Evidence map›Paper›PMID 30030208›Full record

ArticleJMIR research protocols2018

A Decision Support System to Enhance Self-Management of Low Back Pain: Protocol for the selfBACK Project.

Paul Jarle Mork, Kerstin Bach, selfBACK Consortium

Erratum issued Registry-linked trialOpen access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
3.6field-weighted citation impact, top 7% of its field
1 · What the graph read from 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.

2 · The registry

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.

NCT03798288 nacompletednot on this mapstarted 2019, after this paper: background citation

A Decision Support System for Self-management of Low Back Pain - a Randomized Controlled Trial for the selfBACK Project

TypeinterventionalSponsorUniversity of Southern DenmarkRan2019 to 2020Enrolled461ConditionsLow Back PainArmsselfBACK, Usual care
3 · Its place in the literature

Who cites it

37 citing papers in PubMed, 67 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Paul Jarle Mork *Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway.ORCID 0000-0003-3355-2680
Kerstin BachDepartment of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.ORCID 0000-0002-6167-3079
selfBACK Consortium
Norwegian University of Science and Technology · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

case-based reasoningcomputer technologydigital healtheHealthexercisemachine learningmHealthphysical activityprimary caresmartphone

Identifiers

PMID30030208
PMCPMC6076372
OpenAlexW2801795794

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.