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

Technology in Physiotherapy (134) Cognitive and Neurorehabilitation (262)

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

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 (15) Tweetations (4) 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