Virtual Reality Interventions of Daily Versus Weekly Data Collection in Patient-Reported Outcomes Among Adults With Cancer: Pilot Survey Study
Bibliographic record
Abstract
Background: Virtual reality (VR) interventions are increasingly used in health care settings to improve patient-reported outcomes (PROs). PROs are commonly evaluated at weekly intervals with data collected via digital surveys. While weekly assessments have benefits, VR devices enable more frequent in-device data collection. It remains unclear whether PROs collected more frequently provide more information on these interventions than PROs collected more infrequently. Objective: This pilot study explored differences between daily and weekly PRO data collection in a VR intervention with nature imagery, with and without guided imagery, among patients with cancer. Methods: Patients with cancer (n=8) were randomly assigned to one of four intervention groups: (1) virtual reality-assisted guided imagery (VRAGI), (2) VR without guided imagery, (3) desktop VR with guided imagery, or (4) desktop VR without guided imagery. Devices were mailed to participants' homes for 15-20 minutes of daily use over 3 weeks. Weekly outcomes (pain, anxiety, depression, and well-being) were assessed using items from the Edmonton Symptom Assessment Scale. Daily outcomes were captured via in-device pre-post surveys. Data were analyzed descriptively, using visual trend comparisons to explore patterns. Results: Of 41 patients who consented, 8 provided complete and usable data. Weekly outcomes showed no consistent trends. In contrast, daily data revealed more nuanced patterns, such as early symptom relief, plateaus, and "double-bottom" effects. The addition of guided imagery did not consistently enhance outcomes beyond VR alone, although the VRAGI condition showed the greatest improvement in well-being. Given the small sample size, these findings should be considered exploratory. Conclusions: This pilot study suggests that daily PRO data might offer richer insight into intervention effects than weekly assessments. Further research with larger samples is needed to confirm these patterns.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".