Glycaemic control and self‐management behaviours in Type 2 diabetes: results from a 1‐year longitudinal cohort study
Bibliographic record
Abstract
AIM: To better understand the associations between changes in self-management behaviours and glycaemic control. METHODS: We conducted a prospective observational study of 295 adult patients with Type 2 diabetes evaluated at baseline, 6 and 12 months. Four self-management behaviours were evaluated using the Summary of Diabetes Self-Care Activities instrument, which assesses healthy diet, physical activity, medication taking and self-monitoring of blood glucose. Using hierarchical linear regression models, we tested whether changes in self-management behaviours were associated with short-term (6-month) or long-term (12-month) changes in glycaemic control, after controlling for demographic and clinical characteristics. RESULTS: Improved diet was associated with a decrease in HbA1c level, both at 6 and 12 months. Improved medication taking was associated with short-term improvement in glycaemic control, while increased self-monitoring of blood glucose frequency was associated with a 12-month improvement in HbA1c . Completely stopping exercise after being physically active at baseline was associated with a rise in HbA1c level at 6-month follow-up. Interaction analysis indicated that a healthy diet benefitted all participant subgroups, but that medication taking was associated with glycaemic control only for participants living in poverty and more strongly for those with lower educational levels. Finally, a higher self-monitoring of blood glucose frequency was associated with better glycaemic control only in insulin-treated participants. CONCLUSIONS: Even after adjusting for potential confounders (including baseline HbA1c ), increased frequency of healthy diet, medication taking and self-monitoring of blood glucose were associated with improved HbA1c levels. These self-management behaviours should be regularly monitored to identify patients at risk of deterioration in glycaemic control. Barriers to optimum self-management should be removed, particularly among socio-economically disadvantaged populations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".