ArticleJMIR research protocols2019
An App-Delivered Self-Management Program for People With Low Back Pain: Protocol for the selfBACK Randomized Controlled Trial.
Article in JMIR research protocols, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 31 papers, 2 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.
A Decision Support System for Self-management of Low Back Pain - a Randomized Controlled Trial for the selfBACK Project
Effectiveness of the SelfBack-App in Preventing and Treating Low Back Pain Versus no Intervention: a Non-randomized Clinical Trial
Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it, 63 citations in OpenAlex.
- Reporting Quality of AI Intervention in Randomized Controlled Trials in Primary Care: Systematic Review and Meta-Epidemiological Study.Journal of medical Internet research · 2025Pooled it
- Digital Health Technologies for Long-term Self-management of Osteoporosis: Systematic Review and Meta-analysis.JMIR mHealth and uHealth · 2022Pooled it
- Evaluation of the Impact of a Smartphone App on Adherence to an Exercise Program in People With Chronic Low Back Pain: Randomized Controlled Trial.JMIR mHealth and uHealth · 2026Trial
- Trial
- Supporting self-management with an internet intervention for low back pain in primary care: a RCT (SupportBack 2).Health technology assessment (Winchester, England) · 2025Trial
- Effect of a smartphone self-management digital support system for low-back pain (selfBACK) among workers with high physical work demands - secondary analysis of a randomized controlled trial.Scandinavian journal of work, environment & health · 2024Trial
- Trial
- The Role of Pain Duration and Pain Intensity on the Effectiveness of App-Delivered Self-Management for Low Back Pain (selfBACK): Secondary Analysis of a Randomized Controlled Trial.JMIR mHealth and uHealth · 2023Trial
- One size does not fit all: Participants' experiences of the selfBACK app to support self-management of low back pain-a qualitative interview study.Chiropractic & manual therapies · 2022Trial
- Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes.BMC medical informatics and decision making · 2022Trial
- Multimorbidity and co-occurring musculoskeletal pain do not modify the effect of the SELFBACK app on low back pain-related disability.BMC medicine · 2022Trial
- From information to action: a co-created evaluation of digital resources for musculoskeletal disorders.BMC musculoskeletal disorders · 2025Article
- Research status, hotspots and perspectives of artificial intelligence applied to pain management: a bibliometric and visual analysis.Updates in surgery · 2025Article
- Effectiveness of Tailored Self-Management Interventions for People with Chronic Musculoskeletal Conditions: A Systematic Review and Meta-Analysis.Physiotherapy Canada. Physiotherapie Canada · 2025Article
- Effect of Telemedicine-Supported Structured Exercise Program in Patients with Chronic Low Back Pain: Study Protocol for a Randomized Controlled Trial.Journal of pain research · 2025Article
- 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
- Interest in mHealth Among Patients With Low Back Pain: Cross-Sectional Study.JMIR human factors · 2024Article
- Do age, gender, and education modify the effectiveness of app-delivered and tailored self-management support among adults with low back pain?-Secondary analysis of the selfBACK randomised controlled trial.PLOS digital health · 2023Article
- Exercise Prescription for the Work-Life Population and Beyond.Journal of functional morphology and kinesiology · 2023Review
- How to Prevent the Drop-Out: Understanding Why Adults Participate in Summative eHealth Evaluations.Journal of healthcare informatics research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLow back pain (LBP) is prevalent across all social classes, in all age groups, and across industrialized and developing countries. From a global perspective, LBP is considered the leading cause of disability and negatively impacts everyday life and well-being. Self-management is a recommended first-line treatment, and mobile apps are a promising platform to support self-management of conditions like LBP. In the selfBACK project, we have developed a digital decision support system made available for the user via an app intended to support tailored self-management of nonspecific LBP.
objectiveThe trial aims to evaluate the effectiveness of using the selfBACK app to support self-management in addition to usual care (intervention group) versus usual care only (control group) in people with nonspecific LBP.
methodsThis is a single-blinded, randomized controlled trial (RCT) with two parallel arms. The selfBACK app provides tailored self-management plans consisting of advice on physical activity, physical exercises, and educational content. Tailoring of plans is achieved by using case-based reasoning (CBR) methodology, which is a branch of artificial intelligence. The core of the CBR methodology is to use data about the current case (participant) along with knowledge about previous and similar cases to tailor the self-management plan to the current case. This enables a person-centered intervention based on what has and has not been successful in previous cases. Participants in the RCT are people with LBP who consulted a health care professional in primary care within the preceding 8 weeks. Participants are randomized to using the selfBACK app in addition to usual care versus usual care only. We aim to include a total of 350 participants (175 participants in each arm). Outcomes are collected at baseline, 6 weeks, and 3, 6, and 9 months. The primary end point is difference in pain-related disability between the intervention group and the control group assessed by the Roland-Morris Disability Questionnaire at 3 months.
resultsThe trial opened for recruitment in February 2019. Data collection is expected to be complete by fall 2020, and the results for the primary outcome are expected to be published in fall 2020.
conclusionsThis RCT will provide insights regarding the benefits of supporting tailored self-management of LBP through an app available at times convenient for the user. If successful, the intervention has the potential to become a model for the provision of tailored self-management support to people with nonspecific LBP and inform future interventions for other painful musculoskeletal conditions.
trial registrationClinicalTrial.gov NCT03798288; https://clinicaltrials.gov/ct2/show/NCT03798288. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/14720.
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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.