An Artificial Intelligence-Based App for Self-Management of Low Back and Neck Pain in Specialist Care: Process Evaluation From a Randomized Clinical Trial.

IF 2.6 Q2 HEALTH CARE SCIENCES & SERVICES
JMIR Human Factors Pub Date : 2024-07-09 DOI:10.2196/55716
Anna Marcuzzi, Nina Elisabeth Klevanger, Lene Aasdahl, Sigmund Gismervik, Kerstin Bach, Paul Jarle Mork, Anne Lovise Nordstoga
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引用次数: 0

Abstract

Background: 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/NCT04463043.

基于人工智能的专科护理中腰颈疼痛自我管理应用程序:随机临床试验的过程评估。
背景:肌肉骨骼疼痛护理临床实践指南认可自我管理。在一项随机临床试验中,我们测试了基于人工智能的自我管理应用程序(selfBACK)作为常规护理的辅助手段对转诊至专科的腰背痛和颈椎痛患者的疗效:本研究是一项过程评估,旨在探索患者对selfBACK应用的参与度和体验,以及专科医生对在临床实践中采用数字自我管理工具的看法:方法:利用前12周的应用程序使用分析来探讨患者对SELFBACK应用程序的使用情况。在分配给SELFBACK干预措施的99名患者中,根据应用程序的使用情况,有目的性地抽取了11名患者(27-75岁,8名女性)进行半结构化个人访谈。此外,还与专业医护人员(9 人)进行了两次焦点小组访谈。访谈采用主题分析法进行分析:近三分之一的患者从未使用过应用程序,三分之一的患者使用率较低。从与患者和医疗从业人员的访谈中确定了三个主题:(1)对应用程序的总体印象,患者讨论了应用程序的界面和内容,报告了可用性问题,并描述了他们的应用程序使用情况;(2)应用程序的感知价值,患者和医疗从业人员描述了应用程序的主要价值及其补充常规护理的潜力;以及(3)对未来使用的建议,患者和医疗从业人员讨论了他们认为将决定接受程度的各个方面:尽管该应用程序的使用率相对较低,但患者和医护人员都对采用基于应用程序的自我管理干预措施来治疗腰背痛和颈椎痛并将其作为常规护理的补充持积极态度。他们都认为,应用程序可以通过提供值得信赖的信息让患者放心,从而让他们有能力自己采取行动。研究确定了影响应用接受度和参与度的因素,如内容相关性、量身定制、信任度和可用性等:试验注册:ClinicalTrials.gov NCT04463043;https://clinicaltrials.gov/study/NCT04463043。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JMIR Human Factors
JMIR Human Factors Medicine-Health Informatics
CiteScore
3.40
自引率
3.70%
发文量
123
审稿时长
12 weeks
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