The Impact of Different High-Intensity Interval Training Protocols on Body Composition and Physical Fitness in Healthy Young Adult Females
Bibliographic record
Abstract
Although traditional high-intensity interval training (HIIT) has been effective in improving body composition and physical fitness, it is unclear how multimodal HIIT affects these variables. This study compared the differences between these two training programs on body composition and physical fitness in apparently healthy, nonobese young adult females. A total of 16 participants (mean age = 23 ± 5.08 years) completed a 12-week HIIT intervention with two treatment groups: rowing and multimodal. Immediately before and after the intervention, the following measures were assessed: body mass index (BMI), total body mass, waist circumference, waist-to-height ratio, total body fat %, visceral adipose tissue, lean mass, bone mineral outcomes, cardiovascular fitness, and muscular fitness. A general linear model with repeated measures was used to assess changes over time for the group as a whole, as well as between-group differences. For the group as a whole, there were significant decrease in total body fat % (p = 0.04) and significant increases in BMI (p = 0.015), total body mass (p = 0.003), lean mass (p < 0.001), bone mineral content (BMC) (p < 0.001), VO2max (p = 0.01), broad jump (p = 0.001), squat endurance (p = 0.006), press (p < 0.001), back squat (p < 0.001), and deadlift (p < 0.001) one repetition maximum (1RM). The multimodal group (p < 0.001) increased deadlift 1RM significantly more than the rowing group (p = 0.002). HIIT can be an effective means for improving cardiovascular and muscular fitness, increasing lean mass and BMC, and thereby improving cardiometabolic as well as musculoskeletal health in nonobese females. Using a multimodal approach may give the added benefit of superior muscular strength increases.
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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 | 0.001 |
| 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) | 0.001 | 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".