Comparing Telemedicine and In-Person Psychological Interventions for Anxiety: A Systematic Review
Bibliographic record
Abstract
Barriers such as stigma and limited access to care continue to impede treatment for anxiety disorders. Telemedicine has emerged as a promising alternative to in-person psychological interventions, particularly after the COVID-19 pandemic. This systematic review compares the efficacy of telemedicine and in-person therapies for anxiety disorders, evaluating outcomes, patient engagement, and methodological rigor. Following PRISMA 2020 guidelines, we searched PubMed, Scopus, Web of Science, and ClinicalTrials.gov, with the final search conducted in July 2025. Ten studies comparing telemedicine with in-person interventions were included. Risk of bias was assessed using the Cochrane RoB 2 tool for randomized controlled trials and the Newcastle-Ottawa Scale for non-randomized studies. A narrative synthesis was conducted due to heterogeneity. Telemedicine demonstrated non-inferior efficacy to in-person therapy across diverse modalities and outperformed self-help programs. Patient satisfaction and adherence were high, with telehealth groups showing longer retention. Small effect size differences favored in-person therapy for generalized anxiety disorder, but most studies reported comparable outcomes. Risk of bias was low for nine out of ten studies. Telemedicine is a viable alternative to in-person therapy for anxiety disorders, with advantages in accessibility and therapist-guided formats. Future research should address long-term outcomes and equity in delivery.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".