Personalized prediction of response to smartphone-delivered meditation training: A machine learning approach (Preprint)
Christian A. Webb, Matthew J. Hirshberg, Richard J. Davidson, Simon B. Goldberg
July 31, 2022 DOI: 10.2196/preprints.41566 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Randomized controlled trial |
|---|---|
| Sample size | 662 |
| Population | School system employees |
| Duration | 4-week intervention |
| Topics | Anxiety Meditation |
| Keywords | Machine learning Baseline sea Artificial intelligence Randomized controlled trial Popularity Applied psychology Clinical psychology |
| Citations | 1 |
| Registration | NCT04426318 |
| Key findings | A Personalized Advantage Index derived from baseline characteristics predicted which individuals experienced greater distress reduction from a meditation app compared to a control condition. |
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. In fact, meditation apps now represent the most commonly used mental health apps for depression and anxiety. However, little is known regarding who is well-suited to these apps.
Objective: The aim of this study was 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 predicting 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 vs. control.
Results: A significant Group x PAI interaction emerged, indicating that PAI scores moderated group differences in outcome. A regression model including repetitive negative thinking as the sole baseline predictor performed comparably well. Finally, we demonstrate the translation of a predictive model to personalized recommendations of expected benefit.
Conclusions: Overall, results reveal 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 well-informed decisions about whether a meditation app is right for them. CLINICALTRIAL clinicaltrials.gov (NCT04426318)