ArticleJMIR mHealth and uHealth2018
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.
Article in JMIR mHealth and uHealth, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial Intelligence in Physical, Occupational and Neuro-Rehabilitation: Clinical Effectiveness, Prognostic Performance, and Pre-Implementation Feasibility - A Systematic Review.Journal of medical systems · 2026Pooled it
- Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025Pooled it
- Impact of 25 Years of Mobile Health Tools for Pain Management in Patients With Chronic Musculoskeletal Pain: Systematic Review.Journal of medical Internet research · 2024Pooled it
- 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 · 2021Trial
- AI Applications for Chronic Condition Self-Management: Scoping Review.Journal of medical Internet research · 2025Article
- Article
- Artificial intelligence in chronic disease self-management: current applications and future directions.Frontiers in public health · 2025Review
- Comparison of Mobile Health-Based Exercise vs Traditional Exercise for Chronic Neck Pain: A Systematic Review and Meta-Analysis.Journal of pain research · 2025Review
- Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity: Scoping Review and Categorization Overview.Journal of medical Internet research · 2024Article
- Development and Use of Mobile Messaging for Individuals With Musculoskeletal Pain Conditions: Scoping Review.JMIR mHealth and uHealth · 2024Article
- Identifying the risk of exercises, recommended by an artificial intelligence for patients with musculoskeletal disorders.Scientific reports · 2024Article
- 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 (Basel, Switzerland) · 2024Review
- Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives.Frontiers in immunology · 2024Review
- Stakeholders' Perspectives, Needs, and Barriers to Self-Management for People With Physical Disabilities Experiencing Chronic Conditions: Focus Group Study.JMIR rehabilitation and assistive technologies · 2023Article
- Artificial Intelligence-Based Consumer Health Informatics Application: Scoping Review.Journal of medical Internet research · 2023Article
- Development of Automatic Portable Pathology Scanner and Its Evaluation for Clinical Practice.Journal of digital imaging · 2023Article
- Exploring mHealth applications for self-management of chronic low back pain: A survey of features and benefits.Heliyon · 2023Review
- Using artificial intelligence to improve pain assessment and pain management: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Facilitators and Barriers of Artificial Intelligence Applications in Rehabilitation: A Mixed-Method Approach.International journal of environmental research and public health · 2022Article
- Contrasting the Expectations and Experiences Related to Mobile Health Use for Chronic Pain: Questionnaire Study.JMIR human factors · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundChronic musculoskeletal neck and back pain are disabling conditions among adults. Use of technology has been suggested as an alternative way to increase adherence to exercise therapy, which may improve clinical outcomes.
objectiveThe aim was to investigate the self-perceived benefits of an artificial intelligence (AI)-embedded mobile app to self-manage chronic neck and back pain.
methodsA total of 161 participants responded to the invitation. The evaluation questionnaire included 14 questions that were intended to explore if using the AI rehabilitation system may (1) increase time spent on therapeutic exercise, (2) affect pain level (assessed by the 0-10 Numerical Pain Rating Scale), and (3) reduce the need for other interventions.
resultsAn increase in time spent on therapeutic exercise per day was observed. The median Numerical Pain Rating Scale scores were 6 (interquartile range [IQR] 5-8) before and 4 (IQR 3-6) after using the AI-embedded mobile app (95% CI 1.18-1.81). A 3-point reduction was reported by the participants who used the AI-embedded mobile app for more than 6 months. Reduction in the usage of other interventions while using the AI-embedded mobile app was also reported.
conclusionsThis study demonstrated the positive self-perceived beneficiary effect of using the AI-embedded mobile app to provide a personalized therapeutic exercise program. The positive results suggest that it at least warrants further study to investigate the physiological effect of the AI-embedded mobile app and how it compares with routine clinical care.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.