Comparison of 10 × 1-minute high-intensity interval training (HIIT) versus 4 × 4-minute HIIT on glucose control and variability in females with type 2 diabetes
Bibliographic record
Abstract
Two high-intensity interval training (HIIT) regimens are often used in research and clinical settings. Yet, there has been no direct comparison to determine if one can improve glucose control and variability to a greater extent in individuals living with type 2 diabetes (T2D). Fourteen older females with T2D participated in a semi-randomized control trial where HIIT10 (10 × 1-min intervals at 90% heart rate max; HRmax) and HIIT4 (4 × 4-min intervals at 90% of HRmax) were compared to a control condition (CON; no exercise). Continuous glucose monitoring was used to assess glucose control and variability over 24 h after each condition. Both HIIT10 (−2.1 ± 1.1 mmol/L) and HIIT4 (−2.1 ± 1.3 mmol/L) acutely lowered glucose compared to CON (−0.7 ± 0.8 mmol/L; p = 0.001), with no difference between exercise conditions. This glucose-lowering effect did not persist over the 24-h post-exercise period, as both mean glucose ( p = 0.751) and glucose variability ( p = 0.168) were not significantly different among conditions. However, exploratory analyses focusing on individuals with less optimal glucose control (above median 24-h mean glucose in the CON condition; n = 7) revealed that 24-h mean glucose (7.4 [7.14–8.92] vs. 8.4 [7.5–9.9] mmol/L; p = 0.048), glucose variability ( p = 0.010), and peak glucose ( p = 0.048) were lower following HIIT10 compared to CON, while HIIT4 reduced time spent in moderate hyperglycemia compared to CON ( p = 0.023). Both HIIT10 and HIIT4 acutely lower glycemia, but the effect does not persist over 24 h. However, in individuals with worse glucose control, HIIT10 may improve mean 24-h glucose and glycemic variability, while HIIT4 may reduce time spent in moderate hyperglycemia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".