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Cited by 75Musculoskeletal pain and rehabilitationMobile Health and mHealth ApplicationsDigital Mental Health Interventions

Effect of an Artificial Intelligence–Based Self-Management App on Musculoskeletal Health in Patients With Neck and/or Low Back Pain Referred to Specialist Care: A Randomized Clinical Trial

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Marcuzzi, Anna, Nordstoga, Anne Lovise, Bach, Kerstin, Aasdahl, Lene, Nilsen, Tom Ivar Lund, Bardal, Ellen Marie, Boldermo, Nora Østbø, Falkener Bertheussen, Gro +3 more·2023·JAMA Network Open
DOI 10.1001/jamanetworkopen.2023.20400PMC PMC10300712
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Abstract

Importance: Self-management is a key element in the care of persistent neck and low back pain. Individually tailored self-management support delivered via a smartphone app in a specialist care setting has not been tested. Objective: To determine the effect of individually tailored self-management support delivered via an artificial intelligence–based app (SELFBACK) adjunct to usual care vs usual care alone or nontailored web-based self-management support (e-Help) on musculoskeletal health. Design, Setting, and Participants: This randomized clinical trial recruited adults 18 years or older with neck and/or low back pain who had been referred to and accepted on a waiting list for specialist care at a multidisciplinary hospital outpatient clinic for back, neck, and shoulder rehabilitation. Participants were enrolled from July 9, 2020, to April 29, 2021. Of 377 patients assessed for eligibility, 76 did not complete the baseline questionnaire, and 7 did not meet the eligibility criteria (ie, did not own a smartphone, were unable to take part in exercise, or had language barriers); the remaining 294 patients were included in the study and randomized to 3 parallel groups, with follow-up of 6 months. Interventions: Participants were randomly assigned to receive app-based individually tailored self-management support in addition to usual care (app group), web-based nontailored self-management support in addition to usual care (e-Help group), or usual care alone (usual care group). Main Outcomes and Measures: The primary outcome was change in musculoskeletal health measured by the Musculoskeletal Health Questionnaire (MSK-HQ) at 3 months. Secondary outcomes included change in musculoskeletal health measured by the MSK-HQ at 6 weeks and 6 months and pain-related disability, pain intensity, pain-related cognition, and health-related quality of life at 6 weeks, 3 months, and 6 months. Results: Among 294 participants (mean [SD] age, 50.6 [14.9] years; 173 women [58.8%]), 99 were randomized to the app group, 98 to the e-Help group, and 97 to the usual care group. At 3 months, 243 participants (82.7%) had complete data on the primary outcome. In the intention-to-treat analysis at 3 months, the adjusted mean difference in MSK-HQ score between the app and usual care groups was 0.62 points (95% CI, −1.66 to 2.90 points; P = .60). The adjusted mean difference between the app and e-Help groups was 1.08 points (95% CI, −1.24 to 3.41 points; P = .36). Conclusions and Relevance: In this randomized clinical trial, individually tailored self-management support delivered via an artificial intelligence–based app adjunct to usual care was not significantly more effective in improving musculoskeletal health than usual care alone or web-based nontailored self-management support in patients with neck and/or low back pain referred to specialist care. Further research is needed to investigate the utility of implementing digitally supported self-management interventions in the specialist care setting and to identify instruments that capture changes in self-management behavior. Trial Registration: ClinicalTrials.gov Identifier: NCT04463043

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  • AI can help patients with musculoskeletal pain improve their physical activity, posture, and coping skills using a smartphone app that adapts to their individual needs and preferences.

    “AI can help patients with musculoskeletal pain, such as neck or lower back pain, to improve their physical activity, posture, and coping skills using a smartphone app that adapts to their individual needs and preferences. 65”

    From Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews (2024) · cites this paper as [65]

    The Methods (Intervention and Control Conditions; page unavailable) describe an AI-based smartphone app that tailors physical-activity and self-management recommendations using patient-reported information. They do not describe posture training, and the Results (Secondary Outcomes; page unavailable) do not show improved coping-related confidence: no secondary outcome differed from usual care at three months except global perceived effect. The claim therefore describes the app’s intended support as though all three improvements were demonstrated. (LLM, conf=0.88)

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Cited by

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Authors
Marcuzzi, A., Nordstoga, A. L., Bach, K., Aasdahl, L., Nilsen, T. I. L., Bardal, E. M., Boldermo, N. Ø., Falkener Bertheussen, G., Marchand, G. H., Gismervik, S., & Mork, P. J.
Affiliations
  • Norwegian University of Science and Technology (NO)
  • St Olav's University Hospital (NO)
  • Department of Physical Medicine and Rehabilitation, St Olavs Hospital, Trondheim, Norway
Corresponding author
Mork, Paul Jarle
ORCID
Venue
JAMA Network Open
Publisher
American Medical Association (AMA)
Published
2023-06-27
Volume
6
Issue
6
Pages
e2320400
ISSN
2574-3805 (ISSN-L)
DOI
10.1001/jamanetworkopen.2023.20400
PMC
PMC10300712
OpenAlex
W4382181238
Cited by
75
References
8
Citations by year
2026: 25 · 2025: 21 · 2024: 28 · 2023: 1
FWCI
11.68
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JAMA Network Open (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
Funders
European Commission (689043, HORIZON2020, 777090)
Topics
  • Health Sciences › Medicine › Pharmacology › Musculoskeletal pain and rehabilitation
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
Keywords
neck pain, low back pain, self-management, artificial intelligence, mobile application, musculoskeletal health, pain-related disability, pain-related cognitions, health-related quality of life, multidisciplinary rehabilitation, chronic pain management
MeSH terms
Self-Management, Adult, Artificial Intelligence, Female, Humans, Middle Aged, Quality of Life, Low Back Pain, Mobile Applications

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