Personalized prediction of response to smartphone-delivered meditation training
Christian A. Webb, Matthew J. Hirshberg, Richard J. Davidson, Simon B. Goldberg
July 29, 2021 preprint DOI: 10.31234/osf.io/drqa4 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Randomized controlled trial Preregistered |
|---|---|
| Sample size | 662 |
| Population | School system employees |
| Duration | 4-week intervention |
| Topics | Meditation |
| Keywords | Randomized controlled trial Clinical psychology Applied psychology |
| Citations | 1 |
| Key findings | A Personalized Advantage Index based on baseline characteristics predicted which individuals would benefit more from a 4-week meditation app compared to an assessment-only control, with repetitive negative thinking alone performing comparably. |
Abstract
Meditation apps are popular and may reduce psychological distress, including during the COVID-19 pandemic. However, it is not clear who is most likely to benefit. Using randomized controlled trial data comparing a 4-week meditation app (Healthy Minds Program; HMP) with an assessment-only control 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 (preregistered primary outcome) from HMP vs. control. Significant Group x PAI interactions emerged, indicating that PAI scores moderated group differences in outcome. A regression model including repetitive negative thinking as the sole predictor performed comparably well. Finally, we demonstrate the translation of predictive models to personalized recommendations of expected benefit, which could inform users’ decisions of whether to engage with a meditation app.