Effects of an Immersive Virtual Reality Exergame on University Students’ Anxiety, Depression, and Perceived Stress: Pilot Feasibility and Usability Study
Bibliographic record
Abstract
BACKGROUND: In recent years, there has been an increase in the number of students with depression, anxiety, and perceived stress. A solution that has been increasingly used for improving health and well-being is exergaming. The effects and acceptability of exergames have been studied widely but mostly with older adults. The feasibility and usability of exergames among university students, especially those of immersive virtual reality (iVR) exergames, remain unexplored. OBJECTIVE: This study aimed to explore the feasibility of a 6-week iVR exergame-based intervention in reducing anxiety, depression, and perceived stress among university students and to examine the usability and acceptability of such games. METHODS: A total of 31 university students were recruited to participate in a 6-week study in which they needed to play a boxing-style iVR exergame called FitXR (FitXR Limited) twice per week (30 minutes per session). Their anxiety (Beck Anxiety Inventory), depression (Beck Depression Inventory-II), and perceived stress (Perceived Stress Scale) levels were measured before and after intervention. RESULTS: A total of 15 participants completed the 6-week study. Our results suggested that participants' mean depression scores decreased significantly from 8.33 (SD 5.98) to 5.40 (SD 5.14) after the intervention (P=.01). In addition, most participants (14/15, 93%) believed that the iVR exergame has good usability. Furthermore, most participants (14/15, 93%) were satisfied with the iVR gameplay experience and would play the iVR exergame again in the future. Of the 15 participants, 11 (73%) would recommend the iVR exergame to their friends. CONCLUSIONS: The results gained from this study show that the iVR exergame has good usability, is highly acceptable, and has the potential to reduce depression levels among university students.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".