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

mHealth for Rehabilitation (236) Artificial Intelligence (1216) Knowledge Translation and Implementation Science (375) Back Pain (103) Innovations in Exercise Rehabilitation (205) Portable and Mobile Technologies for Rehabilitation (142)

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 (31) Tweetations (11) Metrics

Journals

  1. Chien S, Islam M, Yeh C, Chien P, Chen C, Chin Y, Lin M. Mutual-Aid Mobile App for Emergency Care: Feasibility Study. JMIR Formative Research 2020;4(3):e15494 View
  2. Drydakis N. Mobile applications aiming to facilitate immigrants’ societal integration and overall level of integration, health and mental health. Does artificial intelligence enhance outcomes?. Computers in Human Behavior 2021;117:106661 View
  3. Pfeifer A, Uddin R, Schröder-Pfeifer P, Holl F, Swoboda W, Schiltenwolf M. Mobile Application-Based Interventions for Chronic Pain Patients: A Systematic Review and Meta-Analysis of Effectiveness. Journal of Clinical Medicine 2020;9(11):3557 View
  4. Alev K, Kütt A, Viigimaa M. Disclosing Pharmacogenetic Feedback of Caffeine via eHealth Channels, Assessment of the Methods and Effects to Behavior Change: A Pilot Study. Frontiers in Digital Health 2021;2 View
  5. Chandni Clara D’souza R, Nambiar R, Joseph S Martis J, N Rao S, Singh M. The incidence of persistence cervicalgia among students and the risk factors contributing towards it. IP Indian Journal of Anatomy and Surgery of Head, Neck and Brain 2020;6(2):49 View
  6. Anan T, Kajiki S, Oka H, Fujii T, Kawamata K, Mori K, Matsudaira K. Effects of an Artificial Intelligence–Assisted Health Program on Workers With Neck/Shoulder Pain/Stiffness and Low Back Pain: Randomized Controlled Trial. JMIR mHealth and uHealth 2021;9(9):e27535 View
  7. Lötsch J, Ultsch A, Mayer B, Kringel D. Artificial intelligence and machine learning in pain research: a data scientometric analysis. PAIN Reports 2022;7(6):e1044 View
  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: a scoping review (Preprint). JMIR mHealth and uHealth 2023 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

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