Physical Fitness Surveillance and Monitoring Systems Inventory for Children and Adolescents: A Scoping Review with a Global Perspective
Bibliographic record
Abstract
Surveillance of health-related physical fitness can improve decision-making and intervention strategies promoting health for children and adolescents. However, no study has comprehensively analyzed surveillance/monitoring systems for physical fitness globally. This review sought to address this gap by identifying: (1) national-level surveillance/monitoring systems for physical fitness among children and adolescents globally, (2) the main barriers and challenges to implementing surveillance/monitoring systems, and (3) governmental actions related to existing surveillance/monitoring systems. We used a scoping review to search, obtain, group, summarize, and analyze available evidence. Our review involved three stages: (1) identification of surveillance systems through a systematic literature review, with complementary search of the grey literature (e.g., reference lists, Google Scholar, webpages, recommendations), (2) systematic consultation with relevant experts using a Delphi method to confirm/add systems and to gather and analyze information on the barriers and challenges to implementing systems, and (3) Web searches for public documents on government sites and surveillance/monitoring system pages, and direct internet searches to identify relevant governmental actions related to surveillance systems. A total of 15 fitness surveillance/monitoring systems met our inclusion criteria. Experts identified a lack of government support and funding, and the low priority of fitness on the public health agenda as the main barriers/challenges to implementation. Several governmental actions related to surveillance systems were identified, including policies, strategies, programs, and guidelines. We propose a Global Observatory of Physical Fitness to help address these issues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".