Effects of High-Intensity Interval Training Protocols on Liver Enzymes and Wellness in Women
Bibliographic record
Abstract
Background. Single-modality, high-intensity interval training (HIIT) using traditional cardiorespiratory exercise selection has been found to provide similar and sometimes superior cardiometabolic effects compared with moderate-intensity continuous training. However, little is known regarding the cardiometabolic and psychosocial effects of HIIT using resistance training modalities. Therefore, this study aims to compare the effects of HIIT using rowing (R-HIIT) and multimodal HIIT (MM-HIIT) using resistance training on liver enzymes, cardiometabolic risk factors, and psychosocial outcomes. Method. Recreationally active females with a body mass index <30 kg/m2 (N = 16, 23.0 ± 5.9 years) were randomized into a MM-HIIT or R-HIIT group and completed a 12-week HIIT intervention (ClinicalTrials.gov registration number: https://clinicaltrials.gov/ct2/show/NCT03093441) using principles of social cognitive theory (SCT). Participants completed pre- and postintervention measurements on anthropometrics, resting heart rate, blood pressure, blood measures (lipids, liver enzymes, and glucose), exercise self-efficacy, and perceived wellness. Analysis of covariance was used to examine differences in postintervention measures between groups after controlling for baseline values, waist circumference, and waist-to-height ratio. Results. R-HIIT group had significantly decreased alanine aminotransferase (mean difference = 13.16, P = 0.013 , effect size (ES) = 0.44, confidence interval (CI) = 3.40 to 22.92) and aspartate aminotransferase (mean difference = 10.79, P = 0.024 , ES = 0.38, CI = 1.67 to 19.90) levels compared with the M-HIIT group, and the whole group had improved wellness scores (14.72 ± 2.6 to 16.89 ± 2.76, P = 0.002 ). Conclusion. R-HIIT may be an effective preventative method for improving liver health in females without obesity. When using principles of SCT, HIIT may enhance overall well-being.
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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 | 0.001 |
| 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) | 0.003 | 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".