Virtual Reality Behavioral Activation as an Intervention for Major Depressive Disorder: Case Report
Bibliographic record
Abstract
BACKGROUND: Major depressive disorder (MDD) is a global problem with an increasing incidence and prevalence. There has additionally been an increase in depression due to the COVID-19 pandemic. Behavioral activation is considered an evidence-based treatment for MDD. However, there are many barriers that could hinder one's ability to engage in behavioral activation, with COVID-19 "shelter-in-place" and social distancing orders being current and large impediments. Virtual reality (VR) has been successfully used to help treat a variety of mental health conditions, but it has not yet been used as a method of administering behavioral activation to a clinical population. Using VR to engage in behavioral activation could eliminate barriers that pandemic precautions place and help decrease symptoms of depression that are especially exacerbated in these times. OBJECTIVE: The following case report examines the feasibility, acceptability, and tolerability of VR behavioral activation for an adult with MDD during a global pandemic. This participant was part of a larger pilot study, and the case serves as a description of the VR intervention. METHODS: The participant engaged in a weekly 50-minute psychotherapy Zoom session for 4 weeks, in which a modified behavioral activation protocol was administered using a VR headset to simulate activities. Data on mood ratings, homework compliance, and headset use were obtained from the headset. Acceptability, tolerability, and depression symptoms were obtained using self-report rating scales. RESULTS: The intervention was feasible, acceptable, and tolerable, as reported by this participant. The participant's depressive symptoms decreased by five-points on the Patient Health Questionnaire-9 over a month, with a beginning score of 10 (moderate depression) and a final score of 5 (mild depression). CONCLUSIONS: The implications of these findings for future research are discussed. TRIAL REGISTRATION: ClinicalTrials.gov NCT04268316; http://clinicaltrials.gov/ct2/show/NCT04268316.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".