Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, Glebe, Australia.
J Med Internet Res. 2021 Feb 17;23(2):e24607. doi: 10.2196/24607.
Mobile health (mHealth) apps offer a scalable option for treating sleep disturbances at a population level. However, there is a lack of clarity about the development and evaluation of evidence-based mHealth apps.
The aim of this systematic review was to provide evidence for the design engineering and clinical implementation and evaluation of mHealth apps for sleep disturbance.
A systematic search of studies published from the inception of databases through February 2020 was conducted using 5 databases (MEDLINE, Embase, Cochrane Library, PsycINFO, and CINAHL).
A total of 6015 papers were identified using the search strategy. After screening, 15 papers were identified that examined the design engineering and clinical implementation and evaluation of 8 different mHealth apps for sleep disturbance. Most of these apps delivered cognitive behavioral therapy for insomnia (CBT-I, n=4) or modified CBT-I (n=2). Half of the apps (n=4) identified adopting user-centered design or multidisciplinary teams in their design approach. Only 3 papers described user and data privacy. End-user acceptability and engagement were the most frequently assessed implementation metrics. Only 1 app had available evidence assessing all 4 implementation metrics (ie, acceptability, engagement, usability, and adherence). Most apps were prototype versions (n=5), with few matured apps. A total of 6 apps had supporting papers that provided a quantitative evaluation of clinical outcomes, but only 1 app had a supporting, adequately powered randomized controlled trial.
This is the first systematic review to synthesize and examine evidence for the design engineering and clinical implementation and evaluation of mHealth apps for sleep disturbance. The minimal number of apps with published evidence for design engineering and clinical implementation and evaluation contrasts starkly with the number of commercial sleep apps available. Moreover, there appears to be no standardization and consistency in the use of best practice design approaches and implementation assessments, along with very few rigorous efficacy evaluations. To facilitate the development of successful and evidence-based apps for sleep disturbance, we developed a high-level framework to guide researchers and app developers in the end-to-end process of app development and evaluation.
移动健康 (mHealth) 应用程序为在人群层面上治疗睡眠障碍提供了一种可扩展的选择。然而，关于基于证据的 mHealth 应用程序的开发和评估还缺乏明确性。
本系统评价旨在为睡眠障碍的 mHealth 应用程序的设计工程以及临床实施和评估提供证据。
通过使用 5 个数据库（MEDLINE、Embase、Cochrane 图书馆、PsycINFO 和 CINAHL），对从数据库创建开始到 2020 年 2 月发表的研究进行了系统搜索。
使用搜索策略共确定了 6015 篇论文。经过筛选，确定了 15 篇论文，这些论文检查了 8 种不同的用于睡眠障碍的 mHealth 应用程序的设计工程以及临床实施和评估。这些应用程序大多提供认知行为疗法治疗失眠症 (CBT-I，n=4) 或改良 CBT-I (n=2)。一半的应用程序 (n=4) 确定在其设计方法中采用以用户为中心的设计或多学科团队。只有 3 篇论文描述了用户和数据隐私。最终用户的可接受性和参与度是评估实施的最常用指标。只有 1 个应用程序有可用的证据评估所有 4 个实施指标（即，可接受性、参与度、可用性和依从性）。大多数应用程序是原型版本 (n=5)，成熟的应用程序很少。共有 6 个应用程序有提供临床结果定量评估的支持论文，但只有 1 个应用程序有支持的、充分的随机对照试验。
这是第一项系统评价，综合并检查了用于睡眠障碍的 mHealth 应用程序的设计工程以及临床实施和评估的证据。具有发表的设计工程和临床实施以及评估证据的应用程序数量非常少，与可用于商业的睡眠应用程序数量形成鲜明对比。此外，在使用最佳实践设计方法和实施评估方面似乎没有标准化和一致性，并且很少有严格的功效评估。为了促进睡眠障碍的成功和基于证据的应用程序的开发，我们开发了一个高级框架，以指导研究人员和应用程序开发人员完成应用程序开发和评估的端到端过程。
