Availability of Physical Activity Tracking Data from Wearable Devices for Glaucoma Patients
Bibliographic record
Abstract
Physical activity has been found to potentially modulate glaucoma risk, but the evidence remains inconclusive. The increasing use of wearable physical activity trackers may provide longitudinal and granular data suitable to address this issue, but little is known regarding the characteristics and availability of these data sources. We performed a scoping review and query of data sources on the availability of wearable physical activity data for glaucoma patients. Literature databases (PubMed and MEDLINE) were reviewed with search terms consisting of those related to physical activity trackers and those related to glaucoma, and we evaluated results at the intersection of these two groups. Biomedical databases were also reviewed, for which we completed database queries. We identified eight data sources containing physical activity tracking data for glaucoma, with two being large national databases (UK BioBank and All of Us) and six from individual journal articles providing participant-level information. The number of glaucoma patients with physical activity tracking data available, types of glaucoma-related data, fitness devices utilized, and diversity of participants varied across all sources. Overall, there were limited analyses of these data, suggesting the need for additional research to further investigate how physical activity may alter glaucoma risk.
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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 | 0.023 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.036 | 0.038 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".