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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".