Time spent on self‐management by people with diabetes: results from the population‐based KORA survey in Germany
Bibliographic record
Abstract
Abstract Aims Time needed for health‐related activities in people with diabetes is assumed to be substantial, yet available data are limited. Time spent on self‐management and associated factors was analysed using cross‐sectional data from people with diagnosed diabetes enrolled in a population‐based study. Methods Mean total time spent on self‐management activities was estimated using a questionnaire for all participants with diagnosed diabetes in the KORA FF4 study ( n = 227, 57% men, mean age 69.7, sd 9.9 years). Multiple two‐part regression models were fitted to evaluate associated factors. Multiple imputation was performed to adjust for bias due to missing values. Results Some 86% of participants reported spending time on self‐management activities during the past week. Over the entire sample, a mean of 149 ( sd 241) min/week were spent on self‐management‐activities. People with insulin or oral anti‐hyperglycaemic drug treatment, better diabetes education, HbA 1c 48 to < 58 mmol/mol (6.5% to < 7.5%) or lower quality of life, spent more time on self‐management activities. For example, people without anti‐hyperglycaemic medication invested 66 min/week in self‐management, whereas those taking insulin and oral anti‐hyperglycaemic drugs invested 269 min/week (adjusted ratio 4.34, 95% confidence interval 1.85–10.18). Conclusions Time spent on self‐management activities by people with diabetes was substantial and varied with an individual's characteristics. Because of the small sample size and missing values, the results should be interpreted in an explorative manner. Nevertheless, time needed for self‐management activities should be routinely considered because it may affect diabetes self‐care and quality of life.
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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 | 0.001 | 0.000 |
| 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 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".