Strategic Priorities for Physical Activity Surveillance in the United States
Bibliographic record
Abstract
ABSTRACT Purpose Develop strategic priorities to guide future physical activity surveillance in the United States. Methods The Centers for Disease Control and Prevention and the American College of Sports Medicine convened a scientific roundtable of physical activity and measurement experts. Participants summarized the current state of aerobic physical activity surveillance for adults, focusing on practice and research needs in three areas: 1) behavior, 2) human movement, and 3) community supports. Needs and challenges for each area were identified. At the conclusion of the meeting, experts identified one overarching strategy and five strategic priorities to guide future surveillance. Results The identified overarching strategy was to develop a national plan for physical activity surveillance similar to the U.S. National Physical Activity Plan for promotion. The purpose of the plan would be to enhance coordination and collaboration within and between sectors, such as transportation and public health, and to address specific strategic priorities identified at the roundtable. These strategic priorities were used 1) to identify and prioritize physical activity constructs; 2) to assess the psychometric properties of instruments for physical activity surveillance; 3) to provide training and technical assistance for those collecting, analyzing, or interpreting surveillance data; 4) to explore accessing data from alternative sources; and 5) to improve communication, translation, and dissemination about estimates of physical activity from surveillance systems. Conclusion This roundtable provided strategic priorities for physical activity surveillance in the United States. A first step is to develop a national plan for physical activity surveillance that would provide an operating framework from which to execute these priorities.
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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 | 0.056 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".