COMPARISON OF TWO MODALITIES OF EXERCISE ON THE HEALTH PROFILE IN OLDER WOMEN WITH ABDOMINAL OBESITY
Bibliographic record
Abstract
The impact of high-intensity interval training (HIIT) compared to the current exercise recommendations (moderate intensity continuous aerobic exercise; CONT) has to be verified in obese older women before being used by health professionals. Objective: The purpose of this study is to compare the effect of HIIT to CONT on body composition, metabolic profile and affective responses in obese elderly. Methods: A total of 20 older and sedentary women (60–75 years) with abdominal obesity (waist circumference ≥ 88 cm) are currently recruited and randomized to one of the following group: 1) HIIT (n=10); 2) CONT (n=10). All variables are measured before and after 8 weeks of intervention: Anthropometry (weight, height, body mass index), body composition (fat mass [FM], lean body mass [LBM], visceral adipose tissue [VAT]; DXA), metabolic profile (fasting lipid profile, glucose and insulin) and physical capacity (senior fitness test). Affective responses are measured before and after each training session. Preliminary Results: VAT tend to decrease in HIIT group only (p=0.07) while total FM remained unchanged. Moreover, HDL-C tend to increase in HIIT only (p=0.09) while LDL-C decreased (p=0.01) in both groups. Finally, while HbA1c tend to increase, total cholesterol tend to decrease in HIIT and CONT (both, p=0.08). Finally, affective response before and after each training were similar between HIIT and CONT (both p>0.48) and remained unchanged. Conclusion: Our preliminary results suggest that HIIT could provide better improvements (VAT and HDL-C) compared to the current exercise recommendations in physically inactive 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 | 0.000 | 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".