Trial reportJournal of medical Internet research2022
Personalized Prediction of Response to Smartphone-Delivered Meditation Training: Randomized Controlled Trial.
Trial report in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04426318 (COVID-19 and the Healthy Minds Program for Educators), which is not on this map. Cited by 16 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
COVID-19 and the Healthy Minds Program for Educators
Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 23 citations in OpenAlex.
- AI-Driven Mental Health Support for Caregivers of Individuals With Alzheimer Disease: Systematic Literature Review and Development of a Conceptual Framework.JMIR mental health · 2026Pooled it
- Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications.Psychological medicine · 2025Pooled it
- Personalized prediction of response to three meditation practices: A randomized controlled trial.Behaviour research and therapy · 2026Trial
- Personalized Treatment Selection for Digital Eating Disorder Interventions: A Proof-of-Concept for the Personalized Advantage Index.The International journal of eating disorders · 2026Trial
- Does it matter how meditation feels? An experience sampling study.Journal of consulting and clinical psychology · 2024Trial
- Personalized Prediction of Response to Smartphone-Delivered Meditation Training: Randomized Controlled Trial.Journal of medical Internet research · 2022Trial
- Sitting and Active Meditation Practice: Utilization and Associations with Outcomes in Naturalistic and Clinical Trial Data.Mindfulness · 2026Article
- Uncovering the heterogeneity in young adult anxiety: subtypes predict outcomes of a CBT mobile app.Molecular psychiatry · 2026Article
- Using Digital Technology to Increase Integration of Meditation into Daily Life: The Case for Meditation-Based Ecological Momentary Interventions.Journal of technology in behavioral science · 2025Article
- ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Review
- Mental Health Interventions for Young People Living With HIV/AIDS in Sub-Saharan Africa: A Systematic Review.AIDS research and treatment · 2025Review
- Resilient Stress Reactivity Profiles Predict Mental Health Gains from Online Contemplative Training: A Randomized Clinical Trial.Journal of personalized medicine · 2024Article
- Review: Preventing Psychopathology in the Digital Age: Leveraging Technology to Target Coping and Emotion Regulation in Adolescents.JAACAP open · 2024Review
- Understanding the Implementation of Informal Meditation Practice in a Smartphone-Based Intervention: A Qualitative Analysis.Mindfulness · 2024Article
- Revealing subgroup-specific mechanisms of change via moderated mediation: A meditation intervention example.Journal of consulting and clinical psychology · 2024Article
- At-home use of app-based mindfulness for children: A randomized active-controlled trial.Mindfulness · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundMeditation 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.
objectiveThis study aimed to develop and test a data-driven algorithm to predict which individuals are most likely to benefit from app-based meditation training.
methodsUsing 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.
resultsA significant group × PAI interaction emerged (t
conclusionsOverall, 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 registrationClinicalTrials.gov NCT04426318; https://clinicaltrials.gov/ct2/show/NCT04426318.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.