Contribution of muscular strength in cardiorespiratory fitness tests.
Bibliographic record
Abstract
AIM: Reports from laboratory-based studies have revealed a relationship between resistance training and endurance performance in both trained and untrained individuals. The purpose of the present study was to investigate the contribution of lower extremity muscular strength levels in performing cardiorespiratory fitness tests in laboratory, as well as field-based settings. METHODS: Within 2 weeks 38 healthy males (age 21.6+/-2.5 years, body mass index--BMI--24.4+/-2.2) performed three maximal oxygen uptake (VO(2max)) assessments using the 20 m multistage shuttle run test (MSR), the 20 m square shuttle run test (SSR), and a maximal treadmill test (MT) to exhaustion. Data were also obtained from knee flexion and extension isokinetic dynamometry at 60 degrees xs(-1). RESULTS: MSR performance correlated with the peak torque generated from both legs at r=0.63 (P<0.001). The equivalent for SSR was significant at r=0.44 (P<0.05), while MT demonstrated a non-significant positive correlation coefficient (r=0.34, P>0.05). Stepwise regression analyses revealed that the inclusion of leg strength parameters increased the coefficient of determination by 9% (P<0.001) and 4% (P<0.05) in the MSR and SSR, respectively. The MT model was not significantly associated with any of the isokinetic indices studied. CONCLUSIONS: Although moderately significant, the present coefficients suggest that performance in the present field-based cardiorespiratory fitness tasks is affected to a certain extent by lower extremity muscular strength. The latter also demonstrates a positive relationship with laboratory-based performance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 0.001 |
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".