The effect of surface electromyography biofeedback on the activity of extensor and dorsiflexor muscles in elderly adults: a randomized trial

Ana Belén Gámez, Juan José Hernandez Morante, José Luis Martínez Gil, Francisco Esparza, Carlos Manuel Martínez, Ana Belén Gámez, Juan José Hernandez Morante, José Luis Martínez Gil, Francisco Esparza, Carlos Manuel Martínez

Abstract

Surface electromyography-biofeedback (sEMG-B) is a technique employed for the rehabilitation of patients with neurological pathologies, such as stroke-derived hemiplegia; however, little is known about its effectiveness in the rehabilitation of the extension and flexion of several muscular groups in elderly patients after a stroke. Therefore, this research was focused on determining the effectiveness of sEMG-B in the muscles responsible for the extension of the hand and the dorsiflexion of the foot in post-stroke elderly subjects. Forty subjects with stroke-derived hemiplegia were randomly divided into intervention or control groups. The intervention consisted of 12 sEMG-B sessions. The control group underwent 12 weeks (24 sessions) of conventional physiotherapy. Muscle activity test and functionality (Barthel index) were determined. Attending to the results obtained, the intervention group showed a higher increase in the average EMG activity of the extensor muscle of the hand and in the dorsal flexion of the foot than the control group (p < 0.001 in both cases), which was associated with an increase in the patients' Barthel index score (p = 0.006); In addition, Fugl-Meyer test revealed higher effectiveness in the lower limb (p = 0.007). Thus, the sEMG-B seems to be more effective than conventional physiotherapy, and the use of this technology may be essential for improving muscular disorders in elderly patients with physical disabilities resulting from a stroke.

Trial registration: ClinicalTrials.gov NCT03838809.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Flow diagram of the trial.
Figure 2
Figure 2
Box-plots with individual activity showing changes in average EMG activity in the hemiparetic and normal extremities. Muscular activity was expressed as the % of the maximum voluntary isometric contraction (%MVIC). Statistically significance values were determined through the ANCOVA analysis. Precise data and statistical significance values are available in Supplementary Table S2.
Figure 3
Figure 3
Treatment effect differences between the upper and lower limbs in both Control and sEMG-B groups. Differences were analysed by ANCOVA analysis.
Figure 4
Figure 4
Changes in Barthel index for daily living activity test and the muscle strength functionality tests after 12 weeks of treatment. Forest plot shows estimated treatment differences (ETDs)/odds ratios and 95% CIs. Data are from the full analysis set (completers subjects of control and sEMG-B groups). Data at baseline are mean ± s.d. Improvement/worsening refer to the statistically significant changes from baseline with sEMG-B intervention relative to the control group. Precise data and statistical significance values are available in Supplementary Information Table S3.

References

    1. Béjot Y, Bailly H, Durier J, Giroud M. Epidemiology of stroke in Europe and trends for the 21st century. Presse Med. 2016;45:e391–e398. doi: 10.1016/j.lpm.2016.10.003.
    1. Benjamin, E. J. et al. Heart Disease and Stroke Statistics—2018 Update: A Report From the American Heart Association. Circulation137 (2018).
    1. Centers for Disease Control and Prevention (CDC) Prevalence of disabilities and associated health conditions among adults–United States, 1999. MMWR. Morb. Mortal. Wkly. Rep. 2001;50:120–5.
    1. Duncan PW, et al. Management of Adult Stroke Rehabilitation Care. Stroke. 2005;36:e100–43. doi: 10.1161/01.STR.0000180861.54180.FF.
    1. Veerbeek JM, et al. What is the evidence for physical therapy poststroke? A systematic review and meta-analysis. PLoS One. 2014;9:e87987. doi: 10.1371/journal.pone.0087987.
    1. Howlett OA, Lannin NA, Ada L, McKinstry C. Functional Electrical Stimulation Improves Activity After Stroke: A Systematic Review With Meta-Analysis. Arch. Phys. Med. Rehabil. 2015;96:934–943. doi: 10.1016/j.apmr.2015.01.013.
    1. Glanz M, Klawansky S, Chalmers T. Biofeedback therapy in stroke rehabilitation: a review. J. R. Soc. Med. 1997;90:33–9. doi: 10.1177/014107689709000110.
    1. Dimyan MA, Cohen LG. Neuroplasticity in the context of motor rehabilitation after stroke. Nat. Rev. Neurol. 2011;7:76–85. doi: 10.1038/nrneurol.2010.200.
    1. Mang CS, Campbell KL, Ross CJD, Boyd LA. Promoting neuroplasticity for motor rehabilitation after stroke: considering the effects of aerobic exercise and genetic variation on brain-derived neurotrophic factor. Phys. Ther. 2013;93:1707–16. doi: 10.2522/ptj.20130053.
    1. Mrachacz-Kersting N, et al. The effect of type of afferent feedback timed with motor imagery on the induction of cortical plasticity. Brain Res. 2017;1674:91–100. doi: 10.1016/j.brainres.2017.08.025.
    1. Merletti, R., Parker, P. A. & Parker, P. J. Electromyography: physiology, engineering, and non-invasive applications. 11 (John Wiley & Sons, 2004).
    1. Li Y, Chen X, Zhang X, Zhou P. Several practical issues toward implementing myoelectric pattern recognition for stroke rehabilitation. Med. Eng. Phys. 2014;36:754–760. doi: 10.1016/j.medengphy.2014.01.005.
    1. Neblett R. Surface Electromyographic (SEMG) Biofeedback for Chronic Low Back Pain. Healthcare. 2016;4:27. doi: 10.3390/healthcare4020027.
    1. Giggins OM, Persson U, Caulfield B. Biofeedback in rehabilitation. J. Neuroeng. Rehabil. 2013;10:60. doi: 10.1186/1743-0003-10-60.
    1. Kim J-H. The effects of training using EMG biofeedback on stroke patients upper extremity functions. J. Phys. Ther. Sci. 2017;29:1085–1088. doi: 10.1589/jpts.29.1085.
    1. Sturma A, Hruby LA, Prahm C, Mayer JA, Aszmann OC. Rehabilitation of Upper Extremity Nerve Injuries Using Surface EMG Biofeedback: Protocols for Clinical Application. Front. Neurosci. 2018;12:1–11. doi: 10.3389/fnins.2018.00906.
    1. Zadnia A, Kobravi HR, Sheikh M, Hosseini HA. Generating the visual biofeedback signals applicable to reduction of wrist spasticity: A pilot study on stroke patients. Basic Clin. Neurosci. 2018;9:15–26. doi: 10.29252/nirp.bcn.9.1.15.
    1. Davis AE, Lee RG. EMG biofeedback in patients with motor disorders: an aid for co-ordinating activity in antagonistic muscle groups. Can. J. Neurol. Sci. 1980;7:199–206. doi: 10.1017/S0317167100023209.
    1. Rayegani SM, et al. Effect of Neurofeedback and Electromyographic-Biofeedback Therapy on Improving Hand Function in Stroke Patients. Top. Stroke Rehabil. 2014;21:137–151. doi: 10.1310/tsr2102-137.
    1. Park YK, Kim JH. Effects of kinetic chain exercise using EMG-biofeedback on balance and lower extremity muscle activation in stroke patients. J. Phys. Ther. Sci. 2017;29:1390–1393. doi: 10.1589/jpts.29.1390.
    1. Chen H-X, Wang W, Xiao H-Q, Wang H, Ding X-D. Ultrasound-guided botulinum toxin injections and EMG biofeedback therapy the lower limb muscle spasm after cerebral infarction. Eur. Rev. Med. Pharmacol. Sci. 2015;19:1696–9.
    1. Stanton R, Ada L, Dean CM, Preston E. Biofeedback improves performance in lower limb activities more than usual therapy in people following stroke: a systematic review. J. Physiother. 2017;63:11–16. doi: 10.1016/j.jphys.2016.11.006.
    1. Bradley L, et al. Electromyographic biofeedback for gait training after stroke. Clin. Rehabil. 1998;12:11–22. doi: 10.1191/026921598677671932.
    1. Zucchella, C. et al. Rehabilitation in oldest-old stroke patients: a comparison within over 65 population. Eur. J. Phys. Rehabil. Med., 10.23736/S1973-9087.18.05297-8 (2018).
    1. Mamede S, Schmidt HG. The twin traps of overtreatment and therapeutic nihilism in clinical practice. Med. Educ. 2014;48:34–43. doi: 10.1111/medu.12264.
    1. Doğan-Aslan M, Nakipoğlu-Yüzer GF, Doğan A, Karabay I, Özgirgin N. The effect of electromyographic biofeedback treatment in improving upper extremity functioning of patients with hemiplegic stroke. J. Stroke Cerebrovasc. Dis. 2012;21:187–92. doi: 10.1016/j.jstrokecerebrovasdis.2010.06.006.
    1. Moreland JD, Thomson MA, Fuoco AR. Electromyographic biofeedback to improve lower extremity function after stroke: a meta-analysis. Arch. Phys. Med. Rehabil. 1998;79:134–40. doi: 10.1016/S0003-9993(98)90289-1.
    1. Song R, Tong KY. EMG and kinematic analysis of sensorimotor control for patients after stroke using cyclic voluntary movement with visual feedback. J. Neuroeng. Rehabil. 2013;10:18. doi: 10.1186/1743-0003-10-18.
    1. S. DD, et al. Assessment of biofeedback rehabilitation in post-stroke patients combining fMRI and gait analysis: A case study. J. Neuroeng. Rehabil. 2014;11:53. doi: 10.1186/1743-0003-11-53.
    1. Schulz KF, Altman DG, Moher DM. for the C. G. CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials. BMJ. 2010;340:698–702. doi: 10.1136/bmj.c332.
    1. Mayr S, Buchner A, Erdfelder E, Faul F. A short tutorial of GPower. Tutor. Quant. Methods Psychol. 2007;3:51–59. doi: 10.20982/tqmp.03.2.p051.
    1. Bolgla LA, Uhl TL. Reliability of electromyographic normalization methods for evaluating the hip musculature. J. Electromyogr. Kinesiol. 2007;17:102–11. doi: 10.1016/j.jelekin.2005.11.007.
    1. Singer B, Garcia-Vega J. The Fugl-Meyer Upper Extremity Scale. J. Physiother. 2017;63:53. doi: 10.1016/j.jphys.2016.08.010.
    1. See J, et al. A standardized approach to the Fugl-Meyer assessment and its implications for clinical trials. Neurorehabil. Neural Repair. 2013;27:732–741. doi: 10.1177/1545968313491000.
    1. Hislop, H. J., Avers, D., Brown, M. & Daniels, L. Daniels and Worthingham’s muscle testing: techniques of manual examination and performance testing.
    1. Caruso W, Leisman G. A Force/Displacement Analysis of Muscle Testing. Percept. Mot. Skills. 2000;91:683–692. doi: 10.2466/pms.2000.91.2.683.
    1. Tiffreau V, et al. Postrehabilitation Functional Improvements in Patients With Inflammatory Myopathies: The Results of a Randomized Controlled Trial. Arch. Phys. Med. Rehabil. 2017;98:227–234. doi: 10.1016/j.apmr.2016.09.125.
    1. González N, et al. Psychometric characteristics of the Spanish version of the Barthel Index. Aging Clin. Exp. Res. 2018;30:489–497. doi: 10.1007/s40520-017-0809-5.

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