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Personalized Prediction of Response to Smartphone-Delivered Meditation Training: Randomized Controlled Trial.
Webb, Christian A; Hirshberg, Matthew J; Davidson, Richard J; Goldberg, Simon B.
  • Webb CA; Harvard Medical School, Boston, MA, United States.
  • Hirshberg MJ; McLean Hospital, Belmont, MA, United States.
  • Davidson RJ; Center for Healthy Minds, University of Wisconsin - Madison, Madison, WI, United States.
  • Goldberg SB; Center for Healthy Minds, University of Wisconsin - Madison, Madison, WI, United States.
J Med Internet Res ; 24(11): e41566, 2022 11 08.
Article in English | MEDLINE | ID: covidwho-2109575
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//clinicaltrials.gov/ct2/show/NCT04426318.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Meditation / Mobile Applications / COVID-19 Type of study: Experimental Studies / Prognostic study / Qualitative research / Randomized controlled trials Topics: Traditional medicine Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2022 Document Type: Article Affiliation country: 41566

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Meditation / Mobile Applications / COVID-19 Type of study: Experimental Studies / Prognostic study / Qualitative research / Randomized controlled trials Topics: Traditional medicine Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2022 Document Type: Article Affiliation country: 41566