Identification and Prediction of the Walking Cadence Required to Reach Moderate Intensity in Older Adults
Bibliographic record
Abstract
PURPOSE: To evaluate the walking cadence needed to reach moderate intensity in older adults and to develop an algorithm to individualize the prescription. METHODS: Peak oxygen consumption was established with 121 inactive adults (doing less than 150 minutes of aerobic exercise per week) age 55 and above on a treadmill. Walking cadence at moderate intensity was established when participants reached 40% of peak oxygen consumption on an indoor flat surface. Other variables potentially associated with walking cadence were collected (e.g., body weight, stride length, height) to contribute to the algorithm developed with half the sample, randomly selected, and validate with the other half. RESULTS: Mean walking cadence to reach moderate intensity was 115.8 ± 10.3 steps per minute. The best algorithm to predict the walking cadence needed to reach moderate intensity in this sample was: 117.95 - .23 X body weight (kg) + self-selected walking cadence (steps/min). CONCLUSION: In general, adults aged 55 and above need more than 100 steps per minute to reach moderate intensity when the prescription in individualize. Body weight and the self-selected walking cadence are useful to predict walking cadence needed to reach moderate intensity in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".