Personalized Prediction of Response to Smartphone-Delivered Meditation Training: Randomized Controlled Trial

A data-driven algorithm can predict which individuals are most likely to benefit from a 4-week meditation app (Healthy Minds Program) compared to no intervention. The algorithm, called a Personalized Advantage Index, was developed using machine learning on baseline data from 662 school system employees in a randomized controlled trial. It significantly moderated group differences in distress reduction, meaning it identified people who improved more with the app versus the control condition. Repetitive negative thinking alone predicted benefit nearly as well. Such an algorithm could help individuals make informed decisions about whether a meditation app is appropriate for them.

Study at a glance

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: =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.