Physical Activity and Sedentary Behavior Patterns Across Weekdays and Weekend Days in Youth With Multiple Sclerosis and Controls
Bibliographic record
Abstract
Background: This study quantified and compared weekday and weekend patterns of device-measured physical activity (PA) and sedentary behavior between youth with pediatric multiple sclerosis (MS) and controls for the purpose of informing future PA behavior change interventions. Methods: Participant data were obtained from 3 ongoing observational studies, and the sample included 40 participants with pediatric MS and 41 controls. Light PA (LPA), moderate to vigorous PA (MVPA), and sedentary behavior data were collected using activity monitors (ActiGraph LLC) over 1 week. The main analysis involved a 2-way mixed factor analysis of variance with group as a between-subjects factor (pediatric MS vs control) and day as a within-subjects factor (weekday vs weekend day). Results: There was no group by day interaction from the analysis of variance for percentage of activity monitor wear time spent in LPA, MVPA, or sedentary behavior. There was no effect of group for LPA, MVPA, or sedentary behavior. There was an effect of day of week on percentage of day spent in LPA, MVPA, and sedentary behavior. Conclusions: These results suggest that youth with pediatric MS and controls were less physically active and more sedentary on weekends than on weekdays, but there were no differences between groups in PA and sedentary behavior overall or by day of the week. Physical activity interventions may be more successful by initially targeting weekend day activity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".