%0期刊文章%@ 1438- 8871% I JMIR出版物%V 24卡塔尔世界杯8强波胆分析% N 11% P e41566 %T对智能手机提供的冥想训练的响应的个性化预测:随机对照试验Webb,Christian A Hirshberg,Matthew J %A Davidson,Richard J %A Goldberg,Simon B +威斯康辛大学麦迪逊分校咨询心理学系，教育大楼315,1000 Bascom Mall, Madison, WI, 53706，美国，1 608 265 8986，sbgoldberg@wisc.edu %K精准医疗%K预测%K机器学习%K冥想%K移动技术%K智能手机应用%K手机%D 2022 %7 8.11.2022 %9原创paperetar %J J医学互联网Res %G英语%X背景:近年来，冥想应用程序人气飙升，越来越多的人求助于这些应用程序来应对压力，包括在COVID-19大流行期间。冥想应用程序是治疗抑郁和焦虑最常用的心理健康应用程序。然而，很少有人知道谁适合这些应用程序。目的:本研究旨在开发和测试一种数据驱动算法，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。方法:使用随机对照试验数据，比较为期4周的冥想应用程序(健康心灵计划[HMP])和学校系统员工(n=662)的仅评估对照条件(n=662)，我们开发了一种算法来预测谁最有可能受益于HMP。基线临床和人口统计学特征提交给机器学习模型，以开发“个性化优势指数”(PAI)，反映个体从HMP与对照组的预期痛苦减少(主要结果)。结果:组与PAI存在显著相互作用(t658=3.30;P=.001)，表明PAI评分调节了结果的组间差异。 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 %M 36346668 %R 10.2196/41566 %U //www.mybigtv.com/2022/11/e41566 %U https://doi.org/10.2196/41566 %U http://www.ncbi.nlm.nih.gov/pubmed/36346668