Cited by 11Digital Mental Health InterventionsMindfulness and Compassion InterventionsAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes

Effectiveness of online cognitive behavioral interventions that include mindfulness for clinically-diagnosed anxiety and depressive disorders: A systematic review and meta-analysis

Kirk, Megan A., Pirbaglou, Meysam, Weerasekera, Rasanjala, Ritvo, Paul·2022·International Journal of Mental Health

Abstract

Background Online cognitive behavioral interventions that include mindfulness techniques have attracted considerable attention given the demonstrated mental health benefits of mindfulness and the availability of scalable opportunities for increased therapeutic use. However, comparatively little is known about the effectiveness of these types of interventions when they are delivered to clinician-diagnosed populations receiving psychiatric treatment.Aims This review evaluates therapeutic interventions that included a mindfulness component aimed at reducing anxiety and depression symptoms in clinician-diagnosed samples.Methods Randomized control trials (RCTs) published between January 1990 to September 2020 assessing the effects of online cognitive behavioral interventions that include a mindfulness component were searched across five databases (Medline, PsychINFO, PubMed, CINAHL, Web of Science).Results Eleven studies met inclusion criteria with sample sizes ranging from 37 to 84 per study. Findings revealed an overall statistically significant moderate between-group difference at post-intervention for depression (Hedges’ g = −0.47) and anxiety (Hedges’ g = −0.40) outcomes favoring the online treatment groups. Further analyses revealed larger effect sizes among RCTs employing waitlist control (WLC) comparisons, and reductions in depression symptoms within the intervention groups to be above the minimal clinically important difference (MCID) for BDI-II.Conclusions Findings from this meta-analytic review provide preliminary support for including mindfulness practices within existing therapeutic programs to reduce depression and anxiety symptoms in clinician-diagnosed populations. Research implications and priorities for online mindfulness-based cognitive behavioral programming are discussed.

Integrity signals

Registry

Crossref · 10.1080/00207411.2021.1959807

Cites

63 of 63 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Kirk, M. A., Pirbaglou, M., Weerasekera, R., & Ritvo, P.
Affiliations
  • York University (CA)
  • University of Toronto (CA)
  • University Health Network (CA)
Corresponding author
Ritvo, Paul
Venue
International Journal of Mental Health
Published
2021-11-10
Volume
51
Issue
3
Pages
235-266
ISSN
  • 0020-7411 (ISSN-L)
  • 1557-9328
OpenAlex
W3213726799
Cited by
10
References
63
Citations by year
2026: 1 · 2025: 5 · 2024: 2 · 2023: 1 · 2021: 1
FWCI
0.16
Type
journal article
Language
en
Open access
closed
Locations
International Journal of Mental Health (publishedVersion)
Topics
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
  • Social Sciences › Psychology › Clinical Psychology › Mindfulness and Compassion Interventions
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Keywords
internet-based cognitive behavioral therapy, mindfulness, anxiety disorders, depression, waitlist control, treatment effectiveness, digital mental health interventions, symptom relief

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.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review