Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.
Health Information Management and Technology Department, College of Public Health, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
JMIR Mhealth Uhealth. 2023 Feb 27;11:e43561. doi: 10.2196/43561.
BACKGROUND: Mobile health (mHealth) apps have great potential to support the management of chronic conditions. Despite widespread acceptance of mHealth apps by the public, health care providers (HCPs) are reluctant to prescribe or recommend such apps to their patients. OBJECTIVE: This study aimed to classify and evaluate interventions aimed at encouraging HCPs to prescribe mHealth apps. METHODS: A systematic literature search was conducted to identify studies published from January 1, 2008, to August 5, 2022, using 4 electronic databases: MEDLINE, Scopus, CINAHL, and PsycINFO. We included studies that evaluated interventions encouraging HCPs to prescribe mHealth apps. Two review authors independently assessed the eligibility of the studies. The "National Institute of Health's quality assessment tool for before-and-after (pretest-posttest design) studies with no control group" and "the mixed methods appraisal tool (MMAT)" were used to assess the methodological quality. Owing to high levels of heterogeneity between interventions, measures of practice change, specialties of HCPs, and modes of delivery, we conducted a qualitative analysis. We adopted the behavior change wheel as a framework for classifying the included interventions according to intervention functions. RESULTS: In total, 11 studies were included in this review. Most of the studies reported positive findings, with improvements in a number of outcomes, including increased knowledge of mHealth apps among clinicians, improved self-efficacy or confidence in prescribing, and an increased number of mHealth app prescriptions. On the basis of the behavior change wheel, 9 studies reported elements of environmental restructuring such as providing HCPs with lists of apps, technological systems, time, and resources. Furthermore, 9 studies included elements of education, particularly workshops, class lectures, individual sessions with HCPs, videos, or toolkits. Furthermore, training was incorporated in 8 studies using case studies or scenarios or app appraisal tools. Coercion and restriction were not reported in any of the interventions included. The quality of the studies was high in relation to the clarity of aims, interventions, and outcomes but weaker in terms of sample size, power calculations, and duration of follow-up. CONCLUSIONS: This study identified interventions to encourage app prescriptions by HCPs. Recommendations for future research should consider previously unexplored intervention functions such as restrictions and coercion. The findings of this review can help inform mHealth providers and policy makers regarding the key intervention strategies impacting mHealth prescriptions and assist them in making informed decisions to encourage this adoption.
背景:移动医疗(mHealth)应用具有很大的潜力来支持慢性病的管理。尽管公众广泛接受了 mHealth 应用,但医疗保健提供者(HCP)不愿意为患者开处方或推荐此类应用。
目的:本研究旨在对鼓励 HCP 开 mHealth 应用的干预措施进行分类和评估。
方法:系统地检索了 4 个电子数据库:MEDLINE、Scopus、CINAHL 和 PsycINFO,以确定从 2008 年 1 月 1 日至 2022 年 8 月 5 日发表的研究。我们纳入了评估鼓励 HCP 开 mHealth 应用的干预措施的研究。两位综述作者独立评估了研究的合格性。使用“国家卫生研究院前后(无对照组的预-后测试设计)研究质量评估工具”和“混合方法评估工具(MMAT)”来评估方法学质量。由于干预措施之间存在高度的异质性、实践变化的措施、HCP 的专业以及传递模式,我们进行了定性分析。我们采用行为改变轮作为框架,根据干预功能对纳入的干预措施进行分类。
结果:共有 11 项研究纳入本综述。大多数研究报告了积极的结果,包括提高了临床医生对 mHealth 应用的了解、提高了自我效能或开处方的信心、增加了 mHealth 应用的处方数量等。基于行为改变轮,9 项研究报告了环境重构的元素,例如为 HCP 提供应用程序列表、技术系统、时间和资源。此外,9 项研究包括教育元素,特别是讲习班、课堂讲座、与 HCP 的个别会议、视频或工具包。此外,在 8 项研究中都纳入了培训,使用案例研究或场景或应用程序评估工具。在纳入的干预措施中没有报告强制和限制。研究的质量在目标、干预措施和结果的清晰度方面很高,但在样本量、功效计算和随访时间方面较弱。
结论:本研究确定了鼓励 HCP 开处方的干预措施。对未来研究的建议应考虑以前未探索过的干预功能,如限制和强制。本研究的结果可以帮助 mHealth 提供者和政策制定者了解影响 mHealth 处方的关键干预策略,并帮助他们做出明智的决策,以鼓励这种采用。
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