The relationship between daytime napping and glycemic control in people with type 2 diabetes
Bibliographic record
Abstract
Aim: To examine the association between napping characteristics and glycemic control in people with type 2 diabetes. Design: This study used a cross-sectional design. Methods: A convenience sample of people with type 2 diabetes (N=226) were included. Glycemic control was indicated by HbA1c which was measured by A1C Now®+. Napping characteristics including napping frequency, duration, timing, and type were measured by validated questionnaires. Other variables, such as insomnia, cognitive impairment, and depression were measured by the Insomnia Severity Index, Montreal Cognitive Assessment, and Patient Health Questionnaire-9, respectively. Multivariate linear regression analyses were performed. Results: The sample consisted of 122 women (54.0%), with a median age of 67 years. Their median HbA1c was 6.8%. No significant relationship was found between napping frequency and HbA1c. Among nappers, after controlling for covariates, long napping duration (≥60 min) and morning napping were both associated with poorer glycemic control. Compared with appetitive napping, restorative napping was associated with better glycemic control. Conclusion: Daytime napping (e.g., duration and type) is an important modifiable factor for glycemic control in people with type 2 diabetes. This study provides new insights into the relationship between napping and glucose management among people with diabetes.
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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 | 0.002 |
| 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) | 0.001 | 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".