Multimodal high-intensity interval training increases muscle function and metabolic performance in females
Bibliographic record
Abstract
High-intensity interval training (HIIT) is a time-efficient method of improving aerobic and anaerobic power and capacity. In most individuals, however, HIIT using modalities such as cycling, running, and rowing does not typically result in increased muscle strength, power, or endurance. The purpose of this study is to compare the physiological outcomes of traditional HIIT using rowing (Row-HIIT) with a novel multimodal HIIT (MM-HIIT) circuit incorporating multiple modalities, including strength exercises, within an interval. Twenty-eight recreationally active women (age 24.7 ± 5.4 years) completed 6 weeks of either Row-HIIT or MM-HIIT and were tested on multiple fitness parameters. MM-HIIT and Row-HIIT resulted in similar improvements (p < 0.05 for post hoc pre- vs. post-training increases for each group) in maximal aerobic power (7% vs. 5%), anaerobic threshold (13% vs. 12%), respiratory compensation threshold (7% vs. 5%), anaerobic power (15% vs. 12%), and anaerobic capacity (18% vs. 14%). The MM-HIIT group had significant (p < 0.01 for all) increases in squat (39%), press (27%), and deadlift (18%) strength, broad jump distance (6%), and squat endurance (280%), whereas the Row-HIIT group had no increase in any muscle performance variable (p values 0.33-0.90). Post-training, 1-repetition maximum (1RM) squat (64.2 ± 13.6 vs. 45.8 ± 16.2 kg, p = 0.02), 1RM press (33.2 ± 3.8 vs. 26.0 ± 9.6 kg, p = 0.01), and squat endurance (23.9 ± 12.3 vs. 10.2 ± 5.6 reps, p < 0.01) were greater in the MM-HIIT group than in the Row-HIIT group. MM-HIIT resulted in similar aerobic and anaerobic adaptations but greater muscle performance increases than Row-HIIT in recreationally active women.
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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) | 0.003 | 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".