心理干预
荟萃分析
医学
个性化
行为改变方法
行为改变
随机对照试验
系统回顾
健康
干预(咨询)
梅德林
计算机科学
护理部
万维网
外科
法学
病理
内科学
政治学
作者
Huong Ly Tong,Juan C. Quiroz,A. Baki Kocaballı,Sandrine Chan Moi Fat,Kim Phuong Dao,Holly Gehringer,Clara K Chow,Liliana Laranjo
标识
DOI:10.1016/j.ypmed.2021.106532
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI