JMIR出版物对智能手机冥想训练的个性化预测卡塔尔世界杯8强波胆分析随机对照试验%A Webb,Christian A %A Hirshberg,Matthew J %A Davidson,Richard J %A Goldberg,Simon B %+威斯康星大学麦迪逊分校心理咨询系，315教育大楼，1000 Bascom Mall，威斯康星州麦迪逊，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智能手机应用程序%K手机%D 2022 %7 8.11.2022 %9背景:冥想应用程序近年来人气飙升，越来越多的人转向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。方法:使用随机对照试验数据，将为期4周的冥想应用程序(健康心灵计划[HMP])与学校系统员工(n=662)的仅评估控制条件进行比较，我们开发了一种算法来预测谁最有可能从HMP中受益。基线临床和人口学特征提交给机器学习模型，以开发“个性化优势指数”(PAI)，反映个体预期从HMP与对照组中减少的痛苦(主要结果)。结果:出现显著组× PAI交互作用(t658=3.30;P=.001)，表明PAI评分调节了组间结果差异。 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

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