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 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 | 0.031 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".