Bridging game metrics and user perception in remote virtual reality exergames: Lessons from a COVID-19 home-based study
Bibliographic record
Abstract
Objective With the increasing affordability of virtual reality (VR) technology, VR exergames are emerging as promising tools for promoting physical activity and engagement among older adults. However, little is known about how VR-generated game metrics and user experience data evolve over time and influence long-term adherence. This study examined the feasibility of a custom VR exergame- Seas the Day -for at-home use during the COVID-19 lockdown. Methods Thirteen community-dwelling older adults completed 18 seated VR sessions over 6 weeks (3×/week), integrating Tai Chi, rowing, and fishing activities. Automatically recorded in-game metrics included rowing repetitions, Tai Chi completion time, fishing response times, distance traveled, and in-game errors. A difficulty-adjusted performance index (DAPI) was computed using session one as baseline to track progress over time. Participants also completed a Game User Experience Scale at weeks 3 and 6. Results Most participants showed improving or stable performance across sessions, with a smaller subset declining, highlighting individual differences. Significant gains were observed in Tai Chi completion time, rowing efficiency, and fishing response times. DAPI results confirmed overall upward performance trends despite repeated exposure. Game User Experience ratings remained high, particularly for enjoyment and ease of use, indicating sustained engagement and adherence. Conclusion Custom VR exergames can support physical activity and engagement among older adults in home settings, even during periods of social restriction. Game metrics and self-reported experience offer complementary insights into feasibility, adaptability, and individualized progress, underscoring VR exergaming's potential to promote well-being while emphasizing the need for personalized engagement strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".