Mood and sleep in young adults with type 1 diabetes
Bibliographic record
Abstract
OBJECTIVE: Depression is associated with sleep problems, and both are associated with higher HbA1c in people with type 1 diabetes (T1D). However, little is known about depressed mood and sleep in young adults with T1D. The aims of this secondary analysis were to provide descriptive statistics of multiple aspects of mood and sleep in young adults with T1D and evaluate associations of depressed mood, diabetes distress, and sleep quality and duration. METHODS: At baseline of a behavioral intervention trial, young adults with T1D completed self-report measures of sleep, diabetes distress, and depressive symptoms. We described sleep metrics across racial/ethnic groups and conducted hierarchical regression models to evaluate associations of depressive symptoms and diabetes distress with sleep quality and duration, adjusting for demographic and clinical factors. RESULTS: Participants (n = 100) were 58% female, 12% Black/African American, 25% Hispanic/Latine, 54% non-Hispanic White, 9% multiple or another race/ethnicity, and 34% had public or no insurance. Mean HbA1c was 8.8 ± 2.0%. Approximately one-fifth of participants reported elevated depressive symptoms (19%) or high diabetes distress (20%), 27% reported poor sleep quality, and 28% reported <7 hr of sleep per night. Both regression models were significant, with diabetes distress but not depressive symptoms significantly associated with sleep quality (β = .38, p = .003) and duration (β = .32, p = .016) after controlling for other variables. CONCLUSIONS: As diabetes-specific mood may be particularly relevant for sleep among young adults with T1D, routine screening and behavioral support for sleep health and diabetes distress may be warranted.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".