ArticleJMIR research protocols2018
A Decision Support System to Enhance Self-Management of Low Back Pain: Protocol for the selfBACK Project.
Article in JMIR research protocols, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT03798288 (A Decision Support System for Self-management of Low Back Pain - a Randomized Controlled Trial for the selfBACK Project), which is not on this map. Cited by 37 papers.
What it found
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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
Who cites it
37 citing papers in PubMed, 67 citations in OpenAlex.
- Trial
- 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
- 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
- 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
- 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.JAMA network open · 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
- Evaluation of the Effect of Patient Education and Strengthening Exercise Therapy Using a Mobile Messaging App on Work Productivity in Japanese Patients With Chronic Low Back Pain: Open-Label, Randomized, Parallel-Group Trial.JMIR mHealth and uHealth · 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
- Article
- Translation, Cultural Adaptation, and Mixed-Methods User Feedback of the SelfBack App to the Arabic Language for Patients With Low Back Pain: Pilot Mixed-Methods Study.JMIR formative research · 2025Article
- Research status, hotspots and perspectives of artificial intelligence applied to pain management: a bibliometric and visual analysis.Updates in surgery · 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
- Development and Use of Mobile Messaging for Individuals With Musculoskeletal Pain Conditions: Scoping Review.JMIR mHealth and uHealth · 2024Article
- mHealth Apps for the Self-Management of Low Back Pain: Systematic Search in App Stores and Content Analysis.JMIR mHealth and uHealth · 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
- Article
- How to Prevent the Drop-Out: Understanding Why Adults Participate in Summative eHealth Evaluations.Journal of healthcare informatics research · 2023Article
- A clinical decision support system in back pain helps to find the diagnosis: a prospective correlation study.Archives of orthopaedic and trauma surgery · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLow back pain (LBP) is a leading cause of disability worldwide. Most patients with LBP encountered in primary care settings have nonspecific LBP, that is, pain with an unknown pathoanatomical cause. Self-management in the form of physical activity and strength and flexibility exercises along with patient education constitute the core components of the management of nonspecific LBP. However, the adherence to a self-management program is challenging for most patients, especially without feedback and reinforcement. Here we outline a protocol for the design and implementation of a decision support system (DSS), selfBACK, to be used by patients themselves to promote self-management of LBP.
objectiveThe main objective of the selfBACK project is to improve self-management of nonspecific LBP to prevent chronicity, recurrence and pain-related disability. This is achieved by utilizing computer technology to develop personalized self-management plans based on individual patient data.
methodsThe decision support is conveyed to patients via a mobile phone app in the form of advice for self-management. Case-based reasoning (CBR), a technology that utilizes knowledge about previous cases along with data about the current patient case, is used to tailor the advice to the current patient, enabling a patient-centered intervention based on what has and has not been successful in previous patient cases. The data source for the CBR system comprises initial patient data collected by a Web-based questionnaire, weekly patient reports (eg, symptom progression), and a physical activity-detecting wristband. The effectiveness of the selfBACK DSS will be evaluated in a multinational, randomized controlled trial (RCT), targeting care-seeking patients with nonspecific LBP. A process evaluation will be carried out as an integral part of the RCT to document the implementation and patient experiences with selfBACK.
resultsThe selfBACK project was launched in January 2016 and will run until the end of 2020. The final version of the selfBACK DSS will be completed in 2018. The RCT will commence in February 2019 with pain-related disability at 3 months as the primary outcome. The trial results will be reported according to the CONSORT statement and the extended CONSORT-EHEALTH checklist. Exploitation of the results will be ongoing throughout the project period based on a business plan developed by the selfBACK consortium. Tailored digital support has been proposed as a promising approach to improve self-management of chronic disease. However, tailoring self-management advice according to the needs, motivation, symptoms, and progress of individual patients is a challenging task. Here we outline a protocol for the design and implementation of a stand-alone DSS based on the CBR technology with the potential to improve self-management of nonspecific LBP.
conclusionsThe selfBACK project will provide learning regarding the implementation and effectiveness of an app-based DSS for patients with nonspecific LBP. REGISTERED REPORT IDENTIFIER: RR1-10.2196/9379.
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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.