Personalized prediction of response to smartphone-delivered meditation training

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.

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

“…However, the current prediction analyses were not planned a priori and were not included in the preregistered data analysis plan. All code (implemented in the R statistical software [ 51 ]) used to reproduce the analyses in the manuscript have been posted on Open Science Framework [ 52 ].…”