TY  - JOUR
AU  - Webb, Christian A
AU  - Hirshberg, Matthew J
AU  - Davidson, Richard J
AU  - Goldberg, Simon B
PY  - 2022
DA  - 2022/11/8
TI  - Personalized Prediction of Response to Smartphone-Delivered Meditation Training: Randomized Controlled Trial
JO  - J Med Internet Res
SP  - e41566
VL  - 24
IS  - 11
KW  - precision medicine
KW  - prediction
KW  - machine learning
KW  - meditation
KW  - mobile technology
KW  - smartphone app
KW  - mobile phone
AB  - 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://clinicaltrials.gov/ct2/show/NCT04426318 
SN  - 1438-8871
UR  - https://www.jmir.org/2022/11/e41566
UR  - https://doi.org/10.2196/41566
UR  - http://www.ncbi.nlm.nih.gov/pubmed/36346668
DO  - 10.2196/41566
ID  - info:doi/10.2196/41566
ER  -