Surveillance to improve physical activity of children and adolescents
Bibliographic record
Abstract
The global transition to current low levels of habitual physical activity among children and adolescents began in the second half of the last century. Low physical activity harms health in both the short term (during childhood and adolescence) and long term (during adulthood). In turn, low physical activity could limit progress towards several sustainable development goals, undermine noncommunicable disease prevention, delay physical and mental health recovery from the coronavirus disease 2019 pandemic, increase health-care costs and hinder responses to climate change. However, despite the importance of physical activity, public health surveillance among children and adolescents is very limited globally and low levels of physical activity in children is not on the public health agenda in many countries, irrespective of their level of economic development. This article details proposals for improvements in global public health surveillance of physical activity from birth to adolescence based on recent systematic reviews, international collaborations and World Health Organization guidelines and strategies. Empirical examples from several countries illustrate how improved surveillance of physical activity can lead to public health initiatives. Moreover, better surveillance raises awareness of the extent of physical inactivity, thereby making an invisible problem visible, and can lead to greater capacity in physical activity policy and practice. The time has arrived for a step change towards more systematic physical activity surveillance from infancy onwards that could help inform and inspire changes in public health policy and practice globally.
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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 | 0.017 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".