Smoking and cessation behaviours in a community sample with type-2 diabetes: associations with depression
Bibliographic record
Abstract
Background: Smoking is a highly prevalent behaviour practiced worldwide, associated with high levels of illness morbidity and mortality. It has been associated with the incidence of type-2 diabetes, as well as the progression of diabetes complications and increased disease specific and all-cause mortality. Smoking cessation is an important self-care recommendation in diabetes treatment guidelines, although it appears many continue to smoke. Moreover, smoking has been associated with depression. Depression is twice a prevalent in those with diabetes, and has been linked with poor regimen adherence, increased complications, morbidity and mortality. Little is currently known about the association or impact of smoking and depression in populations with chronic illnesses, and specifically in those with type-2 diabetes. Aims: Using a Canadian community based sample with type-2 diabetes, to determine: 1) Important differences in sociodemographic, health and disease related characteristics across smoking status; 2) Investigating the relationship between smoking status and depression while controlling for potential confounding factors; 3) Determining differences in the population according to cessation status and cessation attempts; and 4) Determining if there is a link between depression status and smoking cessation. Results: Smoking prevalence was similar to rates found in the general population, and appeared to be stable over a 4-year period. Current moderate-heavy smokers differed on sociodemographic characteristics, and were more likely to have more diabetes complications, more comorbid chronic illness and be less physically active. Moderate-heavy smoking was associated with depression in both cross-sectional and longitudinal analyses, controlling for baseline depression. Smoking cessation status also differed across sociodemographic characteristics. Unsuccessful quitters were more likely to rate their health as fair/poor and report more disability affected days in the past month. Finally, unsuccessful quitters were significantly associated with depression syndrome as compared to successful quitters, after controlling for sociodemographic, health and disease related variables. Conclusion: Consistent with findings from the general population, current smokers, and specifically current heavy daily smoking was associated with elevated symptoms of depression. This association appeared to be stable over time, producing a number of negative health and functional outcomes in these individuals. Given this increased risk of morbidity and mortality faced by individual's with diabetes, this strong association of smoking and depression is that much more dangerous. Clinician's should therefore counsel these individuals to give up smoking as soon as possible, following diabetes treatment regimen guidelines. In addition, there is the prevailing notion that individuals with depression may be unmotivated to quit smoking and therefore counselling these individuals might be fruitless. In our study, the association between smoking and depression was extended to include unsuccessful quitters, who also had elevated prevalence rates of depression compared to successful quitters and non-attempters. That successful quitters had lower depression than those who continued to smoke replicates findings from the general population. We did however extend this finding by contrasting those unsuccessful quitters to non-attempters. In our study, unsuccessful quitters had the highest prevalence of depression. This would appear to indicate that those with depression who smoke may be motivated to quit, but unable to do successfully accomplish this task. Clinician's should therefore be prepared to assess and offer smoking cessation advice to those with depression, while also preparing to provide these individuals with additional support during the quit process.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".