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

Christian A Webb, Matthew J Hirshberg, Richard J Davidson, Simon B Goldberg, Christian A Webb, Matthew J Hirshberg, Richard J Davidson, Simon B Goldberg

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 × 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://ichgcp.net/clinical-trials-registry/NCT04426318.

Keywords: machine learning; meditation; mobile phone; mobile technology; precision medicine; prediction; smartphone app.

Conflict of interest statement

Conflicts of Interest: RJD is the founder, president, and serves on the board of directors for the nonprofit organization Healthy Minds Innovations, Inc. MJH has been a paid consultant at Healthy Minds Innovations, Inc for work unrelated to this study.

©Christian A Webb, Matthew J Hirshberg, Richard J Davidson, Simon B Goldberg. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 08.11.2022.

Figures

Figure 1
Figure 1
Group × Personalized Advantage Index (PAI) interaction. As PAI scores decrease (ie, reflecting relatively stronger recommendations for the Healthy Minds Program [HMP] app) group differences in observed outcome increase, favoring HMP.
Figure 2
Figure 2
Group × Personalized Advantage Index (PAI) interaction for the comparison model (ie, linear regression with baseline repetitive negative thinking [PTQ] scores as the sole predictor). As PAI scores decrease (ie, reflecting relatively stronger recommendations for the Healthy Minds Program [HMP] app) group differences in observed outcome increase, favoring HMP.
Figure 3
Figure 3
Plot of the relationship between Personalized Advantage Index (PAI) scores and outcome for each condition to inform personalized recommendations. The dashed vertical gray line indicates the point at which the 2 regression lines intersect (left margin of a bootstrapped 95% CI is shown with a dashed vertical red line). The solid vertical gray line (adjacent to the red line) is derived from the Johnson-Neyman technique and represents the value of the moderator (PAI) at which between-group differences in outcome become statistically significant. Refer to the detailed description in text, with an example for personalized Healthy Minds Program [HMP] recommendation.

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