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

Special Issue on Computing and Mental Health (2018) (33) Methods and Feasibility Studies (383) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1424) mHealth for Wellness, Behavior Change and Prevention (2912) Back Pain (116) Innovations in Exercise Rehabilitation (215)

Published on in Vol 20, No 10 (2018): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10147, first published .
Feasibility and Acceptability of Mobile Phone–Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults

Feasibility and Acceptability of Mobile Phone–Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults

Feasibility and Acceptability of Mobile Phone–Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults

Authors of this article:

Mashfiqui Rabbi1 Author Orcid Image ;   Min SH Aung2 Author Orcid Image ;   Geri Gay2 Author Orcid Image ;   M Cary Reid3 Author Orcid Image ;   Tanzeem Choudhury2 Author Orcid Image
Article Authors Cited by (41) Tweetations (11) Metrics

Journals

  1. Tuerk P, Schaeffer C, McGuire J, Adams Larsen M, Capobianco N, Piacentini J. Adapting Evidence-Based Treatments for Digital Technologies: a Critical Review of Functions, Tools, and the Use of Branded Solutions. Current Psychiatry Reports 2019;21(10) View
  2. Metcalf C, Huntsman M, Garcia G, Kochanski A, Chikinda M, Watanabe E, Underwood T, Vanegas F, Smith M, White H, Bulaj G. Music-Enhanced Analgesia and Antiseizure Activities in Animal Models of Pain and Epilepsy: Toward Preclinical Studies Supporting Development of Digital Therapeutics and Their Combinations With Pharmaceutical Drugs. Frontiers in Neurology 2019;10 View
  3. Rabbi M, Li K, Yan H, Hall K, Klasnja P, Murphy S. ReVibe. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2019;3(4):1 View
  4. Naranjo-Hernández D, Reina-Tosina J, Roa L. Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review. Sensors 2020;20(2):365 View
  5. Madill E, Samuels R, Newman D, Boudreaux-Kelley M, Weiner D. Development of an Evaluative, Educational, and Communication-Facilitating App for Older Adults with Chronic Low Back Pain: Patient Perceptions of Usability and Utility. Pain Medicine 2019;20(11):2120 View
  6. Svendsen M, Wood K, Kyle J, Cooper K, Rasmussen C, Sandal L, Stochkendahl M, Mair F, Nicholl B. Barriers and facilitators to patient uptake and utilisation of digital interventions for the self-management of low back pain: a systematic review of qualitative studies. BMJ Open 2020;10(12):e038800 View
  7. Elbers S, Pool J, Wittink H, Köke A, Smeets R. Exploring the Feasibility of Relapse Prevention Strategies in Interdisciplinary Multimodal Pain Therapy Programs: Qualitative Study. JMIR Human Factors 2020;7(4):e21545 View
  8. Mallick-Searle T, Sharma K, Toal P, Gutman A. Pain and Function in Chronic Musculoskeletal Pain—Treating the Whole Person. Journal of Multidisciplinary Healthcare 2021;Volume 14:335 View
  9. Tong H, Quiroz J, Kocaballi A, Fat S, Dao K, Gehringer H, Chow C, Laranjo L. Personalized mobile technologies for lifestyle behavior change: A systematic review, meta-analysis, and meta-regression. Preventive Medicine 2021;148:106532 View
  10. Jenssen M, Bakkevoll P, Ngo P, Budrionis A, Fagerlund A, Tayefi M, Bellika J, Godtliebsen F. Machine Learning in Chronic Pain Research: A Scoping Review. Applied Sciences 2021;11(7):3205 View
  11. Daryabeygi-Khotbehsara R, Shariful Islam S, Dunstan D, McVicar J, Abdelrazek M, Maddison R. Smartphone-Based Interventions to Reduce Sedentary Behavior and Promote Physical Activity Using Integrated Dynamic Models: Systematic Review. Journal of Medical Internet Research 2021;23(9):e26315 View
  12. 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
  13. Domin A, Spruijt-Metz D, Theisen D, Ouzzahra Y, Vögele C. Smartphone-Based Interventions for Physical Activity Promotion: Scoping Review of the Evidence Over the Last 10 Years. JMIR mHealth and uHealth 2021;9(7):e24308 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. Boutilier J, Jónasson J, Yoeli E. Improving Tuberculosis Treatment Adherence Support: The Case for Targeted Behavioral Interventions. Manufacturing & Service Operations Management 2022;24(6):2925 View
  16. Ensari I, Lipsky-Gorman S, Horan E, Bakken S, Elhadad N. Associations between physical exercise patterns and pain symptoms in individuals with endometriosis: a cross-sectional mHealth-based investigation. BMJ Open 2022;12(7):e059280 View
  17. Lonsdale H, Gray G, Ahumada L, Yates H, Varughese A, Rehman M. The Perioperative Human Digital Twin. Anesthesia & Analgesia 2022;134(4):885 View
  18. Ma J, Floegel T, Li L, Leese J, De Vera M, Beauchamp M, Taunton J, Liu-Ambrose T, Allen K. Tailored physical activity behavior change interventions: challenges and opportunities. Translational Behavioral Medicine 2021;11(12):2174 View
  19. Martinez G, Mattingly S, Robles-Granda P, Saha K, Sirigiri A, Young J, Chawla N, De Choudhury M, D'Mello S, Mark G, Striegel A. Predicting Participant Compliance With Fitness Tracker Wearing and Ecological Momentary Assessment Protocols in Information Workers: Observational Study. JMIR mHealth and uHealth 2021;9(11):e22218 View
  20. Karmakar A, Khan M, Abdul-Rahman M, Shahid U. The Advances and Utility of Artificial Intelligence and Robotics in Regional Anesthesia: An Overview of Recent Developments. Cureus 2023 View
  21. 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
  22. Janevic M, Murnane E, Fillingim R, Kerns R, Reid M. Mapping the Design Space of Technology-Based Solutions for Better Chronic Pain Care: Introducing the Pain Tech Landscape. Psychosomatic Medicine 2023;85(7):612 View
  23. An R, Shen J, Wang J, Yang Y. A scoping review of methodologies for applying artificial intelligence to physical activity interventions. Journal of Sport and Health Science 2024;13(3):428 View
  24. Madrid-García A, Merino-Barbancho B, Rodríguez-González A, Fernández-Gutiérrez B, Rodríguez-Rodríguez L, Menasalvas-Ruiz E. Understanding the role and adoption of artificial intelligence techniques in rheumatology research: An in-depth review of the literature. Seminars in Arthritis and Rheumatism 2023;61:152213 View
  25. Zhong X, Cui Y, Wen L, Li S, Gao Z, Zang S, Zhang M, Bai X. Health information-seeking experience in people with head and neck neoplasms undergoing treatment: a qualitative study. Supportive Care in Cancer 2024;32(2) View
  26. Cai C, Cai T, Li H. Transfer learning for contextual multi-armed bandits. The Annals of Statistics 2024;52(1) View
  27. Agans J, Ma F, Schade S, Sciamanna C. Supporting physical activity adoption through recommender system technology: A pilot study. Journal of Health Psychology 2024 View
  28. Bucher A, Blazek E, Symons C. How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review. Mayo Clinic Proceedings: Digital Health 2024;2(3):375 View
  29. Matthews P, Rhodes-Maquaire C. Personalisation and Recommendation for Mental Health Apps: A Scoping Review. Behaviour & Information Technology 2024:1 View
  30. 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
  31. Gabarron E, Larbi D, Rivera-Romero O, Denecke K. Human Factors in AI-Driven Digital Solutions for Increasing Physical Activity: Scoping Review. JMIR Human Factors 2024;11:e55964 View
  32. Fiedler J, Bergmann M, Sell S, Woll A, Stetter B. Just-in-Time Adaptive Interventions for Behavior Change in Physiological Health Outcomes and the Use Case for Knee Osteoarthritis: Systematic Review. Journal of Medical Internet Research 2024;26:e54119 View
  33. Zahavi I, Ben Shitrit I, Einav S. Using augmented intelligence to improve long term outcomes. Current Opinion in Critical Care 2024;30(5):523 View
  34. 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
  35. Eaton C, Vallejo N, McDonald X, Wu J, Rodríguez R, Muthusamy N, Mathioudakis N, Riekert K. User Engagement With mHealth Interventions to Promote Treatment Adherence and Self-Management in People With Chronic Health Conditions: Systematic Review. Journal of Medical Internet Research 2024;26:e50508 View
  36. Gagnon M, Ouellet S, Attisso E, Supper W, Amil S, Rhéaume C, Paquette J, Chabot C, Laferrière M, Sasseville M. Wearable Devices for Supporting Chronic Disease Self-Management: Scoping Review. Interactive Journal of Medical Research 2024;13:e55925 View
  37. 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
  38. Antel R, Whitelaw S, Gore G, Ingelmo P. Moving towards the use of artificial intelligence in pain management. European Journal of Pain 2024 View

Books/Policy Documents

  1. Rabbi M, Klasnja P, Choudhury T, Tewari A, Murphy S. Digital Phenotyping and Mobile Sensing. View
  2. Rabbi M, Klasnja P, Choudhury T, Tewari A, Murphy S. Digital Phenotyping and Mobile Sensing. View