The effect of software and hardware version on Apple Watch activity measurement: A secondary analysis of the COVFIT retrospective cohort study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective of this study was to estimate the impact of software and hardware version on Apple Watch activity measurement using data from the COVFIT retrospective cohort study. We estimated the impact of software and hardware versions on activity measurement by comparing daily active calories and daily exercise minutes in the 7 days before and 7 days after upgrading from watchOS 5 to 6, 6 to 7, 7 to 8, 8 to 9 or between two hardware versions. For each transition, we fit mixed effect negative binomial regression models to estimate the effect of the upgrade on daily (a) exercise minutes and (b) active calories, overall and stratified by sex, with and without adjusting for weekday. We also calculated and plotted the mean person-level change in average activity levels between the two weeks. As a control, we repeated the entire analysis comparing activity data two weeks before vs. one week before each upgrade. 253 participants contributed data about at least one transition (software = 250, hardware = 74). Hardware upgrades were not associated with either outcome; however, some software upgrades were. Upgrading from watchOS 7 to 8 was associated with a large, statistically significant increase in daily exercise minutes (unadjusted rate ratio (RR) = 1.13, 95% CI: 1.06, 1.20). WatchOS 6 to 7 and 8 to 9 transitions were associated with statistically significant decreases in daily exercise minutes (6 to 7: unadjusted RR = 0.92, 95% CI: 0.86, 0.99; 8 to 9: unadjusted RR = 0.91, 95% CI: 0.86, 0.96) and active calories (6 to 7: RR = 0.96, 95% CI: 0.94, 0.99); 8 to 9: RR = 0.97, 95% CI: 0.94, 0.99). There was no significant change in either outcome during in the two-week control period for most transitions. Differences in software version over time or between people may confound physical activity analyses using Apple Watch data.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it