Smoking and cessation behaviours in a community sample with type-2 diabetes: associations with depression
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».