Developing and Validating a Step Test of Aerobic Fitness among Elementary School Children
Bibliographic record
Abstract
Purpose: The tests to estimate aerobic fitness among children require substantial space and maximum effort, which is often difficult for children. We developed a simple submaximal step test (Step Test of Endurance for Pediatrics, or STEP) and assessed its reliability, validity, and ability to estimate aerobic fitness among elementary school children. Method:Children aged 5–10 years completed the STEP with a protocol consisting of 0.1-, 0.2-, and 0.3-metre (4, 8, and 12 in.) step heights. Participants underwent treadmill testing with open circuit spirometry to determine actual maximal oxygen consumption (V̇o2max). Intra-class correlation coefficients (ICCs) assessed test–retest reliability of the STEP and its component tests. Multivariate linear regression assessed the associations between the STEP and V̇o2max, adjusting for potential covariates such as age, sex, BMI, and comorbidity count. Results: The STEP showed excellent reliability (ICC ≥ 0.92; N = 170), irrespective of effort level during testing. Significant effort issues and collinearity among the independent variables led us to exclude children aged 5–6 years ( n = 45) from the regression analysis. The final regression model for children aged 7–10 years with adequate effort ( n = 111), as defined by a respiratory exchange ratio of 1.0 or more, showed that the STEP, sex, and BMI were significantly predictive of V̇o2max ( R 2 = 0.51). Conclusions: This new, effort-independent step test can estimate the aerobic fitness of children aged 7–10 years. Regression equations to estimate V̇o2max from the STEP were provided.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".