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This paper is in the following e-collection/theme issue:

Technology in Physiotherapy (277) Cognitive and Neurorehabilitation (428)

Published on in Vol 5, No 2 (2018): Jul-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9219, first published .
Diverse group in a workshop discussing plans on paper at tables.

Adoption of Stroke Rehabilitation Technologies by the User Community: Qualitative Study

Adoption of Stroke Rehabilitation Technologies by the User Community: Qualitative Study

Authors of this article:

Andrew Kerr1 Author Orcid Image ;   Mark Smith2 Author Orcid Image ;   Lynn Reid3 Author Orcid Image ;   Lynne Baillie4 Author Orcid Image
Article Authors Cited by (30) Tweetations (3) Metrics

Journals

  1. Sweeney G, Barber M, Kerr A. Exploration of barriers and enablers for evidence-based interventions for upper limb rehabilitation following a stroke: Use of Constraint Induced Movement Therapy and Robot Assisted Therapy in NHS Scotland. British Journal of Occupational Therapy 2020;83(11):690 View
  2. Tomasella F, Morgan H. “Sometimes I don’t have a pulse … and I’m still alive!” Interviews with healthcare professionals to explore their experiences of and views on population-based digital health technologies. DIGITAL HEALTH 2021;7 View
  3. Morrow C, Johnson E, Simpson K, Seo N. Determining Factors that Influence Adoption of New Post-Stroke Sensorimotor Rehabilitation Devices in the USA. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2021;29:1213 View
  4. Kerr A, Grealy M, Kuschmann A, Rutherford R, Rowe P. A Co-creation Centre for Accessible Rehabilitation Technology. Frontiers in Rehabilitation Sciences 2022;2 View
  5. Epalte K, Grjadovojs A, Bērziņa G. Use of the Digital Assistant Vigo in the Home Environment for Stroke Recovery: Focus Group Discussion With Specialists Working in Neurorehabilitation. JMIR Rehabilitation and Assistive Technologies 2023;10:e44285 View
  6. Sharma Y, Cheung L, Patterson K, Iaboni A. Factors Influencing the Clinical Adoption of Quantitative Gait Analysis Technologies for Adult Patient Populations With a Focus on Clinical Efficacy and Clinician Perspectives: Protocol for a Scoping Review. JMIR Research Protocols 2023;12:e39767 View
  7. Broderick M, O'Shea R, Burridge J, Demain S, Johnson L, Bentley P. Examining Usability, Acceptability, and Adoption of a Self-Directed, Technology-Based Intervention for Upper Limb Rehabilitation After Stroke: Cohort Study. JMIR Rehabilitation and Assistive Technologies 2023;10:e45993 View
  8. Alder G, Taylor D, Rashid U, Olsen S, Brooks T, Terry G, Niazi I, Signal N. A Brain Computer Interface Neuromodulatory Device for Stroke Rehabilitation: Iterative User-Centered Design Approach. JMIR Rehabilitation and Assistive Technologies 2023;10:e49702 View
  9. Stark A, Krayter S, Dockweiler C. Competencies required by patients and health professionals regarding telerehabilitation: A scoping review. DIGITAL HEALTH 2023;9 View
  10. Sharma Y, Cheung L, Patterson K, Iaboni A. Factors influencing the clinical adoption of quantitative gait analysis technology with a focus on clinical efficacy and clinician perspectives: A scoping review. Gait & Posture 2024;108:228 View
  11. Rony R, Amir S, Ahmed N, Atiba S, Verdezoto N, Sparkes V, Stawarz K. Understanding the Sociocultural Challenges and Opportunities for Affordable Wearables to Support Poststroke Upper-Limb Rehabilitation: Qualitative Study. JMIR Rehabilitation and Assistive Technologies 2024;11:e54699 View
  12. Kerr A, Grealy M, Slachetka M, Wodu C, Sweeney G, Boyd F, Colville D, Rowe P. A Participatory Model for Cocreating Accessible Rehabilitation Technology for Stroke Survivors: User-Centered Design Approach. JMIR Rehabilitation and Assistive Technologies 2024;11:e57227 View
  13. Madanaguli A, Parida V, Oghazi P, Tran P. Technological Innovation Adoption Among Swedish Healthcare Professionals: A Contingency Technology Adoption Framework. IEEE Transactions on Engineering Management 2024;71:13006 View
  14. Lv Z, Singh A. Edge-Cloud-Based Wearable Computing for Automation Empowered Virtual Rehabilitation. IEEE Transactions on Automation Science and Engineering 2024;21(3):3896 View
  15. Li F, Guo Y, Xu W, Zhang W, Zhao F, Wang B, Du H, Zhang C. Design and control of a low-cost non-backdrivable end-effector upper limb rehabilitation device. Frontiers in Rehabilitation Sciences 2024;5 View
  16. MENSAH-GOURMEL J, BEKTESHI S, BROCHARD S, MONBALIU E, GRIGORIU A, NEWMAN C, KONINGS M, DE LA CRUZ J, PONS C. Digital technologies for pediatric rehabilitation: current access and use in the European Rehatech4child survey. European Journal of Physical and Rehabilitation Medicine 2024;60(6) View
  17. Covarrubias‐Escudero F, Balbontín‐Miranda F, Urzúa‐Soler B, Ciuffardi R, Muñoz M, Hernández V, Appelgren‐González J. Home‐based functional electrical stimulation protocol for people with chronic stroke. Efficacy and usability of a single‐center cohort. Artificial Organs 2025;49(7):1141 View
  18. Kheirollahzadeh M, Sarvghadi P, Azizkhani S, Bani Hani J, Monnin C, Choukou M. Digital Health Technology for Stroke Rehabilitation in Canada: A Scoping Review. Applied Sciences 2025;15(10):5340 View
  19. Jiao Y, Dajime P, Zhang Q, Reading S, Smith M, Zhang Y. A clinically oriented VR-based treadmill training system for post-stroke rehabilitation: integration of motor learning and control principles. Virtual Reality 2025;29(3) View
  20. Lin D, Eaves D, Gibbons T, Aquino M, Edwards M, Poliakoff E, Bek J, Emerson J. Perspectives of stroke survivors and informal caregivers on home-based mental practice for upper limb recovery after stroke: a qualitative co-design study. Neuropsychological Rehabilitation 2026;36(6):1122 View
  21. Fai Ho S, Thomson A, Moylan T, McGuckin J, Kerr A. Automated Movement Feedback for Recovering Independence in the Sit-to-Stand Movement in an Older Population: A Pilot Randomised Controlled Trial of a Novel System. OBM Geriatrics 2019;03(04):1 View
  22. Gouroubera M, Gouthon M, Segnon A, Dosso F, Togbévi Q, Moumouni-Moussa I, Zougmoré R. Understanding farmers’ acceptance of digital technologies: a meta-analytic structural equation modeling approach based on the technology acceptance model. Frontiers in Sustainable Food Systems 2026;10 View
  23. Handayani P, Alifia K, Sutanto J, Erlina E, Davies S, Mawuntu A, Jusuf M, Warren N. Online Community Support for Stroke Survivors and Caregivers: Scoping Review. Journal of Medical Internet Research 2026;28:e71190 View
  24. Jamieson M, Parry E, Parry L, Russell R, Fish J, Gentry S, Bateman A, Evans J. Sustained use of compensatory technology following brain injury: experiences from Neumind users. Disability and Rehabilitation: Assistive Technology 2026:1 View
  25. Grevet E, Izac M, Py J, Amadieu F, Glize B, Gasq D, Jeunet-Kelway C. Bridging innovation and adoption: a mixed-methods investigation into stroke survivors’ acceptability of brain-computer interface-based rehabilitation interventions. Journal of Neural Engineering 2026;23(2):026037 View
  26. Mensah Gourmel J, Brochard S, Bekteshi S, Monbaliu E, Cornec G, Grigoriu A, Newman C, Konings M, De La Cruz J, Pons C. Explaining the Use Behavior of Digital Technologies in Pediatric Rehabilitation: Structural Equation Modeling Analysis of a Cross-Sectional European Survey. Journal of Medical Internet Research 2026;28:e80355 View
  27. Setyonugroho W, Fathiya A, Islami M, Setiawan F, Utami S, Abdul Rahman M, Permana I. User-Centered Feature Prioritization in Stroke Self-Care Applications: A Kano Model Study. Journal of Innovation Information Technology and Application (JINITA) 2026;8(1):70 View

Books/Policy Documents

  1. Muthukrishnan A, Sindhe R, Arakeri G, Maruthi M. Oral Cancer and Artificial Intelligence. View

Conference Proceedings

  1. Jiao Y, Dajime P, Reading S, Zhang Y. 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). A VR-based Treadmill Training System for Post-stroke Gait Rehabilitation* View
  2. Setyonugroho W, Utami S, Islami M, Amanu D, Kirana I, Fathiya A. 2024 Ninth International Conference on Informatics and Computing (ICIC). IoT-based System for The Stroke Survivor Feature Needs: A Qualitative Analysis View