Comparison of performance and health indicators between perimenopausal and postmenopausal obese women: the effect of high-intensity interval training (HIIT)
Bibliographic record
Abstract
OBJECTIVE: This study examined the effects of 6 weeks of high-intensity interval training (HIIT) on performance and health indicators in obese perimenopausal (PERIM) women and similarly aged and older postmenopausal women (POSTM1 and POSTM2, respectively). METHODS: Sixteen PERIM women (average age 49.6 y), 21 POSTM1 women (average age 50.6 y), and 19 POSTM2 women (average age 69.6 y) completed a 6-week HIIT intervention. Anthropometric parameters, mechanical efficiency (ME in %), lipid oxidation (LO in %), and low and high spectral frequencies (LF[ms2] and HF[ms2]) were computed pre- and postintervention. RESULTS: PERIM women showed a significantly higher VO2max preintervention compared to POSTM1 and 2 (P < 0.01). Moreover, HF, LF, and the LF/HF ratio differed significantly in PERIM women preintervention compared to POSTM1 and 2 (P < 0.01, respectively). After 6 weeks, a significant decrease in anthropometric variables was observed for all groups (P < 0.01). The VO2max in mL/kg/min increased for all groups (P < 0.01). Multiple linear regression analysis demonstrated that age contributed significantly to differences in VO2max values between groups preintervention (r = 0.72). This model accounted for 34% (r2 = 0.34) of the variation. On the other hand, menopause status was an independent predictor of LO, accounting for 38% of the variation, as well as of HF (33%), LF (29%), and the LF/HF ratio (24%). After HIIT, no age or menopause effect was detected for these independent variables. CONCLUSIONS: Maximal oxygen consumption, HF, LF, and the LF/HF ratio differ among women according to the menopausal status and age. A 6-week HIIT intervention improved many health and performance parameters and reduced the effects of menopause and age.
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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".