Longitudinal levels and bouts of sedentary time among adolescent girls
Bibliographic record
Abstract
BACKGROUND: Adolescent girls are one of the most sedentary demographic groups. A better understanding of their accumulation of sedentary time is needed to inform future interventions. The purpose of this study was to examine the longitudinal levels and bouts of objectively measured sedentary time accumulated during different days of the week and periods of the weekday among a large sample of adolescent girls. METHODS: The results are based on 655 adolescent girls from the Girls in Sport Intervention and Research Project. Levels and bouts of sedentary time were derived from accelerometer data collected at baseline and 18-month follow-up. Total, weekday, weekend, school (i.e., morning bell to afternoon bell), after school (i.e., afternoon bell to 19:00), and evening (i.e. 19:01 to 23:59) sedentary time levels and bouts were calculated. Repeated-measures ANCOVAs were conducted to examine differences in sedentary time levels and bouts between days and time periods after adjusting for wear time, accelerometer model, and intervention group. RESULTS: Cross-sectional analyses revealed that levels and bouts of sedentary time were higher on weekdays compared to weekend days at baseline. Similar trends were observed at follow-up. In addition, percentage of wear time spent sedentary and bouts/hr of sedentary time were highest in the evening compared to the school and after school periods at both baseline and follow-up. Longitudinal analyses revealed that levels and bouts of sedentary time were higher at follow-up compared to baseline across the different days of the week and periods of the weekday examined, with the biggest increase (15%) occurring in the school period. CONCLUSIONS: Future interventions targeting sedentary time among adolescent girls should consider developing strategies to reduce and break up prolonged sedentary time during the school day and in the evening.
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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 | 0.001 |
| 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 | 0.000 | 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".