Time spent in different sedentary activity domains across adolescence: a follow-up study
Bibliographic record
Abstract
OBJECTIVE: This longitudinal study aimed to verify possible changes in the time spent in sedentary activities occurring as screen-time, educational, cultural, social, and transportation domains in a sample of Brazilian adolescents between 2015 and 2017. METHODS: It is a longitudinal prospective study with 586 adolescents from 12 to 15 years old at the Baseline (2015) enrolled in 14 public schools from Curitiba, Brazil. The Adolescent Sedentary Activity Questionnaire assessed the time spent in sedentary activities in five domains (recreational screen-time, educational, cultural, social, and transportation). A series of linear random effects regressions analyzed changes in the sedentary time between 2015 and 2017, with p < .05. RESULTS: Overall, 323 adolescents dropped out of the study resulting in a retention rate of 44.9%. The overall sedentary time remained stable from 2015 to 2017 (-3.98 min/day, 95%CI: -15.39; 7.42). The screen-time decreased (-22.22 min/day, 95%CI: -30.30; -14.15), and educational (8.29 min/day, 95% CI: 3.52; 13.06), cultural (3.41 min/day, 95% CI: 0.66; 6.15) and social sedentary activities (8.20 min/day, 95% CI: 2.06; 14.34) increased from 2015 to 2017. CONCLUSION: Significant reductions in screen-time were evidenced along with increases in time spent on other sedentary activities of educational, cultural, and social nature. KeywordsSedentary behavior, Adolescent health, Longitudinal studies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".