Step Length, But Not Stepping Cadence, Strongly Predicts Physical Activity Intensity During Jogging and Running
Bibliographic record
Abstract
Device-based measures often rely on the positive relationship between walking cadence and metabolic equivalents of task (METs) to estimate physical activity. It is unknown whether this relationship remains during jogging/running. The study purpose was to investigate the relationships between METs, cadence, and step length during walking and jogging/running. A treadmill protocol with 5 walking (3.2–6.4 km•hr−1) and 5 jogging/running stages (8.0–11.3 km•hr−1) was completed in 43 adults (23 ± 5 years, 19♀). Predictors of METs during walking and jogging/running were determined by generalized mixed modeling. The strongest prediction models for walking (R2 = 0.72, P < .001) and jogging/running (R2 = 0.75, P < .001) included cadence2, cadence, step length, age, and leg length (all, P < .001). Step length accounted for 49.1% and 78.3% of model variance during walking and jogging/running, respectively. METs are poorly estimated by cadence during jogging/running but step length reduces error. Strategies to measure step length in free-living settings could better predict physical activity intensity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".