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dc.contributor.authorMarcuzzi, Anna
dc.contributor.authorKlevanger, Nina Elisabeth
dc.contributor.authorAasdahl, Lene
dc.contributor.authorGismervik, Sigmund Østgård
dc.contributor.authorBach, Kerstin
dc.contributor.authorMork, Paul Jarle
dc.contributor.authorNordstoga, Anne Lovise
dc.date.accessioned2024-08-29T08:35:46Z
dc.date.available2024-08-29T08:35:46Z
dc.date.created2024-08-05T15:20:46Z
dc.date.issued2024
dc.identifier.citationJMIR Hum Factors. 2024, 11, 1-13.en_US
dc.identifier.issn2292-9495
dc.identifier.urihttps://hdl.handle.net/11250/3149009
dc.description.abstractBackground: Self-management is endorsed in clinical practice guidelines for the care of musculoskeletal pain. In a randomized clinical trial, we tested the effectiveness of an artificial intelligence–based self-management app (selfBACK) as an adjunct to usual care for patients with low back and neck pain referred to specialist care. Objective: This study is a process evaluation aiming to explore patients’ engagement and experiences with the selfBACK app and specialist health care practitioners’ views on adopting digital self-management tools in their clinical practice. Methods: App usage analytics in the first 12 weeks were used to explore patients’ engagement with the SELFBACK app. Among the 99 patients allocated to the SELFBACK interventions, a purposive sample of 11 patients (aged 27-75 years, 8 female) was selected for semistructured individual interviews based on app usage. Two focus group interviews were conducted with specialist health care practitioners (n=9). Interviews were analyzed using thematic analysis. Results: Nearly one-third of patients never accessed the app, and one-third were low users. Three themes were identified from interviews with patients and health care practitioners: (1) overall impression of the app, where patients discussed the interface and content of the app, reported on usability issues, and described their app usage; (2) perceived value of the app, where patients and health care practitioners described the primary value of the app and its potential to supplement usual care; and (3) suggestions for future use, where patients and health care practitioners addressed aspects they believed would determine acceptance. Conclusions: Although the app’s uptake was relatively low, both patients and health care practitioners had a positive opinion about adopting an app-based self-management intervention for low back and neck pain as an add-on to usual care. Both described that the app could reassure patients by providing trustworthy information, thus empowering them to take actions on their own. Factors influencing app acceptance and engagement, such as content relevance, tailoring, trust, and usability properties, were identified. Trial Registration: ClinicalTrials.gov NCT04463043; https://clinicaltrials.gov/study/NCT04463043en_US
dc.language.isoengen_US
dc.publisherJMIR Publicationsen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleAn Artificial Intelligence–Based App for Self-Management of Low Back and Neck Pain in Specialist Care: Process Evaluation From a Randomized Clinical Trialen_US
dc.title.alternativeAn Artificial Intelligence–Based App for Self-Management of Low Back and Neck Pain in Specialist Care: Process Evaluation From a Randomized Clinical Trialen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber1-13en_US
dc.source.volume11en_US
dc.source.journalJMIR Human Factorsen_US
dc.identifier.doi10.2196/55716
dc.identifier.cristin2284493
dc.relation.projectEC/H2020/777090en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal