Trial reportJAMA network open2023
Effect of an Artificial Intelligence-Based Self-Management App on Musculoskeletal Health in Patients With Neck and/or Low Back Pain Referred to Specialist Care: A Randomized Clinical Trial.
Trial report in JAMA network open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04463043 (The Effectiveness of an App-based), which is not on this map. Cited by 44 papers, 11 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.
The Effectiveness of an App-based (selfBACK) Versus a Web-based (e-Help) Self-management Intervention or Usual Care in People With Low Back and/or Neck Pain on a Waiting List for Hospital-based Outpatient Rehabilitation: a Three Arm Randomised Controlled Trial
Who cites it
44 citing papers in PubMed, 11 syntheses or guidelines pooled it.
- Effects of Digital Health Interventions in Chronic Musculoskeletal Pain: Systematic Review and Bayesian Network Meta-Analysis of Randomized Controlled Trials.Journal of medical Internet research · 2026Pooled it
- Cost-effectiveness of artificial intelligence interventions for musculoskeletal disorders of the spine: a systematic review.Rheumatology international · 2026Pooled it
- The physical and psychological effectiveness of mHealth-based physiotherapy for patients with chronic non-specific low back pain: A systematic review and meta-analysis.Journal of telemedicine and telecare · 2026Pooled it
- Telerehabilitation for neck pain.The Cochrane database of systematic reviews · 2025Pooled it
- Models of care for managing non-specific low back pain.The Cochrane database of systematic reviews · 2025Pooled it
- Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025Pooled it
- The multiple uses of artificial intelligence in exercise programs: a narrative review.Frontiers in public health · 2025Pooled it
- Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions.Frontiers in public health · 2025Pooled it
- Effectiveness of AI-assisted rehabilitation for musculoskeletal disorders: a network meta-analysis of pain, range of motion, and functional outcomes.Frontiers in bioengineering and biotechnology · 2025Pooled it
- Application of Chatbots to Help Patients Self-Manage Diabetes: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled 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
- Clinical Efficacy of Multimodal Exercise Telerehabilitation Based on AI for Chronic Nonspecific Low Back Pain: Randomized Controlled Trial.JMIR mHealth and uHealth · 2025Trial
- The Physical and Psychological Effects of Telerehabilitation-Based Exercise for Patients With Nonspecific Low Back Pain: Prospective Randomized Controlled Trial.JMIR mHealth and uHealth · 2024Trial
- An Artificial Intelligence-Based App for Self-Management of Low Back and Neck Pain in Specialist Care: Process Evaluation From a Randomized Clinical Trial.JMIR human factors · 2024Trial
- Patients' experiences of musculoskeletal pain self-management and perspectives on virtual physiotherapy in Saudi Arabia: a reflexive thematic analysis.BMC health services research · 2026Article
- Personalization of Training and Weight Reduction Using Artificial Intelligence: A Scoping Review of Current Evidence and Practical Limitations.Journal of functional morphology and kinesiology · 2026Review
- Multidisciplinary expert consensus on clinical diagnosis and therapies for low back pain.Bone research · 2026Article
- Article
- The Analytical Framework of Clinical Trials Evaluating Clinical Outcomes of Artificial Intelligence-Based Digital Health Interventions: A Systematic Literature Review.Journal of market access & health policy · 2026Review
- A Gamified Pain Management Intervention for Adults With Chronic Pain in Mainland China: Single-Arm Pre-Post Pilot Study With Machine Learning Predictive Modeling.JMIR formative research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Importance: Self-management is a key element in the care of persistent neck and low back pain. Individually tailored self-management support delivered via a smartphone app in a specialist care setting has not been tested. Objective: To determine the effect of individually tailored self-management support delivered via an artificial intelligence-based app (SELFBACK) adjunct to usual care vs usual care alone or nontailored web-based self-management support (e-Help) on musculoskeletal health. Design, Setting, and Participants: This randomized clinical trial recruited adults 18 years or older with neck and/or low back pain who had been referred to and accepted on a waiting list for specialist care at a multidisciplinary hospital outpatient clinic for back, neck, and shoulder rehabilitation. Participants were enrolled from July 9, 2020, to April 29, 2021. Of 377 patients assessed for eligibility, 76 did not complete the baseline questionnaire, and 7 did not meet the eligibility criteria (ie, did not own a smartphone, were unable to take part in exercise, or had language barriers); the remaining 294 patients were included in the study and randomized to 3 parallel groups, with follow-up of 6 months. Interventions: Participants were randomly assigned to receive app-based individually tailored self-management support in addition to usual care (app group), web-based nontailored self-management support in addition to usual care (e-Help group), or usual care alone (usual care group). Main Outcomes and Measures: The primary outcome was change in musculoskeletal health measured by the Musculoskeletal Health Questionnaire (MSK-HQ) at 3 months. Secondary outcomes included change in musculoskeletal health measured by the MSK-HQ at 6 weeks and 6 months and pain-related disability, pain intensity, pain-related cognition, and health-related quality of life at 6 weeks, 3 months, and 6 months. Results: Among 294 participants (mean [SD] age, 50.6 [14.9] years; 173 women [58.8%]), 99 were randomized to the app group, 98 to the e-Help group, and 97 to the usual care group. At 3 months, 243 participants (82.7%) had complete data on the primary outcome. In the intention-to-treat analysis at 3 months, the adjusted mean difference in MSK-HQ score between the app and usual care groups was 0.62 points (95% CI, -1.66 to 2.90 points; P = .60). The adjusted mean difference between the app and e-Help groups was 1.08 points (95% CI, -1.24 to 3.41 points; P = .36). Conclusions and Relevance: In this randomized clinical trial, individually tailored self-management support delivered via an artificial intelligence-based app adjunct to usual care was not significantly more effective in improving musculoskeletal health than usual care alone or web-based nontailored self-management support in patients with neck and/or low back pain referred to specialist care. Further research is needed to investigate the utility of implementing digitally supported self-management interventions in the specialist care setting and to identify instruments that capture changes in self-management behavior. Trial Registration: ClinicalTrials.gov Identifier: NCT04463043.
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