Daily vs. weekly data collection in VR interventions: Implications from a pilot study of patient-reported outcomes among adults with cancer (Preprint)
Bibliographic record
Abstract
BACKGROUND Virtual reality (VR) interventions are increasingly used in healthcare settings to improve patient-reported outcomes (PROs). Measuring 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 We examined PROs collected daily versus weekly in a VR intervention with nature imagery designed to reduce pain, anxiety and depression and improve well-being among cancer patients. We also evaluated whether guided imagery accompanying the nature imagery improved PROs. METHODS Patients with cancer were randomly assigned to one of four conditions: [1] VR assisted guided imagery (“VRAGI”); [2] VR without guided imagery, [3] “Desktop VR” on a laptop with guided imagery; and [4] Desktop VR without guided imagery. Devices were mailed to patients' homes. Patients engaged with their assigned intervention for 15-20 minutes daily for three weeks. Weekly levels of pain, anxiety, depression and well-being were measured using items from the Edmonton Symptom Assessment Scale. Daily outcomes were collected in-device before and after each VR session. Descriptive analyses and visual pattern comparisons were used to explore trends and answer our research objectives. RESULTS Among 41 patients who consented, eight provided usable data for the current study. Findings from weekly data were unclear. Findings from daily data were more informative and showed such patterns as double-bottoms and plateau effects. There was little evidence for the addition of guided imagery improving PROs above and beyond virtual nature imagery. Still, the greatest change in outcomes over time was seen with the VRAGI condition improving well-being. CONCLUSIONS Daily collection of PROs may be more informative than less frequent data collection. Additional research is needed to confirm these study findings with larger sample sizes. CLINICALTRIAL Clinicaltrials.gov, NCT05348174 INTERNATIONAL REGISTERED REPORT RR2-10.1136/bmjopen-2022-064363
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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 | 0.055 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".