A comparison of the health benefits of reduced-exertion high-intensity interval training (REHIT) and moderate-intensity walking in type 2 diabetes patients
Bibliographic record
Abstract
Reduced-exertion high-intensity interval training (REHIT) is a genuinely time-efficient intervention that can improve aerobic capacity and insulin sensitivity in sedentary individuals. The present study compared the effects of REHIT and moderate-intensity walking on health markers in patients with type 2 diabetes (T2D) in a counter-balanced crossover study. Sixteen men with T2D (mean ± SD age: 55 ± 5 years, body mass index: 30.6 ± 2.8 kg·m−2, maximal aerobic capacity: 27 ± 4 mL·kg−1·min−1) completed 8 weeks of REHIT (three 10-min low-intensity cycling sessions/week with two “all-out” 10–20-s sprints) and 8 weeks of moderate-intensity walking (five 30-min sessions/week at an intensity corresponding to 40%–55% of heart-rate reserve), with a 2-month wash-out period between interventions. Before and after each intervention, participants underwent an incremental fitness test, an oral glucose tolerance test (OGTT), a whole-body dual-energy X-ray absorptiometry scan, and continuous glucose monitoring. REHIT was associated with a significantly larger increase in maximal aerobic capacity compared with walking (7% vs. 1%; time × intervention interaction effect: p < 0.05). Both REHIT and walking decreased resting mean arterial pressure (−4%; main effect of time: p < 0.05) and plasma fructosamine (−5%; main effect of time: p < 0.05). Neither intervention significantly improved OGTT-derived measures of insulin sensitivity, glycaemic control measured using continuous glucose monitors, blood lipid profile, or body composition. We conclude that REHIT is superior to a 5-fold larger volume of moderate-intensity walking in improving aerobic fitness, but similar to walking REHIT is not an effective intervention for improving insulin sensitivity or glycaemic control in T2D patients in the short term.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".