Predicting Cardiovascular Fitness From Accelerometry-based Indices Of Physical Activity In School-aged Children
Bibliographic record
Abstract
Cardiovascular fitness (CVF) is a key determinant of health status across the lifespan. Previous research has shown a positive relationship between physical activity and CVF in adults; however, it is unclear whether this relationship extends to children. This is due in part to the challenge of measuring the physical activity habits of children. Although accelerometry is recommended as an appropriate tool to capture children's physical activity, the accelerometry outcomes that best predict CVF are not known. PURPOSE: To examine the ability of select accelerometry-based indices of physical activity to predict CVF in school-aged children. METHODS: A total of 313 children (164 girls; aged 9.9 ± 0.6 yr) were evaluated for CVF (Leger's 20-m shuttle) and physical activity (accelerometry) over a 3-5 day period (using 15 second epochs; minimum 10 hr/d). Indices of physical activity included: minutes of moderate-to-vigorous physical activity (MVPA) and sedentary activity per day (using age-specific cut-points), minutes of MVPA/day accumulated in bouts of at least 5 minutes, and counts/minute. We developed four hierarchical linear regression models to assess the unique contribution of these indices after controlling for age, body mass index (BMI) and sex. RESULTS: Modest correlations (p<0.001) existed between CVF and minutes of MVPA/day (r=0.25), sedentary time/day (r=-0.23), bouted MVPA/day (r=0.24) and counts/minute (r=0.27). Age, BMI and sex accounted for 12.4% of the variance in CVF. In independent models (1-4), additional variance explained was 5.1% for MVPA/day, 3.5% for sedentary min/day, 4% for bouted MVPA/day and 5.9% for average counts/min (p<0.001). CONCLUSIONS: These preliminary data suggest that the predictive value of accelerometry varies according to the index selected. Accelerometry-based indices of physical activity only explain a small portion of the variance in CVF and as such, CVF should be evaluated directly in studies of child health.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".