ReviewJMIR XR and spatial computing2026
Efficacy of Virtual Reality-Based Mindfulness Interventions: Systematic Review and Meta-Analysis.
Review in JMIR XR and spatial computing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Efficacy of Virtual Reality-Based Mindfulness Interventions: Systematic Review and Meta-Analysis.JMIR XR and spatial computing · 2026Review
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Virtual reality-based mindfulness interventions (VRbMIs) increasingly populate studies as scalable tools for stress and emotion regulation. However, findings across psychological outcomes are heterogeneous, and methodological variation in intervention design, outcome measurement, and reporting practices limits cross-study comparability and cumulative synthesis. Objective: A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020-compliant systematic review and meta-analysis was conducted to evaluate the psychological effects of VRbMIs published between 2023 and 2025, examining methodological quality and outcome consistency across diverse study designs. Methods: A PRISMA 2020-compliant systematic search was conducted across PubMed, PsycINFO, Scopus, Semantic Scholar, and CORE from January 2023 to April 2025. Eligible studies included quantitative VRbMIs reporting psychological outcomes. We assessed risk of bias using the Mixed Methods Appraisal Tool and conducted random-effects meta-analyses using Hedges Results: VRbMIs were associated with a large, statistically significant reduction in negative affect and a statistically significant increase in state mindfulness, while effects on depression, stress, and anxiety were small to moderate and nonsignificant. Trait mindfulness was described narratively rather than meta-analyzed and showed limited, inconsistent change across studies. For anxiety, we included 13 studies contributing 27 effect size estimates in the quantitative synthesis. A random-effects model indicated a small-to-moderate, nonsignificant pooled effect ( Conclusions: VRbMIs demonstrate statistically significant short-term benefits for negative affect and state mindfulness, with consistently positive but nonsignificant trends toward improvement in stress, anxiety, and depression. Effects on positive affect and trait mindfulness were small and less consistent. Physiological findings were promising but limited by inconsistent reporting. These results support the use of VRbMIs as accessible tools for emotional regulation across diverse populations, while highlighting the need for larger trials, standardized outcome reporting, and more rigorous control conditions to strengthen the evidentiary foundation of this rapidly evolving field.
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.