Effectiveness of online cognitive behavioral interventions that include mindfulness for clinically-diagnosed anxiety and depressive disorders: A systematic review and meta-analysis
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.026 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".