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

mHealth for Rehabilitation (263) Artificial Intelligence (1371) Knowledge Translation and Implementation Science (398) Back Pain (116) Innovations in Exercise Rehabilitation (213) Portable and Mobile Technologies for Rehabilitation (151)

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 (37) Tweetations (11) Metrics

Journals

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  11. Kelly M, Fullen B, Martin D, McMahon S, McVeigh J. eHealth Interventions to Support Self-Management in People With Musculoskeletal Disorders, “eHealth: It’s TIME”—A Scoping Review. Physical Therapy 2022;102(4) View
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  14. Lewkowicz D, Slosarek T, Wernicke S, Winne A, Wohlbrandt A, Bottinger E. Digital Therapeutic Care and Decision Support Interventions for People With Low Back Pain: Systematic Review. JMIR Rehabilitation and Assistive Technologies 2021;8(4):e26612 View
  15. Asan O, Choi E, Wang X. Artificial Intelligence–Based Consumer Health Informatics Application: Scoping Review. Journal of Medical Internet Research 2023;25:e47260 View
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  17. Kheirinejad S, Visuri A, Suryanarayana S, Hosio S. Exploring mHealth applications for self-management of chronic low back pain: A survey of features and benefits. Heliyon 2023;9(6):e16586 View
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  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
  21. Salave S, Rana D, Benival D, Jain A. Decoding Artificial Intelligence in Neuroscience: Applications Beyond Diagnosis. Current Indian Science 2023;01 View
  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 2024:1 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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  31. Brons A, Wang S, Visser B, Kröse B, Bakkes S, Veltkamp R. Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity: Scoping Review and Categorization Overview. Journal of Medical Internet Research 2024;26:e47774 View

Books/Policy Documents

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