Virtual Reality Portable Perimetry and Home Monitoring of Glaucoma: Retention and Compliance over a 2-year Period
Bibliographic record
Abstract
Purpose To evaluate long-term retention, compliance, and performance of glaucoma patients using a virtual reality portable perimeter to monitor visual fields (VFs) at home. Design Prospective, longitudinal, cohort study. Subjects Twenty-five glaucoma patients with stable and reliable VFs (average age 67.4 years) were recruited at Toronto Western Hospital, Ontario, Canada. Methods Participants were instructed to perform bilateral home VF tests fortnightly for 2 years using the Toronto Portable Perimeter (TPP). Based on empirical home monitoring data, simulation analyses were conducted to evaluate the progression detection performance of high-frequency TPP testing. Main Outcome Measures Retention rates were calculated as the percentage of participants who performed ≥1 home VF test. Compliance rates measured the percentage of participants adhering to the recommended test frequency of every 2-month period. Visual field indices, test reliability, intertest variability, and the precision of estimating progression rate with TPP were compared to those with the Humphrey Field Analyzer (HFA). After 6 months, participants completed a questionnaire to evaluate their experiences and preferences. The years required to detect progression were also compared between HFA and TPP tests. Results Eighteen of the 25 participants (72%) completed ≥1 unsupervised VF test at home, with an average test frequency of 1.6 tests/month. Compliance decreased as the monitoring duration progressed, dropping from 83% (initial 2 months) to 11% (final 2 months). Unfamiliarity with technology and time constraints were identified as the main barriers to regular testing. Visual field indices of TPP home tests were strongly correlated with clinical results ( r > 0.900). Home testing significantly reduced intertest variability ( P < 0.001) and improved the precision of progression rate estimates ( P < 0.010). Participants overwhelmingly preferred home testing over clinic VF follow-ups ( P < 0.001). Simulations showed that TPP tests can significantly shorten the time to detect progression for different progression rates compared with clinical VF follow-up, even with compromised compliance. Conclusions Despite the small sample size, our study demonstrated that glaucoma patients could reliably perform VF tests at home over a 2-year period. However, issues with retention rate and compliance with long-term VF monitoring were observed in some participants. Nevertheless, high-quality VF data from home tests can provide supplementary information to improve the timely detection of VF progression. Financial Disclosure(s) The author(s) have no proprietary or commercial interest in any materials discussed in this article.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".