Diabetes distress is linked with worsening diabetes management over time in adults with Type 1 diabetes
Bibliographic record
Abstract
Abstract Aim To determine the cross‐sectional and longitudinal associations between diabetes distress and diabetes management. Methods In a non‐interventional study, 224 adults with Type 1 diabetes were assessed for diabetes distress, missed insulin boluses, hypoglycaemic episodes, and HbA1c at baseline and 9 months. Results At baseline, greater distress was associated with higher HbA1c and a greater percentage of missed insulin boluses. Longitudinally, elevated baseline distress was related to increased missed insulin boluses, and decreases in distress were associated with decreases in HbA1c. In supplementary analyses, neither depression symptoms nor a diagnosis of major depressive disorder was associated with missed insulin boluses, HbA1c or hypoglycaemic episodes in cross‐sectional or longitudinal analyses. Conclusions Significant cross‐sectional and longitudinal associations were found between diabetes distress and management; in contrast, no parallel associations were found for major depressive disorder or depression symptoms. Findings suggest that elevated distress may lead to more missed insulin boluses over time, suggesting a potential intervention target. The covarying association between distress and HbA1c points to the complex and likely interactive associations between these constructs. Findings highlight the need to address distress as an integral part of diabetes management in routine care.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".