@Article{info:doi/10.2196/41566,
author="Webb, Christian A
and Hirshberg, Matthew J
and Davidson, Richard J
and Goldberg, Simon B",
title="Personalized Prediction of Response to Smartphone-Delivered Meditation Training: Randomized Controlled Trial",
journal="J Med Internet Res",
year="2022",
month="Nov",
day="8",
volume="24",
number="11",
pages="e41566",
keywords="precision medicine; prediction; machine learning; meditation; mobile technology; smartphone app; mobile phone",
abstract="Background: Meditation apps have surged in popularity in recent years, with an increasing number of individuals turning to these apps to cope with stress, including during the COVID-19 pandemic. Meditation apps are the most commonly used mental health apps for depression and anxiety. However, little is known about who is well suited to these apps. Objective: This study aimed to develop and test a data-driven algorithm to predict which individuals are most likely to benefit from app-based meditation training. 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="https://www.jmir.org/2022/11/e41566",
url="https://doi.org/10.2196/41566",
url="http://www.ncbi.nlm.nih.gov/pubmed/36346668"
}