The Efficacy and Therapeutic Alliance of Augmented Reality Exposure Therapy in Treating Adults With Phobic Disorders: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Phobic disorders are characterized by excessive fear of a stimulus that can affect the quality of a patient's life. The lifetime prevalence in adults is 7.7% to 12.5%. The current literature provides evidence-based inferences about the effectiveness of in-vivo exposure therapy (IVET) in treating phobia. However, this method can put the therapist and the client in danger, with high drop out and refusal rates. A newer approach for exposure therapy using augmented reality technology is under assessment. OBJECTIVE: This systematic review investigated the novel technology's efficacy, cost-efficacy, and therapeutic alliance in treating adults with phobia. METHODS: An extensive search was conducted using 4 major databases (MEDLINE, PsycINFO, Embase, and Scopus) using a comprehensive list of synonyms for augmented reality exposure therapy (ARET) and phobic disorders. The search targeted any randomized control trial testing ARET in adults with phobic disorders up to August 8, 2022. RESULTS: A total of 6 studies were included, with 208 participants providing results. Studies investigating the efficacy of ARET compared to no intervention showed significant results (P<.05) in the ARET group improvement. Head-to-head comparative studies comparing ARET to IVET showed no significant difference (P>.05) in the effectiveness and therapeutic alliance between both therapies. Further, the results demonstrated that the ARET group had a better long-term effect than IVET, with the ability to put the patients in more situations to face the feared object. CONCLUSIONS: The current data suggest clinically significant efficacy and a promising therapeutic alliance of ARET. However, no data are available investigating the cost-effectiveness of ARET. Further research is warranted to ascertain ARET's cost-effectiveness and examine its efficacy in other populations and anxiety conditions.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".