Improved Glycemic Control in Adults with Type 2 Diabetes Is More Strongly Associated with Exercise Duration than with Volume, Frequency, or Consistency
Bibliographic record
Abstract
PURPOSE: Exercise interventions play a pivotal role in managing type 2 diabetes (T2D), yielding significant benefits in glycemic control. Despite the recognized role of exercise duration, volume, frequency, and consistency, the literature remains discrepant on which exerts the greatest effect. The purpose of this secondary analysis was to determine the relationship between exercise duration, volume, frequency, and consistency and markers of glycemic control (HbA1c and continuous glucose monitoring metrics) following a 26-wk mHealth intervention. METHODS: Inactive adults with newly diagnosed (<2 yr) T2D ( n = 58) completed blood and 14-day continuous glucose monitoring testing before and after a 26-wk personalized exercise intervention. Raw exercise data from fitness watches were extracted for each session. Duration, volume, frequency, and consistency were calculated for the full intervention and for the first 13 wk (greater support) and last 13 wk (reduced support). RESULTS: Average session duration (57 ± 36 min) significantly predicted HbA1c (β = -0.23 [0.07], P = 0.002), 24-h mean glucose (β = -0.03 [0.01], P = 0.01), and glycemic variability (standard deviation; β = -0.01 [0.0], P = 0.003; Beta coefficients are reported with standard errors). Total exercise time during the first 13 wk (2931 ± 3362 min) also predicted HbA1c (β = -0.001 [0.0], P = 0.01), mean glucose (β = -0.002 [0.0], P = 0.01), and glycemic variability (β = -0.005 [0.0], P = 0.03). No other exercise metrics significantly predicted outcomes. CONCLUSIONS: Average session duration and exercise time accumulated early in the intervention were the only significant predictors of improvements in HbA1c, mean glucose, and glucose variability. These findings suggest that promoting increased exercise duration, independent of type or intensity, may improve glycemic control among individuals with newly diagnosed T2D. TRIAL REGISTRATION: ClinicalTrials.gov NCT04653532.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".