Cross-sectional and longitudinal analysis of the association between diabetes distress and sociodemographic, lifestyle and diabetes related factors in a sample of adults with type 2 diabetes
Notice bibliographique
Résumé
INTRODUCTION.Moderate to severe diabetes distress (DD) is a common mental health comorbidity among adults with type 2 diabetes and is characterized by negative emotions, worries and fears relating to living with diabetes and its management. Cross-sectional studies find DD is strongly and independently correlated with poorer diabetes health and disease management however, few studies have examined how lifestyle behaviors such as smoking, physical activity and alcohol consumption relate to DD. Additionally, the vast majority of the published literature on DD has been cross-sectional, and relatively little is known about the pattern of change of DD symptoms over long periods of time. The objectives of the following thesis are two-fold: 1) to examine the association between moderate and severe DD and lifestyle behaviors (physical activity, smoking, alcohol consumption) according to gender, 2) to identify and describe longitudinal changes in DD symptoms over 4 years of follow-up time. GENERAL METHODS. The data for this thesis were derived from the Evaluation of Diabetes Treatment study, a longitudinal community based survey of Canadian adults with type 2 diabetes (2011-2014). Participants were recruited using mixed methods sampling and were considered eligible if they had a doctor diagnosis of type 2 diabetes (≤10 years), were insulin-naïve, between 40 and 75 years of age and from Quebec, Canada. A total of 2,028 adults completed the baseline interview and provided information on DD, lifestyle behaviors, mental health, sociodemographic and diabetes related factors. RESULTS. Cross-sectional analyses: A series of multinomial logistic regression analyses were used to evaluate the association between DD and lifestyle behaviors. Effects estimates can be interpreted as probability ratios (PR). In females, physical inactivity was associated with an increased likelihood of moderate distress (PR: 2.2 (1.49-3.24)) and severe distress (PR: 1.80 (1.00-3.24)). In males, only severe distress was associated with physical inactivity (PR: 1.92 (1.00-3.66)). Current smoking was associated with a greater probability of severe distress in both males and females; however this effect was stronger in male smokers (PR: 3.0 (1.54-5.84)) than female smokers (PR: 1.32 (0.67-2.60)). No association was found between alcohol consumption and DD in females, however, in males, frequent alcohol consumption was associated with a reduced probability of moderate (PR: 0.56 (0.34-0.91)) and severe distress (PR: 0.47 (0.21-1.06)). Longitudinal analyses: We used a latent class group modelling approach to uncover trajectories of DD over 4 years of follow up time. Five distinct trajectories of DD were identified. Trajectories 1 and 2 described participants with persistently low distress (61% of sample) or persistently low, but at risk levels of distress (22% of sample). Trajectory 3 (7.5% of sample) included participants with moderate levels of distress that decreased over time. Trajectories 4 (6.5% of sample) and 5 (2.4% of sample) consisted of participants with moderate, but increasing levels of distress and persistently high (severe) levels of distress, respectively. Additionally, participants following trajectories of moderate and severe distress tended to have worse baseline mental and physical health.CONCLUSIONS.The findings from this thesis suggest an important role for DD in diabetes-related health behaviors and diabetes disease management. Additionally, the results indicate that for a subset of individuals, DD is a persistent condition over 4 years of follow-up time, while for other patients; DD symptoms may worsen over time. The dynamic nature of DD suggests that individuals may differ in their risk of developing negative health outcomes, and interventions aimed at reducing DD levels may not be warranted for all patients with DD symptoms. Screening for DD in high-risk patient groups may be an important consideration for medical health professionals.
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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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».