Whole body vibration training as an exercise intervention strategy for individuals with type 2 diabetes
Bibliographic record
Abstract
Type 2 diabetes has become increasingly prevalent in recent years. Physical activity has been shown to be a central component to both prevention and management of the disease, and both aerobic and resistance training have been effective at acutely lowering blood glucose levels and improving glycemic control long term. The Canadian Diabetes Association (CDA) guidelines recommend approximately 150 minutes of moderate to vigorous aerobic exercise weekly, and an additional 2 or 3 resistance training sessions, but it is clear that these guidelines are not being met, as individuals with diabetes have been shown to be even less physically active than their non-diabetic counterparts. Health care practitioners are looking towards alternative forms of exercise which may improve adherence rates among this population. Recently, whole body vibration (VIB) has been explored as a potential exercise mode. VIB has been suggested to improve muscle strength and metabolic rate, and some companies have even claimed it can do so in half the time compared to traditional exercise regimes. The purpose of this study was to explore the effects of VIB on blood glucose levels and metabolic cost in individuals with type 2 diabetes. The intervention involved comparing 15 minutes of VIB, a 30 minutes resistance band (RB) session and a seated control condition. It was found that VIB led to a greater metabolic cost and increased energy expenditure, and both exercise modes resulted in a similar decrease in blood glucose. These results suggest VIB may be an effective strategy for type 2 diabetes, however more research is needed to verify the acute and long term effects of VIB in this population.
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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 | 0.000 |
| 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) | 0.002 | 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".