Two weeks of lower body resistance training enhances cycling tolerability to improve precision of maximal cardiopulmonary exercise testing in sedentary middle-aged females
Bibliographic record
Abstract
It is not uncommon for sedentary individuals to cite leg fatigue as the primary factor for test termination during a cardiopulmonary exercise test (CPET) on a cycle ergometer. The purpose of this study was to examine the effect of 2 weeks of lower body resistance training (RT) on cardiopulmonary capacity in sedentary middle-aged females. Additionally, the impact of RT on muscle strength was evaluated. Following familiarization, 28 women (18 exercise group, 10 control group) completed a maximal CPET on a cycle ergometer to determine peak oxygen uptake and leg extensor strength assessed using isokinetic dynamometry. Participants in the exercise group performed 2 weeks (6 sessions) of lower body RT, which comprised leg press, leg curl, and leg extension exercises. A 2-way repeated-measures ANOVA was used to evaluate the difference in changes of peak oxygen uptake and peak torque (PT). Peak oxygen uptake significantly improved from 22.2 ± 4.5 mL·kg−1·min−1 to 24.3 ± 4.4 mL·kg−1·min−1 (10.8%, p < 0.05) as well as PT from 83.1 ± 25.4 Nm to 89.0 ± 29.7 Nm (6.1%, p < 0.05) in the exercise group with no change in the control group. These findings provide initial evidence that 2 weeks of lower body RT prior to a CPET may be a helpful preconditioning strategy to achieve a more accurate peak oxygen uptake during testing, enhancing tolerability to a CPET by improving lower body strength.
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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 | 0.000 |
| 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".