Impact of Accompaniment Music in App-Assisted Music-Based Therapy on Home Compliance and Movement Performance in Stroke: A Pilot Study
Main Article Content
Abstract
Background: Technology-assisted home programs offer a promising approach to expanding access to rehabilitation, but may be limited by low patient compliance. Music-based therapy has been shown to enhance motivation and support holistic stroke recovery. Integrating music into technology-assisted home programs may improve adherence and amplify therapeutic benefits. To better understand how technology-assisted music-based therapy supports stroke recovery, it is important to identify features that promote high compliance and positive outcomes.
Aim: This pilot study examined the impact of accompaniment music on home rehabilitation compliance and motor performance during app-assisted music-based therapy in stroke survivors.
Methods: Six community-dwelling adults with chronic stroke completed a 2-week, within-subject study using KeyStroke, a stroke-specific music-based therapy app designed for self-directed upper extremity home exercise. Participants practiced piano-based song exercises under two alternating conditions: Music Mode (with accompaniment music) and Non-Music Mode (without accompaniment music). Two outcomes were analyzed: (1) total engagement time, measuring app usage as a proxy for home rehabilitation compliance, and (2) mean absolute delta time, assessing timing accuracy as an indicator of motor performance. Data were further categorized by the hand-use requirements of each song: more-affected hand only, less-affected hand only, and bimanual practice.
Results: Participants used the app for an average of ~30 minutes per day, 4-5 days per week over two weeks (4.51 ± 3.67 total hours), with an average total of ~14,000 keypresses. Total engagement times were higher in Music Mode than in Non-Music Mode across categories, with a particularly consistent result across participants for songs targeting the more-affected hand. Mean absolute delta times were also consistently better in Music Mode across all song categories. Notably, the training elicited a high volume of repetitions (~1,555 keypresses within 30 minutes), exceeding typical levels seen in outpatient clinics and approaching thresholds believed to promote neuroplasticity.
Conclusion: App-assisted music-based therapy with accompaniment music may support higher training compliance and improve motor performance in stroke rehabilitation. Background music may engage feedforward motor planning and enrich the rehabilitation experience. Although findings are preliminary due to the small, heterogeneous sample, they offer promising direction for future studies and app refinements targeting upper extremity recovery post-stroke.
Article Details
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
2. Martin SS, Aday AW, Almarzooq ZI, et al. 2024 heart disease and stroke statistics: A report of US and global data from the American Heart Association. Circulation. 2024;149(8):e347-e913. doi:10.1161/cir.0000000000001209
3. Skolarus LE, Meurer WJ, Burke JF, Prvu Bettger J, Lisabeth LD. Effect of insurance status on postacute care among working age stroke survivors. Neurology. 2012;78(20):1590-5. doi:10.1212/WNL.0b013e3182563bf5
4. Medford-Davis LN, Fonarow GC, Bhatt DL, et al. Impact of insurance status on outcomes and use of rehabilitation services in acute ischemic stroke: Findings from get with the guidelines-stroke. J Am Heart Assoc. 2016;5(11) doi:10.1161/jaha.116.004282
5. Lohse KR, Lang CE, Boyd LA. Is more better? Using metadata to explore dose–response relationships in stroke rehabilitation. Stroke. 2014;45(7):2053-2058. doi:10.1161/STROKEAHA.114.004695
6. Winstein C, Kim B, Kim S, Martinez C, Schweighofer N. Dosage Matters. Stroke. Jul 2019;50(7):1831-1837. doi:10.1161/strokeaha.118.023603
7. Lang CE, Macdonald JR, Reisman DS, et al. Observation of amounts of movement practice provided during stroke rehabilitation. Arch Phys Med Rehabil. Oct 2009;90(10):1692-8. doi:10.1016/j.apmr.2009.04.005
8. Young BM, Holman EA, Cramer SC. Rehabilitation therapy doses are low after stroke and predicted by clinical factors. Stroke. 2023; 54(3):831-839. doi:10.1161/strokeaha.122.041098
9. Mayo NE. Stroke rehabilitation at home. Stroke. 2016;47(6):1685-1691. doi:10.1161/STROKEAHA.116.011309
10. Chen Y, Abel KT, Janecek JT, Chen Y, Zheng K, Cramer SC. Home-based technologies for stroke rehabilitation: A systematic review. Int J Med Inform. 2019;123:11-22. doi:10.1016/j.ijmedinf.2018.12.001
11. Niyomyart A, Ruksakulpiwat S, Benjasirisan C, et al. Current status of barriers to mHealth access among patients with stroke and steps toward the digital health era: Systematic Review. JMIR Mhealth Uhealth. 2024;12:e54511. doi:10.2196/54511
12. Cramer SC, Young BM, Schwarz A, Chang TY, Su M. Telerehabilitation following stroke. Phys Med Rehabil Clin N Am. 2024;35(2):305-318. doi:10.1016/j.pmr.2023.06.005
13. Carraturo G, Pando-Naude V, Costa M, Vuust P, Bonetti L, Brattico E. The major-minor mode dichotomy in music perception. Phys Life Rev. 2025;52:80-106. doi:10.1016/j.plrev.2024.11.017
14. Dimitriadis T, Della Porta D, Perschl J, Evers AWM, Magee WL, Schaefer RS. Motivation and music interventions in adults: A systematic review. Neuropsychol Rehabil. Jun 2024;34(5):649-678. doi:10.1080/09602011.2023.2224033
15. Vigl J, Ojell-Järventausta M, Sipola H, Saarikallio S. Melody for the Mind: Enhancing Mood, Motivation, Concentration, and Learning through Music Listening in the Classroom. Music & Science. 2023;6: 20592043231214085. doi:10.1177/20592043231214085
16. Wu Q, Chen T, Wang Z, et al. Effectiveness of Music Therapy on Improving Treatment Motivation and Emotion in Female Patients with Methamphetamine Use Disorder: A Randomized Controlled Trial. Substance Abuse. 2020;41(4): 493-500. doi:10.1080/08897077.2019.1675117
17. Hankinson K, Shaykevich A, Vallence AM, Rodger J, Rosenberg M, Etherton-Beer C. A Tailored Music-Motor Therapy and Real-Time Biofeedback Mobile Phone App ('GotRhythm') to Promote Rehabilitation Following Stroke: A Pilot Study. Neurosci Insights. 2022;17:26331055221100587. doi:10.1177/26331055221100587
18. Wang Z, Xue Y, Sun G, et al. Effects of music-supported therapy for depression and cognitive disorders in people living with stroke and its impact on quality of life: A systematic evaluation and meta-analysis. Cerebrovasc Dis. 2025:1-26. doi:10.1159/000543361
19. Huang WH, Dou ZL, Jin HM, Cui Y, Li X, Zeng Q. The effectiveness of music therapy on hand function in patients with stroke: A systematic review of randomized controlled trials. Frontiers in neurology. 2021;12:641023. doi:10.3389/fneur.2021.641023
20. Zhang Y, Cai J, Zhang Y, Ren T, Zhao M, Zhao Q. Improvement in stroke-induced motor dysfunction by music-supported therapy: A systematic review and meta-analysis. Sci Rep. 2016;6:38521. doi:10.1038/srep38521
21. Chen Y-A, Norgaard M. Important findings of a technology-assisted in-home music-based intervention for individuals with stroke: A small feasibility study. Disability and Rehabilitation: Assistive Technology. 2024;19(6):2239-2249. doi:10.1080/17483107.2023.2274397
22. Tosto-Mancuso J, Tabacof L, Herrera JE, et al. Gamified Neurorehabilitation Strategies for Post-stroke Motor Recovery: Challenges and Advantages. Curr Neurol Neurosci Rep. Mar 2022;22(3):183-195. doi:10.1007/s11910-022-01181-y
23. Sullivan KJ, Tilson JK, Cen SY, et al. Fugl-Meyer assessment of sensorimotor function after stroke: Standardized training procedure for clinical practice and clinical trials. Stroke. 2011;42(2):427-32. doi:10.1161/STROKEAHA.110.592766
24. Woytowicz EJ, Rietschel JC, Goodman RN, et al. Determining Levels of Upper Extremity Movement Impairment by Applying a Cluster Analysis to the Fugl-Meyer Assessment of the Upper Extremity in Chronic Stroke. Archives of Physical Medicine and Rehabilitation. Mar 2017; 98(3):456-462. doi:10.1016/j.apmr.2016.06.023
25. Ansari NN, Naghdi S, Arab TK, Jalaie S. The interrater and intrarater reliability of the Modified Ashworth Scale in the assessment of muscle spasticity: Limb and muscle group effect. NeuroRehabilitation. 2008;23(3):231-237. doi:10.3233/nre-2008-23304
26. Han CE, Kim S, Chen S, et al. Quantifying arm nonuse in individuals poststroke. Neurorehabilitation and Neural Repair. Jun 2013;27(5):439-47. doi:10.1177/1545968312471904
27. Chen Y-A, Norgaard M, Albright R, Buchman E, Maitra K. A Home-Based, Mobile-Health-Assisted Piano Therapy to Improve Upper-Extremity Performance in Stroke Survivors: A Pilot Study. The American Journal of Occupational Therapy. 2020; 74(4_Supplement_1):7411515391p1-7411515391p1. doi:10.5014/ajot.2020.74S1-PO4732
28. Chen Y-A, Norgaard M. A Home-Based, mHealth-Assisted Piano Therapy to Improve Upper Extremity Performance in Stroke Survivors: A Pilot Study. Archives of Physical Medicine and Rehabilitation. 2020;101(11):e50. doi:10.1016/j.apmr.2020.09.148
29. Grimm P. Social Desirability Bias. In J. Sheth, & N. Malhotra (Eds.) ed. Wiley International Encyclopedia of Marketing. Hoboken, NJ: John Wiley & Sons.; 2010.
30. van de Mortel TF. Faking it: social desirability response bias in self-report research. Australian Journal of Advanced Nursing. 2008;25(4):40-48.
31. Sedgwick P, Greenwood N. Understanding the Hawthorne effect. Bmj. Sep 4 2015;351:h4672. doi:10.1136/bmj.h4672
32. Adair JG. The Hawthorne effect: A reconsideration of the methodological artifact. Journal of Applied Psychology. 1984;69(2):334-345. doi:10.1037/0021-9010.69.2.334
33. Maidhof C. Error monitoring in musicians. Review. Frontiers in Human Neuroscience. 2013-July-26 2013;Volume 7 – 2013 doi:10.3389/fnhum.2013.00401
34. Ruiz MH, Jabusch HC, Altenmüller E. Detecting wrong notes in advance: neuronal correlates of error monitoring in pianists. Cereb Cortex. Nov 2009;19(11):2625-39. doi:10.1093/cercor/bhp021
35. Rojo N, Amengual J, Juncadella M, et al. Music-supported therapy induces plasticity in the sensorimotor cortex in chronic stroke: a single-case study using multimodal imaging (fMRI-TMS). Brain Inj. 2011;25(7-8):787-93. doi:10.3109/02699052.2011.576305
36. Fujioka T, Ween JE, Jamali S, Stuss DT, Ross B. Changes in neuromagnetic beta-band oscillation after music-supported stroke rehabilitation. Ann N Y Acad Sci. Apr 2012;1252:294-304. doi:10.1111/j.1749-6632.2011.06436.x
37. Amengual JL, Rojo N, Veciana de Las Heras M, et al. Sensorimotor plasticity after music-supported therapy in chronic stroke patients revealed by transcranial magnetic stimulation. PLoS One. 2013;8(4):e61883. doi:10.1371/journal.pone.0061883
38. Grau-Sanchez J, Amengual JL, Rojo N, et al. Plasticity in the sensorimotor cortex induced by music-supported therapy in stroke patients: A TMS study. Front Hum Neurosci. 2013;7:494. doi:10.3389/fnhum.2013.00494
39. Ripollés P, Rojo N, Grau-Sánchez J, et al. Music supported therapy promotes motor plasticity in individuals with chronic stroke. Brain imaging and behavior. 2016;10(4):1289-1307. doi:10.1007/s11682-015-9498-x
40. Ghai S, Maso FD, Ogourtsova T, et al. Neurophysiological changes induced by music-supported therapy for recovering upper extremity function after stroke: A case series. Brain Sciences. 2021;11(5):666.
41. Palumbo A, Groves K, Munoz-Vidal EL, et al. Improvisation and live accompaniment increase motor response and reward during a music playing task. Sci Rep. 2024;14(1):13112. doi:10.1038/s41598-024-62794-6
42. Lang CE, MacDonald JR, Gnip C. Counting repetitions: an observational study of outpatient therapy for people with hemiparesis post-stroke. J Neurol Phys Ther. 2007;31(1):3-10. doi:10.1097/01.npt.0000260568.31746.34
43. Kleim JA, Jones TA. Principles of experience-dependent neural plasticity: implications for rehabilitation after brain damage. Journal of Speech Language and Hearing Research. 2008; 51(1):S225-39. doi:10.1044/1092-4388(2008/018)
44. Nudo RJ, Milliken GW, Jenkins WM, Merzenich MM. Use-dependent alterations of movement representations in primary motor cortex of adult squirrel monkeys. The Journal of Neuroscience. 1996;16(2):785-807. doi:10.1523/JNEUROSCI.16-02-00785.1996