@文章{信息:doi/10.2196/20581，作者=“Sin, Jacqueline和Galeazzi, Gian和McGregor, Elicia和Collom, Jennifer和Taylor, Anna和Barrett, Barbara和Lawrence, Vanessa和Henderson, Claire”，标题=“筛查和治疗成人常见精神障碍或常见精神疾病症状的数字干预:系统评价和荟元分析”，期刊=“J Med Internet Res”，年=“2020”，月=“Sep”，日=“2”，卷=“22”，数=“9”，页=“e20581”，关键词=“eHealth”;移动健康;精神疾病;精神障碍;常见精神疾病;抑郁症;焦虑;背景:针对常见精神障碍(CMDs)或CMDs症状的数字干预措施正在迅速增长并越来越受欢迎，这可能是对CMDs患病率上升和早期求助和自我保健意识提高的回应。然而，以前没有发现针对这些新干预措施的系统综述。目的:本系统综述旨在涵盖完全基于网络的干预措施，为CMDs或阈下症状患者提供筛查和治疗指示，包括自我管理策略。 In addition, a meta-analysis was conducted to evaluate the effectiveness of these interventions for mental well-being and mental health outcomes. Methods: Ten electronic databases including MEDLINE, PsycINFO, and EMBASE were searched from January 1, 1999, to early April 2020. We included randomized controlled trials (RCTs) that evaluated a digital intervention (1) targeting adults with symptoms of CMDs, (2) providing both screening and signposting to other resources including self-care, and (3) delivered entirely through the internet. Intervention characteristics including target population, platform used, key design features, and outcome measure results were extracted and compared. Trial outcome results were included in a meta-analysis on the effectiveness of users' well-being and mental health outcomes. We also rated the meta-analysis results with the Grading of Recommendations, Assessment, Development, and Evaluations approach to establish the quality of the evidence. Results: The electronic searches yielded 21 papers describing 16 discrete digital interventions. These interventions were investigated in 19 unique trials including 1 (5{\%}) health economic study. Most studies were conducted in Australia and North America. The targeted populations varied from the general population to allied health professionals. All interventions offered algorithm-driven screening with measures to assess symptom levels and to assign treatment options including automatic web-based psychoeducation, self-care strategies, and signposting to existing services. A meta-analysis of usable trial data showed that digital interventions improved well-being (3 randomized controlled trials [RCTs]; n=1307; standardized mean difference [SMD] 0.40; 95{\%} CI 0.29 to 0.51; I2=28{\%}; fixed effect), symptoms of mental illness (6 RCTs; n=992; SMD −0.29; 95{\%} CI −0.49 to −0.09; I2=51{\%}; random effects), and work and social functioning (3 RCTs; n=795; SMD −0.16; 95{\%} CI −0.30 to −0.02; I2=0{\%}; fixed effect) compared with waitlist or attention control. However, some follow-up data failed to show any sustained effects beyond the post intervention time point. Data on mechanisms of change and cost-effectiveness were also lacking, precluding further analysis. Conclusions: Digital mental health interventions to assess and signpost people experiencing symptoms of CMDs appear to be acceptable to a sufficient number of people and appear to have enough evidence for effectiveness to warrant further study. We recommend that future studies incorporate economic analysis and process evaluation to assess the mechanisms of action and cost-effectiveness to aid scaling of the implementation. ", issn="1438-8871", doi="10.2196/20581", url="//www.mybigtv.com/2020/9/e20581", url="https://doi.org/10.2196/20581", url="http://www.ncbi.nlm.nih.gov/pubmed/32876577" }