A digital decision support system (selfBACK) for improved self-management of low back pain: a pilot study with 6-week follow-up

Louise Fleng Sandal, Cecilie K Øverås, Anne Lovise Nordstoga, Karen Wood, Kerstin Bach, Jan Hartvigsen, Karen Søgaard, Paul Jarle Mork, Louise Fleng Sandal, Cecilie K Øverås, Anne Lovise Nordstoga, Karen Wood, Kerstin Bach, Jan Hartvigsen, Karen Søgaard, Paul Jarle Mork

Abstract

Background: Very few of the publicly available apps directed towards self-management of low back pain (LBP) have been rigorously tested and their theoretical underpinnings seldom described. The selfBACK app was developed in collaboration with end-users and clinicians and its content is supported by best evidence on self-management of LBP. The objectives of this pilot study were to investigate the basis for recruitment and screening procedures for the subsequent randomized controlled trial (RCT), to test the inclusion process in relation to questionnaires and app installation, and finally to investigate the change in primary outcome over time.

Methods: This single-armed pilot study enrolled 51 participants who had sought help for LBP of any duration from primary care (physiotherapy, chiropractic, or general practice) within the past 8 weeks. Participants were screened for eligibility using the PROMIS-Physical-Function-4a questionnaire. Participants were asked to use the selfBACK app for 6 weeks. The app provided weekly tailored self-management plans targeting physical activity, strength and flexibility exercises, and education. The construction of the self-management plans was achieved using case-based reasoning (CBR) methodology to capture and reuse information from previous successful cases. Participants completed the primary outcome pain-related disability (Roland-Morris Disability Questionnaire [RMDQ]) at baseline and 6-week follow-up along with a range of secondary outcomes. Metrics of app use were collected throughout the intervention period.

Results: Follow-up data at 6 weeks was obtained for 43 participants. The recruitment procedures were feasible, and the number needed to screen was acceptable (i.e., 1.6:1). The screening questionnaire was altered during the pilot study. The inclusion process, answering questionnaires and app installation, were feasible. The primary outcome (RMDQ) improved from 8.6 (SD 5.1) at baseline to 5.9 (SD 4.0) at 6-week follow-up (change score 1.8, 95% CI 0.7 to 2.9). Participants spent on average 134 min (range 0-889 min) using the app during the 6-week period.

Conclusion: The recruitment, screening, and inclusion procedures were feasible for the subsequent RCT with a small adjustment. The improvement on the RMDQ from baseline to follow-up was small. Time pattern of app usage varied considerably between the participants.

Trial registration: NCT03697759. Registered on August 10, 2018. https://ichgcp.net/clinical-trials-registry/NCT03697759.

Keywords: App; Artificial intelligence; Case-based reasoning; Low back pain; Recommender system; Self-management; mHealth.

Conflict of interest statement

Competing interestsThe overall aim of the selfBACK project is to develop a digital DSS and mobile app to support participants to self-manage their LBP. The results and experiences from the pilot and RCT will inform the further development of the app, which may be introduced into a commercial market.

© The Author(s) 2020.

Figures

Fig. 1
Fig. 1
Screenshot of the selfBACK app plan screen showing the three main components of a weekly self-management plan
Fig. 2
Fig. 2
Flow of the participants throughout the study. LBP low back pain
Fig. 3
Fig. 3
Box-plot showing the completion levels for physical activity (i.e., no. of steps), exercise, and education. Physical activity is shown as percentage completion of the daily step goal (e.g., 130% equals goal achieved plus 30% additional steps). Strength and flexibility exercises are shown as exercise volume (e.g., 100% equals 3 weekly exercise sessions of any duration). Education is shown as the percentage of the suggested educational messages that have been read (e.g., 100% means that all the 7 messages for a week have been read)

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