Therapist-guided remote versus in-person cognitive behavioural therapy: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Cognitive behavioural therapy (CBT) has been shown to be effective for several psychiatric and somatic conditions; however, most randomized controlled trials (RCTs) have administered treatment in person and whether remote delivery is similarly effective remains uncertain. We sought to compare the effectiveness of therapist-guided remote CBT and in-person CBT. METHODS: We systematically searched MEDLINE, Embase, PsycINFO, CINAHL, and the Cochrane Central Register of Controlled Trials from inception to July 4, 2023, for RCTs that enrolled adults (aged ≥ 18 yr) presenting with any clinical condition and that randomized participants to either therapist-guided remote CBT (e.g., teleconference, videoconference) or in-person CBT. Paired reviewers assessed risk of bias and extracted data independently and in duplicate. We performed random-effects model meta-analyses to pool patient-important primary outcomes across eligible RCTs as standardized mean differences (SMDs). We used Grading of Recommendations Assessment, Development and Evaluation (GRADE) guidance to assess the certainty of evidence and used the Instrument to Assess the Credibility of Effect Modification Analyses (ICEMAN) to rate the credibility of subgroup effects. RESULTS: We included 54 RCTs that enrolled a total of 5463 patients. Seventeen studies focused on treatment of anxiety and related disorders, 14 on depressive symptoms, 7 on insomnia, 6 on chronic pain or fatigue syndromes, 5 on body image or eating disorders, 3 on tinnitus, 1 on alcohol use disorder, and 1 on mood and anxiety disorders. Moderate-certainty evidence showed little to no difference in the effectiveness of therapist-guided remote and in-person CBT on primary outcomes (SMD -0.02, 95% confidence interval -0.12 to 0.07). INTERPRETATION: Open Science Framework (https://osf.io/7asrc).
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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 | 0.041 | 0.110 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.039 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".