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Effectiveness of app-delivered, tailored self-management support for adults with lower back pain-related disability: a selfBACK randomized clinical trial. [Dataset]

Contributors

Louise Fleng Sandal
Data Collector

Cecilie K. Øverås
Data Collector

Kerstin Bach
Data Collector

Abstract

SELFBACK is an evidence-based decision support system that supports self-management of nonspecific low back pain. In specific, SELFBACK provides the user with evidence-based advice on physical activity level, strength/ flexibility exercises, and educational content. The self-management advice is delivered via a smartphone app and individually tailored to the user’s personal goals, personal characteristics, symptom progression and functional level. The SELFBACK system uses the case-based reasoning (CBR) methodology to capture and reuse knowledge from successful previous cases to suggest the most suitable self-management plan for a current user. Figure 1 illustrates the architecture of the SELFBACK system and the process for producing and tailoring the weekly self-management plans (steps 1-5). In the current trial, patients with low back pain were referred to the research project from their primary care clinician (general practitioner, physiotherapist, chiropractor) or an outpatient spine clinic. The patient was screened for eligibility by a research assistant and if eligible, invited to the trial and sent a link to an online web-based questionnaire (step 1). The questionnaire information was used to create a user profile (step 2), initiate the first CBR cycle (i.e., matching of the current case with the most similar and successful previous case in the SELFBACK case-base), and produce the first weekly self-management plan. The resulting self-management plan is pushed to the mobile phone (step 3) and accessed by the user (step 4). On a weekly basis, the users answered a set of tailoring questions in the app (eg, pain intensity, self-efficacy level, fear-avoidance level, barriers to self-management etc.). In addition, physical activity was tracked by a step detecting wristband (Mi Band 3, Xiaomi) connected to the SELFBACK app. The self-reported data and the objective physical activity data for the past week was then fed back to the CBR system (step 5) where the refined and enhanced user profile was matched with the most similar and successful case in the case-base to create and tailor the next weekly self-management plan. This supplementary material has been provided by the authors to give readers additional information about their work.

Citation

SANDAL, L.F., BACH, K., ØVERÅS, C.K., WIRATUNGA, N., COOPER, K, et al. [2021]. Effectiveness of app-delivered, tailored self-management support for adults with lower back pain-related disability: a selfBACK randomized clinical trial. [Dataset]. JAMA internal medicine [online], Online First. Available from: https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2782459#supplemental-tab

Acceptance Date Jun 11, 2021
Online Publication Date Aug 2, 2021
Deposit Date Aug 5, 2021
Publicly Available Date Aug 5, 2021
Publisher American Medical Association (AMA)
DOI https://doi.org/10.1001/jamainternmed.2021.4097
Keywords SelfBACK; Evidence-based; Self-management support system; Lower back pain (LBP); Smartphone app
Public URL https://rgu-repository.worktribe.com/output/1400289
Related Public URLs https://rgu-repository.worktribe.com/output/1400226
Type of Data PDF file.
Collection Date Jul 1, 2021
Collection Method The full methodology is described in a published journal article: SANDAL, L.F., BACK, K., ØVERÅS, C.K., WIRATUNGA, N., COOPER, K, et al. [2021]. Effectiveness of app-delivered, tailored self-management support for adults with lower back pain-related disability: a selfBACK randomized clinical trial. JAMA internal medicine [online], Online First. Available from: https://doi.org/10.1001/jamainternmed.2021.4097