Quarter-mile walk test sensitive to training-induced fitness changes
Bibliographic record
Abstract
BACKGROUND: Cardiorespiratory fitness (CRF) is an important aspect of the overall health of an individual and its monitoring must be promoted in the general population. Thus, the aim of the study was to cross-validate and improve CRF estimation based on quarter-mile Rockport Fitness Walking Test. METHODS: Thirty participants (31.4±7.99 years) were randomized in either a four-week aerobic training group (10 men and 10 women) or a control group (eight men and two women). CRF was assessed via VO2max test and estimated via quarter-mile Rockport Fitness and Ebbeling treadmill tests, before and after the training intervention. The original quarter-mile Rockport VO2max estimation was found to greatly overestimate CRF by 22 mL/kg/min. When its coefficient was updated according to our data, it largely improved (by 6.8 mL/kg/min). Furthermore, a new algorithm for predicting VO2max was designed using multi-linear regression analysis. RESULTS: The original quarter-mile Rockport Fitness Walking Test was not sensitive to CRF changes. It showed changes in VO2max, which were significantly different from the actual observed changes (-1.1±4.08 vs. 1.61±2.84, P=0.02, respectively). The Ebbeling treadmill test appeared to systematically overestimate CRF changes. Our new algorithm showed improved sensitivity for detecting CRF changes and stability. CONCLUSIONS: The original quarter-mile Rockport Fitness Walking Test equation for predicting VO2max was neither accurate nor sensitive to changes in CRF, most likely due to cardiovascular drift. Our new algorithm, based on the same brisk walking test, can provide a more accurate estimate of CRF, which is also sensitive to VO2max changes, in a broad age range (18 to 50 years).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".