Impact of Conversational and Animation Features of a Mental Health App Virtual Agent on Depressive Symptoms and User Experience Among College Students: Randomized Controlled Trial
Bibliographic record
Abstract
Background: Numerous mental health apps purport to alleviate depressive symptoms. Strong evidence suggests that brief cognitive behavioral therapy (bCBT)-based mental health apps can decrease depressive symptoms, yet there is limited research elucidating the specific features that may augment its therapeutic benefits. One potential design feature that may influence effectiveness and user experience is the inclusion of virtual agents that can mimic realistic, human face-to-face interactions. Objective: The goal of the current experiment was to determine the effect of conversational and animation features of a virtual agent within a bCBT-based mental health app on depressive symptoms and user experience in college students with and without depressive symptoms. Methods: College students (N=209) completed a 2-week intervention in which they engaged with a bCBT-based mental health app with a customizable therapeutic virtual agent that varied in conversational and animation features. A 2 (time: baseline vs 2-week follow-up) × 2 (conversational vs non-conversational agent) × 2 (animated vs non-animated agent) randomized controlled trial was used to assess mental health symptoms (Patient Health Questionnaire-8, Perceived Stress Scale-10, and Response Rumination Scale questionnaires) and user experience (mHealth App Usability Questionnaire, MAUQ) in college students with and without current depressive symptoms. The mental health app usability and qualitative questions regarding users' perceptions of their therapeutic virtual agent interactions and customization process were assessed at follow-up. Results: Mixed ANOVA (analysis of variance) results demonstrated a significant decrease in symptoms of depression (P=.002; mean [SD]=5.5 [4.86] at follow-up vs mean [SD]=6.35 [4.71] at baseline), stress (P=.005; mean [SD]=15.91 [7.67] at follow-up vs mean [SD]=17.02 [6.81] at baseline), and rumination (P=.03; mean [SD]=40.42 [12.96] at follow-up vs mean [SD]=41.92 [13.61] at baseline); however, no significant effect of conversation or animation was observed. Findings also indicate a significant increase in user experience in animated conditions. This significant increase in animated conditions is also reflected in the user's ease of use and satisfaction (F(1, 201)=102.60, P<.001), system information arrangement (F(1, 201)=123.12, P<.001), and usefulness of the application (F(1, 201)=3667.62, P<.001). Conclusions: The current experiment provides support for bCBT-based mental health apps featuring customizable, humanlike therapeutic virtual agents and their ability to significantly reduce negative symptomology over a brief timeframe. The app intervention reduced mental health symptoms, regardless of whether the agent included conversational or animation features, but animation features enhanced the user experience. These effects were observed in both users with and without depressive symptoms.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".