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An app-delivered self-management program for people with low back pain: protocol for the selfBACK randomized controlled trial. [Protocol]

Sandal, Louise Fleng; Stochkendahl, Mette Jensen; Svendsen, Malene Jagd; Wood, Karen; �veras, Cecilie K.; Nordstoga, Anne Lovise; Villumsen, Morten; Rasmussen, Charlotte Diana N�rregaard; Nicholl, Barbara; Cooper, Kay; Kjaer, Per; Mair, Frances S.; Sj�gaard, Gisela; Nilsen, Tom Ivar Lund; Hartvigsen, Jan; Bach, Kerstin; Mork, Paul Jarle; S�gaard, Karen


Louise Fleng Sandal

Mette Jensen Stochkendahl

Malene Jagd Svendsen

Karen Wood

Cecilie K. �veras

Anne Lovise Nordstoga

Morten Villumsen

Charlotte Diana N�rregaard Rasmussen

Barbara Nicholl

Per Kjaer

Frances S. Mair

Gisela Sj�gaard

Tom Ivar Lund Nilsen

Jan Hartvigsen

Kerstin Bach

Paul Jarle Mork

Karen S�gaard


Low 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. The 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. This 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. The 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. This 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 Registration: NCT03798288; International Registered Report Identifier (IRRID): DERR1-10.2196/14720.


SANDAL, L.F., STOCHKENDAHL, M.J., SVENDSEN, M.J., COOPER, K., et al. 2019. An app-delivered self-management program for people with low back pain: protocol for the selfback randomized controlled trial. [Protocol]. JMIR research protocols [online], 8(12), e14720. Available from:

Other Type Experiment
Acceptance Date Aug 31, 2019
Online Publication Date Dec 3, 2019
Publication Date Dec 31, 2019
Deposit Date Jan 6, 2020
Publicly Available Date Jan 6, 2020
Publisher JMIR Publications
Keywords Low back pain; Self-management; Case-based reasoning; eHealth; mHealth; App; Decision support system
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Related Public URLs (Journal article)
Additional Information A pre-print version of this paper was first published 18.05.2019 and is available on Preprints ( ) and JMIR Preprints ( ).


SANDAL 2019 An app-delivered [PROTOCOL] (273 Kb)

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© Louise Fleng Fleng Sandal, Mette Jensen Stochkendahl, Malene Jagd Svendsen, Karen Wood, Cecilie K Øverås, Anne Lovise Nordstoga, Morten Villumsen, Charlotte Diana Nørregaard Rasmussen, Barbara Nicholl, Kay Cooper, Per Kjaer, Frances S Mair, Gisela Sjøgaard, Tom Ivar Lund Nilsen, Jan Hartvigsen, Kerstin Bach, Paul Jarle Mork, Karen Søgaard.

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