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

mHealth for Rehabilitation (304) Artificial Intelligence (1721) Knowledge Translation and Implementation Science (442) Back Pain (126) Innovations in Exercise Rehabilitation (242) Portable and Mobile Technologies for Rehabilitation (177)

Published on in Vol 6, No 11 (2018): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/8127, first published .
The Perceived Benefits of an Artificial Intelligence–Embedded Mobile App Implementing Evidence-Based Guidelines for the Self-Management of Chronic Neck and Back Pain: Observational Study

The Perceived Benefits of an Artificial Intelligence–Embedded Mobile App Implementing Evidence-Based Guidelines for the Self-Management of Chronic Neck and Back Pain: Observational Study

The Perceived Benefits of an Artificial Intelligence–Embedded Mobile App Implementing Evidence-Based Guidelines for the Self-Management of Chronic Neck and Back Pain: Observational Study

Authors of this article:

Wai Leung Ambrose Lo1, 2 Author Orcid Image ;   Di Lei1 Author Orcid Image ;   Le Li1 Author Orcid Image ;   Dong Feng Huang1 Author Orcid Image ;   Kin-Fai Tong2 Author Orcid Image
Article Authors Cited by (42) Tweetations (11) Metrics

Journals

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  19. Raja M, Loughran R, Caffery F. A review of applications of artificial intelligence in cardiorespiratory rehabilitation. Informatics in Medicine Unlocked 2023;41:101327 View
  20. Evans E, Zengul A, Knight A, Willig A, Cherrington A, Mehta T, Thirumalai M. Stakeholders’ Perspectives, Needs, and Barriers to Self-Management for People With Physical Disabilities Experiencing Chronic Conditions: Focus Group Study. JMIR Rehabilitation and Assistive Technologies 2023;10:e43309 View
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  22. Hurmuz M, Jansen-Kosterink S, Mork P, Bach K, Hermens H. Factors influencing the use of an artificial intelligence-based app ( self BACK) for tailored self-management support among adults with neck and/or low back pain. Disability and Rehabilitation 2025;47(4):958 View
  23. Armfield N, Elphinston R, Liimatainen J, Scotti Requena S, Eather C, Edirippulige S, Ritchie C, Robins S, Sterling M. Development and Use of Mobile Messaging for Individuals With Musculoskeletal Pain Conditions: Scoping Review. JMIR mHealth and uHealth 2024;12:e55625 View
  24. Griefahn A, Zalpour C, Luedtke K. Identifying the risk of exercises, recommended by an artificial intelligence for patients with musculoskeletal disorders. Scientific Reports 2024;14(1) View
  25. Scala L, Giglioni G, Bertazzoni L, Bonetti F. The Efficacy of the Smartphone App for the Self-Management of Low Back Pain: A Systematic Review and Assessment of Their Quality through the Mobile Application Rating Scale (MARS) in Italy. Life 2024;14(6):760 View
  26. Shi J, Sit R. Impact of 25 Years of Mobile Health Tools for Pain Management in Patients With Chronic Musculoskeletal Pain: Systematic Review. Journal of Medical Internet Research 2024;26:e59358 View
  27. Reis F, Carvalho M, Neves G, Nogueira L, Meziat-Filho N. Machine learning methods in physical therapy: A scoping review of applications in clinical context. Musculoskeletal Science and Practice 2024;74:103184 View
  28. 刘 涛. Visualization Analysis of Exercise Therapy Applications for Cervical Spondylosis Based on CiteSpace. Traditional Chinese Medicine 2024;13(09):2401 View
  29. Yang Y, Liu Y, Chen Y, Luo D, Xu K, Zhang L. Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives. Frontiers in Immunology 2024;15 View
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Books/Policy Documents

  1. Machi A. Pain Management for Clinicians. View
  2. Davids J, Lidströmer N, Ashrafian H. Artificial Intelligence in Medicine. View
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Conference Proceedings

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  3. Laureano F, Da Fonseca L, Silva P. 2023 International Conference on Graphics and Interaction (ICGI). Enhancing Telerehabilitation Through Gamification: Usability and User Experience Evaluation View