Psychosocial determinants of healthy sleep habits in adults with type 1 and type 2 diabetes
Bibliographic record
Abstract
Background: Many adults with type 1 (T1D) and type 2 diabetes (T2D) have inadequate sleep increasing their risk of hyperglycemia and developing complications. The objective was to identify psychosocial determinants of healthy sleep habits (HSH) among adults with T1D and T2D.Methods: The two HSH were: avoiding screen use in bed and having sleep regularity. Adults (≥18 years) with T1D and T2D were invited to complete an anonymous online survey. The questionnaires were based on the Reasoned Action Approach and formative qualitative research previously conducted in 56 adults with T1D and T2D. Habit was included as an additional variable for screen use in bed.Results: In total, 320 adults with diabetes (T1D: 39%; T2D: 61%) completed the questionnaires (screen use in bed: 174; sleep timing: 146). Close to 75% of participants reported screen use in bed and close to 90% reported sleep timing variability in the last month. Perceived behavioral control (PBC) to avoid screen use in bed (β = −0.4486, p < 0.0001), habit of using screens in bed (β = 0.4002; p < 0.0001), and age (β = −0.0202; p = 0.0086) were determinants of screen use in bed, and this model explained 71% of the variance. PBC for sleep regularity (β = −0.2909; p = 0.0004) and being female (β = 0.5057; p = 0.0069) were determinants of sleep timing variability, and this model explained 28% of the variance. The most important beliefs associated with each HSH were identified to obtain information to design targeted interventions.Conclusions: Few adults with diabetes have HSH. Screen use in bed was strongly influenced by habit and the results suggest that both HSH are not easy to adopt among adults with diabetes. Younger adults with diabetes should be prioritized for screen use in bed, while females with diabetes should be prioritized for sleep timing variability. Adults with diabetes should have access to behavior change interventions to encourage them to adopt HSH.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".