Effects of Visual Feedback on Walking Speed for Stroke Patients: Single-case Design
Efecto de la retroalimentación visual sobre la velocidad de la marcha después de un accidente cerebrovascular: diseño de caso único
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Introduction. Gait recovery is one of the main goals in post-stroke rehabilitation. Based on the principles of motor learning, new strategies have been developed in neurorehabilitation based on repetitive, task-oriented practice, and feedback. The latter has proven to be one of the most critical variables for training, because it is easy to obtain and manipulate. However, there are still no conclusive studies to identify the real effect of this variable and its influence on recovery and functional gait performance.
Objective. To determine the effect of visual feedback on gait speed after stroke in adults with subacute and chronic stages.
Methodology. Single-case, multiple baseline, non-concurrent randomized, and four-participant design. Gait velocity was assessed by determining differences in level, trend, data stability, and nonoverlapping data using visual analysis based on technical documentation for single-case designs from the What Works Clearinghouse.
Results. Four participants ranging in age from 19 to 73 years were included in the study. The change in level for all participants demonstrated an increase in gait velocity values after the introduction of the intervention (mean: 0.76 m/s). Visual trend analysis estimated acceleration for the intervention line for three participants. The data in the baseline and intervention phase met the stability criterion measured with the two standard deviation band method (mean: 0.05 m/s); patterns of change demonstrated immediate effect with gradual improvement during the intervention for participants 1, 3, and 4. The percentage of nonoverlapping data showed effectiveness of the intervention for three of the participants (PND >91.67%).
Conclusions. The findings presented in this study represent a scientific contribution that supports the relevance of the use and application of motor learning principles for the development of new strategies in motor rehabilitation. However, this study constitutes a first step towards more robust studies that include replication of the phases in the study and follow-up evaluation to determine the permanence of long-term effects.
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- Feigin VL, Norrving B, Mensah GA. Global Burden of Stroke. Circ Res. 2017;120:439–48. doi: https://doi.org/10.1161/CIRCRESAHA.116.308413
- Chamarro-lusar A, Medina-casanovas J. Walking speed as a predictor of community mobility and quality of life after stroke. Top Stroke Rehabil. 2019;26:349–58. doi: https://doi.org/10.1080/10749357.2019.1605751
- Saini V, Guada L, Yavagal DR. Global Epidemiology of Stroke and Access to Acute Ischemic Stroke Interventions. Neurology. 2021;97:S6–16. doi: https://doi.org/10.1212/WNL.0000000000012781
- Lui SK, Nguyen MH. Elderly Stroke Rehabilitation: Overcoming the Complications and Its Associated Challenges. Curr Gerontol Geriatr Res. 2018;2018:1–9. doi: https://doi.org/10.1155/2018/9853837
- Roelker SA, Bowden MG, Kautz SA, Neptune RR. Paretic propulsion as a measure of walking performance and functional motor recovery post-stroke: A review. Gait Posture. 2019;68:6–14. doi: https://doi.org/10.1016/j.gaitpost.2018.10.027
- Beyaert C, Vasa R, Frykberg GE. Gait post-stroke: Pathophysiology and rehabilitation strategies. Neurophysiologie Clinique/Clinical Neurophysiology. 2015;45:335–55. doi: https://doi.org/10.1016/j.neucli.2015.09.005
- Wonsetler EC, Bowden MG. A systematic review of mechanisms of gait speed change post-stroke. Part 2: exercise capacity, muscle activation, kinetics, and kinematics. Top Stroke Rehabil. 2017;24:394–403. doi: https://doi.org/10.1080/10749357.2017.1282413
- Selves C, Stoquart G, Lejeune T. Gait rehabilitation after stroke: review of the evidence of predictors, clinical outcomes and timing for interventions. Acta Neurol Belg. 2020;120:783–90. doi: https://doi.org/10.1007/s13760-020-01320-7
- Schröder J, Truijen S, Criekinge T, Saeys W. Feasibility and effectiveness of repetitive gait training early after stroke: A systematic review and meta-analysis. J Rehabil Med. 2019;51:78–88. doi: https://doi.org/10.2340/16501977-2505
- Esquenazi A, Lee S, Wikoff A, Packel A, Toczylowski T, Feeley J. A Comparison of Locomotor Therapy Interventions: Partial-Body Weight−Supported Treadmill, Lokomat, and G-EO Training in People With Traumatic Brain Injury. PM&R. 2017;9:839–46. doi: https://doi.org/10.1016/j.pmrj.2016.12.010
- Hornby TG, Reisman DS, Ward IG, Scheets PL, Miller A, Haddad D, et al. Clinical Practice Guideline to Improve Locomotor Function Following Chronic Stroke, Incomplete Spinal Cord Injury, and Brain Injury. Journal of Neurologic Physical Therapy. 2020;44:49–100. doi: https://doi.org/10.1097/NPT.0000000000000303
- Rendos NK, Zajac-Cox L, Thomas R, Sato S, Eicholtz S, Kesar TM. Verbal feedback enhances motor learning during post-stroke gait retraining. Top Stroke Rehabil. 2021;28:362–77. doi: https://doi.org/10.1080/10749357.2020.1818480
- Mendes FA dos S, Pompeu JE, Lobo AM, da Silva KG, Oliveira T de P, Zomignani AP, et al. Motor learning, retention and transfer after virtual-reality-based training in Parkinson’s disease - effect of motor and cognitive demands of games: A longitudinal, controlled clinical study. Physiotherapy (United Kingdom). 2012;98:217–23. doi: https://doi.org/10.1016/j.physio.2012.06.001
- Pignolo L, Basta G, Carozzo S, Bilotta M, Todaro MR, Serra S, et al. A body-weight-supported visual feedback system for gait recovering in stroke patients: A randomized controlled study. Gait Posture. 2020;82:287–93. doi: https://doi.org/10.1016/j.gaitpost.2020.09.020
- Hasegawa N, Takeda K, Sakuma M, Mani H, Maejima H. Gait & Posture Learning effects of dynamic postural control by auditory biofeedback versus visual biofeedback training. Gait Posture. 2017;58:188–93. doi: https://doi.org/10.1016/j.gaitpost.2017.08.001
- Walker ER, Hyngstrom AS, Schmit BD. Influence of visual feedback on dynamic balance control in chronic stroke survivors. J Biomech. 2016;49:698–703. doi: https://doi.org/10.1016/j.jbiomech.2016.01.028
- Shin J, Chung Y. Influence of visual feedback and rhythmic auditory cue on walking of chronic stroke patient induced by treadmill walking in real-time basis. NeuroRehabilitation. 2017;41:445–52. doi: https://doi.org/10.3233/NRE-162139.
- Druzbicki M, Przysada G, Guzik A, Brzozowska-Magoń A, Kołodziej K, Wolan-Nieroda A, et al. The efficacy of gait training using a body weight support treadmill and visual biofeedback in patients with subacute stroke: A randomized controlled trial. Biomed Res Int. 2018. doi: https://doi.org/10.1155/2018/3812602
- Proulx CE, Louis Jean MT, Higgins J, Gagnon DH, Dancause N. Somesthetic, Visual, and Auditory Feedback and Their Interactions Applied to Upper Limb Neurorehabilitation Technology: A Narrative Review to Facilitate Contextualization of Knowledge. Frontiers in Rehabilitation Sciences. 2022;3. doi: https://doi.org/10.3389/fresc.2022.789479
- Kim J-S, Oh D-W. Use of real-time visual feedback during overground walking training on gait symmetry and velocity in patients with post-stroke hemiparesis: randomized controlled, single-blind study. International Journal of Rehabilitation Research. 2020;43:247–54. doi: https://doi.org/10.1097/MRR.0000000000000419
- van Kammen K, Boonstra AM, van der Woude LH v., Visscher C, Reinders-Messelink HA, den Otter R. Lokomat guided gait in hemiparetic stroke patients: the effects of training parameters on muscle activity and temporal symmetry. Disabil Rehabil. 2020;42:2977–85. doi: https://doi.org/10.1080/09638288.2019.1579259
- Lobo MA, Moeyaert M, Baraldi Cunha A, Babik I. Single-Case Design, Analysis, and Quality Assessment for Intervention Research. Journal of Neurologic Physical Therapy 2017. https://doi.org/10.1097/NPT.0000000000000187
- Cheng DK, Nelson M, Brooks D, Salbach NM. Validation of stroke-specific protocols for the 10-meter walk test and 6-minute walk test conducted using 15-meter and 30-meter walkways. Top Stroke Rehabil. 2020;27:251–61. doi: https://doi.org/10.1080/10749357.2019.1691815
- Dalgas U, Severinsen K, Overgaard K. Relations Between 6 Minute Walking Distance and 10 Meter Walking Speed in Patients With Multiple Sclerosis and Stroke. Arch Phys Med Rehabil. 2012;93:1167–72. doi: https://doi.org/10.1016/j.apmr.2012.02.026
- Tamburella F, Moreno JC, Sofía D, Valenzuela H, Pisotta I, Iosa M, et al. Influences of the biofeedback content on robotic post-stroke gait rehabilitation: electromyographic vs joint torque biofeedback. J NeuroEngineering Rehabil. 2019;16:95. doi: https://doi.org/10.1186/s12984-019-0558-0
- Kratochwill, T.R; Hitchcock J. Single-case design technical documentation. 2010. Disponible en: https://files.eric.ed.gov/fulltext/ED510743.pdf
- Tate RL, Perdices M, Rosenkoetter U, Shadish W, Vohra S, Barlow DH, et al. The Single-Case Reporting Guideline In BEhavioural Interventions (SCRIBE) 2016 Statement. Phys Ther. 2016;96:e1–10. doi: https://doi.org/10.2522/ptj.2016.96.7.e1
- Lane JD, Gast DL. Visual analysis in single case experimental design studies: Brief review and guidelines. Neuropsychol Rehabil. 2014;24:445–63. doi: https://doi.org/10.1080/09602011.2013.815636
- Arnout Tilgenkamp. Theil–Sen estimator: Robust regression for slope estimation between 1 dimensional X and y. Version 1.0 [software]. Disponible en: https://www.mathworks.com/matlabcentral/fileexchange/34308-theil-sen-estimator?s_tid=FX_rc2_behav
- Bulté I, Onghena P. The Single-Case Data Analysis Package: Analysing Single-Case Experiments with R Software. Journal of Modern Applied Statistical Methods. 2013;12:450–78. doi: https://doi.org/10.22237/jmasm/1383280020
- Krasny-Pacini A, Evans J. Single-case experimental designs to assess intervention effectiveness in rehabilitation: A practical guide. Ann Phys Rehabil Med. 2018;61:164–79. doi: https://doi.org/10.1016/j.rehab.2017.12.002
- Gast DL. Single Subject Research Methodology in Behavioral Sciences. 1st ed. Georgia: Routledge; 2010.
- Pak NW, Lee JH. Effects of visual feedback training and visual targets on muscle activation, balancing, and walking ability in adults after hemiplegic stroke: A preliminary, randomized, controlled study. International Journal of Rehabilitation Research. 2020:76–81. doi: https://doi.org/10.1097/MRR.0000000000000376
- Genthe K, Schenck C, Eicholtz S, Zajac-cox L, Kesar TM, Rehabilitation N, et al. Effects of real-time gait biofeedback on paretic propulsion and gait biomechanics in individuals post-stroke. Top Stroke Rehabil. 2019;25:186–93. doi: https://doi.org/10.1080/10749357.2018.1436384.Effects
- Kim J, Oh D. Use of real-time visual feedback during overground walking training on gait symmetry and velocity in patients with post- stroke hemiparesis: randomized controlled, single-blind study. International Journal of Rehabilitation Research. 2020;43(3):247–54. doi: https://doi.org/10.1097/MRR.0000000000000419
- Lewek MD, Feasel J, Wentz E, Brooks FP, Whitton MC. Use of Visual and Proprioceptive Feedback to Improve Gait Speed and Spatiotemporal Symmetry Following Chronic Stroke: A Case Series. Phys Ther. 2012;92:748–56. doi: https://doi.org/10.2522/ptj.20110206