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Record W4414569808 · doi:10.7759/cureus.93355

The Impact of Vipassana Meditation on Health and Well-Being: A Systematic Review of Current Evidence

2025· review· en· W4414569808 on OpenAlexaboutno aff
Selvaraj Giridharan, Soni Soumian, Nagaraj V Kumar, Mrunmai Godbole

Bibliographic record

VenueCureus · 2025
Typereview
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsObservational studySystematic reviewRandomized controlled trialAnxietyMindfulnessMeditationMental healthMEDLINEEvidence-based medicine

Abstract

fetched live from OpenAlex

Vipassana meditation, an ancient Buddhist-derived practice that emphasises insight through non-judgmental observation of sensations and thoughts, has gained popularity for its potential to enhance health and well-being. Building on earlier research, this updated review synthesises empirical evidence published since 2010 to evaluate the impact of Vipassana across the psychological, physiological, neurobiological, and behavioural domains, addressing the growing need for effective, evidence-based approaches to global mental health challenges. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, databases such as PubMed, Cochrane Library, Google Scholar, PsycINFO, and Scopus were searched from January 2010 to April 2025 to identify relevant studies. Eligible studies comprised randomized controlled trials (RCTs), single-arm trials, pilot studies, and observational designs published in English, involving adult participants and quantifiable outcomes. Reviews, non-Vipassana practices, and low-quality studies with fewer than 10 participants per group were excluded, while the risk of bias was assessed using the Cochrane Risk of Bias (RoB) 2 tool and Newcastle-Ottawa Scale. Given the heterogeneity, the findings were synthesised narratively, exploring subgroups based on retreat intensity and practitioner experience. Eleven studies were included (three RCTs, two single-arm trials, one pilot trial, and five observational trials), revealing psychological outcomes such as reductions in stress and anxiety alongside gains in mindfulness and well-being; physiological and neurobiological findings included improved hippocampal topology, increased heart rate variability, and fewer migraine days; and behavioural improvements encompassed enhanced executive functions and memory consolidation, with stronger effects noted in intensive retreats and among experienced meditators, although evidence was limited by small sample sizes, moderate to high risk of bias, and absence of blinding. In conclusion, moderate evidence supports the benefits of Vipassana meditation for psychological and physiological health, particularly in alleviating stress, anxiety, and migraine burden while enhancing mindfulness and neurobiological markers, with effects appearing intensity-dependent and retreats yielding sustained advantages. Despite methodological limitations, Vipassana holds promise as an adjunct for stress-related disorders, warranting larger, well-controlled RCTs to substantiate its long-term efficacy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.041
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.009
Bibliometrics0.0110.010
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.127
GPT teacher head0.515
Teacher spread0.388 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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