Low-Volume High-Intensity Interval Training Versus Moderate-Intensity Continuous Training on Body Composition, Cardiometabolic Profile, and Physical Capacity in Older Women
Bibliographic record
Abstract
Objectives : To compare the effect of low-volume high-intensity interval training (HIIT) with moderate-intensity continuous training (MICT) on fat mass, cardiometabolic profile, and physical capacity, and confirm its feasibility in older women. Methods : Inactive older women (60–75 years) were randomly assigned to 8 weeks of either HIIT (75 min/week; n = 9) or MICT (150 min/week; n = 9). Body composition, fasting metabolic profile, cardiovascular risk (Framingham score), and physical capacity (senior fitness test, peak oxygen uptake) were assessed before and after the intervention. Feasibility was evaluated with completion rate (training compliance; dropout rate) and affective response (Feeling Scale; pre- and postexercise). Results : Total cholesterol level, non-high-density lipoprotein cholesterol level, and the Framingham risk score decreased in both groups ( p s ≤ .03). Although peak oxygen uptake remained unchanged, the 6-min walk test distance increased ( p < .0001), irrespective of the group. Completion rate and affective responses were not different between groups ( p s ≥ .38). Conclusion : A short-term HIIT program is feasible and provides as much benefits as MICT in older 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
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".