Acute and Chronic Effects of Low-Volume High-Intensity Interval Training Compared to Moderate-Intensity Continuous Training on Glycemic Control and Body Composition in Older Women with Type 2 Diabetes
Bibliographic record
Abstract
Objective: To compare the acute and chronic effects of low-volume high-intensity interval training (HIIT) to moderate-intensity continuous training (MICT) on glycemic control, body composition and continuous glucose monitoring (CGM) in older women with type 2 diabetes (T2D). Methods: Thirty older women (68 ± 5 years) with T2D were randomized in two groups—HIIT (75 min/week) or MICT (150 min/week). Glucose homeostasis (A1c, glucose, insulin, HOMA-IR2) and body composition (iDXA) were measured before and after the 12-week exercise intervention. During the first and last week of training (24-h before and 48-h after exercise), the following CGM-derived data were measured: 24-h and peak glucose levels, glucose variability and time spent in hypoglycemia as well as severe and mild hyperglycemia. Results: While lean body mass increased (p = 0.035), total and trunk fat mass decreased (p ≤ 0.007), without any difference between groups (p ≥ 0.81). Fasting glucose levels (p = 0.001) and A1c (p = 0.014) significantly improved in MICT only, with a significant difference between groups for fasting glucose (p = 0.02). Neither HIIT nor MICT impacted CGM-derived data at week 1 (p ≥ 0.25). However, 24-h and peak glucose levels, as well as time spent in mild hyperglycemia, decreased in HIIT at week 12 (p ≤ 0.03). Conclusion: These results suggest that 12 weeks of low-volume HIIT is enough to provide similar benefit to MICT for body composition and improve the acute effect of exercise when measured with CGM.
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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) | 0.001 | 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".