@Article{info:doi/10.2196/41566，作者=“Webb, Christian A and Hirshberg, Matthew J and Davidson, Richard J and Goldberg, Simon B”，标题=“智能手机冥想训练反应的个性化预测:随机对照试验”，期刊=“J Med Internet Res”，年=“2022”，月=“11”，日=“8”，卷=“24”，数=“11”，页=“e41566”，关键词=“精准医学”;预测;机器学习;冥想;移动技术;智能手机应用;背景:近年来，冥想应用程序越来越受欢迎，越来越多的人转向这些应用程序来应对压力，包括在2019冠状病毒病大流行期间。冥想应用是治疗抑郁和焦虑最常用的心理健康应用。然而，很少有人知道谁更适合使用这些应用程序。目的:本研究旨在开发和测试一种数据驱动的算法，以预测哪些人最有可能从基于应用程序的冥想训练中受益。 Methods: Using randomized controlled trial data comparing a 4-week meditation app (Healthy Minds Program [HMP]) with an assessment-only control condition in school system employees (n=662), we developed an algorithm to predict who is most likely to benefit from HMP. Baseline clinical and demographic characteristics were submitted to a machine learning model to develop a ``Personalized Advantage Index'' (PAI) reflecting an individual's expected reduction in distress (primary outcome) from HMP versus control. Results: A significant group {\texttimes} PAI interaction emerged (t658=3.30; P=.001), indicating that PAI scores moderated group differences in outcomes. A regression model that included repetitive negative thinking as the sole baseline predictor performed comparably well. Finally, we demonstrate the translation of a predictive model into personalized recommendations of expected benefit. Conclusions: Overall, the results revealed the potential of a data-driven algorithm to inform which individuals are most likely to benefit from a meditation app. Such an algorithm could be used to objectively communicate expected benefits to individuals, allowing them to make more informed decisions about whether a meditation app is appropriate for them. Trial Registration: ClinicalTrials.gov NCT04426318; https://clinicaltrials.gov/ct2/show/NCT04426318 ", issn="1438-8871", doi="10.2196/41566", url="//www.mybigtv.com/2022/11/e41566", url="https://doi.org/10.2196/41566", url="http://www.ncbi.nlm.nih.gov/pubmed/36346668" }