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

mHealth for Rehabilitation (269) Artificial Intelligence (1421) Knowledge Translation and Implementation Science (407) Back Pain (116) Innovations in Exercise Rehabilitation (216) Portable and Mobile Technologies for Rehabilitation (156)

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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  8. Hogan T, Etingen B, McMahon N, Bixler F, Am L, Wacks R, Shimada S, Reilly E, Frisbee K, Smith B. Understanding Adoption and Preliminary Effectiveness of a Mobile App for Chronic Pain Management Among US Military Veterans: Pre-Post Mixed Methods Evaluation. JMIR Formative Research 2022;6(1):e33716 View
  9. Alsobhi M, Sachdev H, Chevidikunnan M, Basuodan R, K U D, Khan F. Facilitators and Barriers of Artificial Intelligence Applications in Rehabilitation: A Mixed-Method Approach. International Journal of Environmental Research and Public Health 2022;19(23):15919 View
  10. Esmaeilzadeh P, Mirzaei T, Dharanikota S. Patients’ Perceptions Toward Human–Artificial Intelligence Interaction in Health Care: Experimental Study. Journal of Medical Internet Research 2021;23(11):e25856 View
  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
  12. Kheirinejad S, Alorwu A, Visuri A, Hosio S. Contrasting the Expectations and Experiences Related to Mobile Health Use for Chronic Pain: Questionnaire Study. JMIR Human Factors 2022;9(3):e38265 View
  13. Xu J, Jin J, Cheng M, Zhou W, Zhang S, Bai Y. Benefit of an Internet-Based Management System among Hemodialysis Patients at the Risk of Intradialytic Hypotension and Muscle Cramps: A Controlled before and after Study. Blood Purification 2022;51(5):464 View
  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
  16. Sumner J, Lim H, Chong L, Bundele A, Mukhopadhyay A, Kayambu G. Artificial intelligence in physical rehabilitation: A systematic review. Artificial Intelligence in Medicine 2023;146:102693 View
  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
  18. Amorim P, Paulo J, Silva P, Peixoto P, Castelo-Branco M, Martins H. Machine Learning Applied to Low Back Pain Rehabilitation – A Systematic Review. International Journal of Digital Health 2021;1(1):10 View
  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
  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
  30. Antel R, Whitelaw S, Gore G, Ingelmo P. Moving towards the use of artificial intelligence in pain management. European Journal of Pain 2024 View
  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

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