Effect of Elliptical High Intensity Interval Training on Metabolic Risk Factor in Pre- and Type 2 Diabetes Patients: A Pilot Study
Bibliographic record
Abstract
OBJECTIVE: The purpose of the current study was to examine the impact of 12 weeks of elliptical high intensity interval training (HIIT) on metabolic risk factors and body composition in pre- and type 2 diabetes patients. METHODS: Sixteen pre- (n = 8) and type 2 diabetes (n = 8) participants completed this study. Fasting blood glucose, HbA1c, anthropometric measurements, body composition (DXA), blood pressure, resting heart rate, VO2max, and dietary factors, as well as total and physical activity energy expenditure, were measured. The HIIT program on the elliptical was performed 3 times a week for 12 weeks. RESULTS: After the intervention, we observed a significant improvement for fasting blood glucose, waist and hip circumference, appendicular fat mass, leg lean body mass and appendicular lean body mass, systolic blood pressure, resting heart rate, and VO2max (P < .05). In addition, we noted a lower tendency for leg fat mass (P = .06) and diastolic blood pressure (P = .05) as well as a higher tendency for total energy expenditure (P = .06) after the intervention. CONCLUSION: The current study indicates that elliptical HIIT seems to improve metabolic risk factors and body composition in pre- and type 2 diabetes patients.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".