Prediction Equations of Energy Expenditure in Chinese Youth Based on Step Frequency During Walking and Running
Bibliographic record
Abstract
PURPOSE: This study set out to examine the relationship between step frequency and velocity to develop a step frequency-based equation to predict Chinese youth's energy expenditure (EE) during walking and running. METHOD: A total of 173 boys and girls aged 11 to 18 years old participated in this study. The participants walked and ran on a treadmill at speeds of 3 km/hr, 4 km/hr, 5 km/hr, 6 km/hr, 7 km/hr, and 8 km/hr. EE was measured using indirect calorimetry of open circuit spirometry (Cosmed K4b2 metabolic analyzer). Using multiple regression analysis, the relationship between step frequency and velocity was first examined, and the prediction equation of EE based on step frequency, age, and gender was derived. RESULTS: The hypothesized relationship between step frequency and velocity was confirmed and an accurate (R2 = .78) EE prediction equation was derived: NetEE = - 13.7744 + 1.8004 (step frequency) - 5.5715 (age) - 11.5244 (gender). CONCLUSION: A step frequency-, age-, and gender-based equation was derived to predict the EE of youth during walking and running. The equation can be used to develop a simple device to estimate EE during walking and running in this population.
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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 | 0.000 |
| 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) | 0.001 | 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".