The meditation app revolution.
Open accessAbstract
Smartphone-based meditation training has jumped onto the world stage, shifting how millions of people learn and practice meditation. This review examines the rapid proliferation of meditation apps and synthesizes current scientific findings on their usage patterns, efficacy, mechanisms of action, and safety. Though research lags well behind public adoption, recent randomized controlled trials and meta-analyses reveal that app-based meditation interventions produce modest but consistent reductions in depression and anxiety. Initial mechanistic studies further suggest that improvements in worry, repetitive negative thinking, and self-reported mindfulness skills may underpin these effects, alongside early findings on blood pressure reduction and pro-inflammatory gene expression. This review describes some of the similarities and differences between meditation apps and traditional, in-person mindfulness programs. Meditation apps often differ in the relative absence of interpersonal support, briefer practice sessions, lower sustained engagement rates, and greater opportunities for personalization and large-scale data capture. We discuss opportunities based on these issues, including hybrid models that combine app-based content with human support, just-in-time interventions, and advanced trial designs that harness app analytics. With thoughtful development and rigorous evaluation, meditation apps have potential to expand the reach of evidence-based meditation training, offering a unique platform for advancing translational research on meditative practices. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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Mindfulness-based interventions can help individuals focus on the present moment and reduce the cognitive hyperarousal that often accompanies sleep onset anxiety.
“Mindfulness-based interventions can help individuals focus on the present moment and reduce the cognitive hyperarousal that often accompanies sleep onset anxiety [73,74].”
From Pre-Sleep Anxiety: Mechanisms and Therapeutic Approaches in Clinical Practice. (2026) · cites this paper as [73]
The Mechanisms section reports that app-based mindfulness meditation can reduce worry and repetitive negative thinking, while the Similarities section describes attention monitoring as a foundational skill. Those findings support the general idea of redirecting attention and easing anxious thought, but the paper does not establish that these interventions reduce cognitive hyperarousal specifically during sleep-onset anxiety. (LLM, conf=0.84)
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- Authors
- Creswell, J. D., & Goldberg, S. B.
- Affiliations
- Carnegie Mellon University (US)
- University of Wisconsin–Madison (US)
- Venue
- American Psychologist
- Published
- 2025-08-07
- ISSN
- 0003-066X (ISSN-L)
- 1935-990X
- PMC
- PMC12333550
- OpenAlex
- W4413040824
- Cited by
- 18
- References
- 62
- Citations by year
- 2026: 16 · 2025: 2
- FWCI
- 21.15
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- American Psychologist (publishedVersion, other-oa)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (R01CA236860; R01DK128114, R24AT012845; K32AT010879)
- Hope for Depression Research Foundation
- Topics
- Social Sciences › Psychology › Clinical Psychology › Mindfulness and Compassion Interventions
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- meditation apps, smartphone-based intervention, mindfulness training, depression, anxiety, worry, repetitive negative thinking, mindfulness skills, blood pressure, inflammatory gene expression, app engagement, hybrid intervention, just-in-time interventions, app analytics, translational research
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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