, M.D. (2013). Use of a tracing task to assess visuomotor performance: Effects of age, sex, and handedness. The Journals of Gerontology. Series A, Biological Sciences & Medical Sciences, 68(8), 938945. PubMed ID: 23388876 doi:10.1093/gerona/glt003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takahashi, A.C., Porta, A., Melo, R.C., Quitério, R.J., da Silva, E., Borghi-Silva, A., … Catai, A.M. (2012). Aging reduces complexity of heart rate variability assessed by conditional entropy and symbolic analysis. Internal and Emergency Medicine, 7(3), 229235. PubMed ID: 21253879 doi:10.1007/s11739-011-0512-z

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takahashi, T., Cho, R.Y., Murata, T., Mizuno, T., Kikuchi, M., Mizukami, K., … Wada, Y. (2009). Age-related variation in EEG complexity to photic stimulation: A multiscale entropy analysis. Clinical Neurophysiology, 120(3), 476483. PubMed ID: 19231279 doi:10.1016/j.clinph.2008.12.043

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Temprado, J.J., Vieluf, S., & Sleimen-Malkoun, R. (2017). Age-related changes in force control under different task contexts. Experimental Brain Research, 235(1), 231246. doi:10.1007/s00221-016-4787-1

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vaillancourt, D.E., & Newell, K.M. (2002). Changing complexity in human behavior and physiology through aging and disease. Neurobiology of Aging, 23(1), 111. PubMed ID: 11755010 doi:10.1016/S0197-4580(01)00247-0

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vaillancourt, D.E., & Newell, K.M. (2003). Aging and the time and frequency structure of force output variability. Journal of Applied Physiology, 94(3), 903912. doi:10.1152/japplphysiol.00166.2002

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vaillancourt, D.E., Sosnoff, J.J., & Newell, K.M. (2004). Age-related changes in complexity depend on task dynamics. Journal of Applied Physiology, 97(1), 454455. doi:10.1152/japplphysiol.00244.2004

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vanneste, S., Pouthas, V., & Wearden, J.H. (2001). Temporal control of rhythmic performance: A comparison between young and old adults. Experimental Aging Research, 27(1), 83102. PubMed ID: 11205531 doi:10.1080/03610730125798

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Varlet, M., Novembre, G., & Keller, P.E. (2017). Dynamical entrainment of corticospinal excitability during rhythmic movement observation: A transcranial magnetic stimulation study. European Journal of Neuroscience, 45(11), 14651472. PubMed ID: 28394475 doi:10.1111/ejn.13581

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Welford, A.T. (1981). Signal, noise, performance, and age. Human Factors, 23(1), 97109. doi:10.1177/001872088102300109

  • Werkle-Bergner, M., Grandy, T.H., Chicherio, C., Schmiedek, F., Lövdén, M., & Lindenberger, U. (2014). Coordinated within-trial dynamics of low-frequency neural rhythms controls evidence accumulation. Journal of Neuroscience, 34(25), 85198528. doi:10.1523/JNEUROSCI.3801-13.2014

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 2192 557 12
Full Text Views 28 7 0
PDF Downloads 29 9 0