Single Versus Multiple Monitoring Periods for Accelerometer-Measured Physical Activity in Medial Knee Osteoarthritis and Asymptomatic Controls
Bibliographic record
Abstract
Purpose: 1) To compare group-level physical activity calculated from a single versus multiple non-consecutive, one-week accelerometer monitoring periods in individuals with medial-compartment knee osteoarthritis and asymptomatic controls; and 2) to examine agreement among these estimates of physical activity at the individual-level. Methods: Accelerometer data from 38 individuals with knee osteoarthritis and 47 asymptomatic individuals was collected during three non-consecutive monitoring periods over one year. General linear models examined the effects of number of sessions averaged (one, two, or three) and group on light and moderate-to-vigorous intensity physical activity, step count, and sedentary behavior. Bland Altman analyses examined agreement between one-, two-, and/or three-session averages. Results: There were no sessions by group interactions. There was a main effect of sessions for sedentary behavior that was borderline significant when expressed as percent wear time. Limits of agreement indicated that two-session average versus single-session metrics could differ by ±50 minutes for light physical activity, ±20 minutes for moderate-to-vigorous physical activity, and ±2100 steps per day. Conclusions: These data suggest that objective physical activity monitoring practices might differ between clinical research, where group data are compared, and clinical decision making, where individual data are compared. Good estimates of group level differences in step count, light, and moderate-to-vigorous physical activity were found using a single session of accelerometer data, but a single session of sedentary behavior data should take wear time into account. The large limits of agreement indicate that multiple sessions may be needed to compare these metrics among or within individuals.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".