Temporal trends of no moderate to vigorous physical activity in adolescents: a 16-year trend analysis of 115,926 participants
Bibliographic record
Abstract
BACKGROUND: Engaging in no moderate-to-vigorous physical activity (MVPA) has been recognized as an important indicator in physical activity (PA) surveillance, as any engagement in MVPA confers health benefits compared to none. Studying the prevalence of no MVPA can provide valuable insights into physical inactivity patterns and inform public health intervention efforts. While some cross-sectional studies have examined this issue, no research has analysed year-to-year trends. Therefore, the aim of this study was to assess trends of no MVPA among adolescents and key subgroups using a nationally representative US sample. METHODS: Data from 2005 to 2021 cycles of the Youth Risk Behavior Surveillance System were used, with 115,926 US adolescents aged 14-17 years included (female: unweighted sample size = 58,582, 50.5%; weighted%=49.4%). Participants self-reported their demographic (sex, age, race/ethnicity, body mass index) and behavioural information (days of ≥ 60 min of MVPA over the past week, and recreational screen time). No MVPA was operationalized as reporting 0 days of ≥ 60 min of MVPA. Trend analysis was performed to assess temporal variations from 2005 to 2021 using a series of binary logistic regression models after controlling for demographic and screen time related variables. RESULTS: Declining trends in no MVPA were observed among adolescents from 2005 (weighted: 24.3%) to 2021 (weighted: 15.5%). After stratifying by sex, age, race/ethnicity, body mass index and recreational screen time, similar downward trends were shown across all adolescent subgroups consistently (p for trend < 0.001). Girls, older adolescents, those who identified as non-White, adolescents with excess weight, and those engaging in more than 2 h of recreational screen time per day tended to report no MVPA at higher rates (all p < 0.001) compared to their counterparts. CONCLUSIONS: No MVPA has declined among the US adolescents, especially after 2009. Notably, sociodemographic disparities were observed in no MVPA among different population subgroups. PA promotion strategies targeting girls and older adolescents should be prioritized to further reduce the prevalence of no MVPA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".