Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia.
Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia; Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia.
Prev Med. 2021 Jul;148:106532. doi: 10.1016/j.ypmed.2021.106532. Epub 2021 Mar 24.
Given that the one-size-fits-all approach to mobile health interventions have limited effects, a personalized approach might be necessary to promote healthy behaviors and prevent chronic conditions. Our systematic review aims to evaluate the effectiveness of personalized mobile interventions on lifestyle behaviors (i.e., physical activity, diet, smoking and alcohol consumption), and identify the effective key features of such interventions. We included any experimental trials that tested a personalized mobile app or fitness tracker and reported any lifestyle behavior measures. We conducted a narrative synthesis for all studies, and a meta-analysis of randomized controlled trials. Thirty-nine articles describing 31 interventions were included (n = 77,243, 64% women). All interventions personalized content and rarely personalized other features. Source of data included system-captured (12 interventions), user-reported (11 interventions) or both (8 interventions). The meta-analysis showed a moderate positive effect on lifestyle behavior outcomes (standardized difference in means [SDM] 0.663, 95% CI 0.228 to 1.10). A meta-regression model including source of data found that interventions that used system-captured data for personalization were associated with higher effectiveness than those that used user-reported data (SDM 1.48, 95% CI 0.76 to 2.19). In summary, the field is in its infancy, with preliminary evidence of the potential efficacy of personalization in improving lifestyle behaviors. Source of data for personalization might be important in determining intervention effectiveness. To fully exploit the potential of personalization, future high-quality studies should investigate the integration of multiple data from different sources and include personalized features other than content.
鉴于一刀切的移动健康干预方法效果有限,可能需要个性化方法来促进健康行为和预防慢性疾病。我们的系统综述旨在评估个性化移动干预措施对生活方式行为(即身体活动、饮食、吸烟和饮酒)的有效性,并确定此类干预措施的有效关键特征。我们纳入了任何测试个性化移动应用程序或健身追踪器的实验性试验,并报告了任何生活方式行为措施。我们对所有研究进行了叙述性综合分析,并对随机对照试验进行了荟萃分析。有 39 篇文章描述了 31 项干预措施(n=77243,64%为女性)。所有干预措施都个性化了内容,很少个性化其他特征。数据来源包括系统捕获(12 项干预措施)、用户报告(11 项干预措施)或两者兼有(8 项干预措施)。荟萃分析显示,生活方式行为结果有适度的积极影响(标准化均数差 [SDM] 0.663,95%置信区间 0.228 至 1.10)。包括数据来源的元回归模型发现,使用系统捕获数据进行个性化的干预措施与更高的有效性相关,而使用用户报告数据的干预措施则不然(SDM 1.48,95%置信区间 0.76 至 2.19)。总之,该领域仍处于起步阶段,有初步证据表明个性化在改善生活方式行为方面具有潜在的功效。个性化的数据来源对于确定干预措施的有效性可能很重要。为了充分发挥个性化的潜力,未来应进行高质量的研究,调查来自不同来源的多种数据的整合,并纳入除内容以外的个性化特征。