LONGITUDINAL CHANGES IN THE PREVALENCE OF THE EXCESSIVE DAYTIME SLEEPINESS IN TWO SASKATCHEWAN FIRST NATIONS COMMUNITIES
Notice bibliographique
Résumé
Background: Excessive daytime sleepiness (EDS) is a major public health issue that can reduce individuals’ work productivity and could be a sign for sleep disordered breathing such as obstructive sleep apnea (OSA). Objectives: 1) To determine predictors associated with EDS at baseline and follow-up; and 2) to investigate the longitudinal changes in the prevalence of EDS assessed by Epworth Sleepiness Scale (ESS) in two First Nations communities. Methods: The First Nations Sleep Health Project (FNSHP) was conducted in two phases in 2018 and 2022 in Saskatchewan. The survey collected information on demographics, socioeconomics, environment, and determinants of health. Two separate cross-sectional data analysis using baseline (n=573) and follow-up (n=346) surveys was conducted. For longitudinal analysis (n=919), multivariable logistic regression based on generalized estimating equations to account for within subject correlation due to repeated measurements at baseline and follow-up was employed. Results: Women made up 56% of respondents with an average age of 41 years (±15), while men comprised 44% with an average age of 39 years (±14.5). At baseline, individuals with heart disease had significantly increased odds of experiencing EDS (OR = 2.67; 95% CI: 1.37–5.23; p < 0.004). Ever-smokers who reported trouble sleeping due to coughing or snoring had significantly higher odds of EDS (p = 0.02), suggesting a confounding role of smoking in this relationship. At follow-up, ever-smokers had increased odds of EDS (OR = 3.39; 95% CI: 0.87–13.20). Among housing-related variables, higher crowd index was significantly associated with higher odds of EDS (OR = 3.38; 95% CI: 1.20–9.49). Obesity appeared to amplify the effect of depression on EDS, with obese individuals experiencing notably higher odds of EDS when also reporting depressive symptoms (OR = 4.67; 95% CI: 1.03–21.16). Longitudinal data analysis revealed that age, BMI, and smoking status were not significantly associated with EDS. Participants with heart conditions had 2.1 times higher odds of EDS compared to those without. Trouble sleeping due to coughing/snoring was also significantly associated with EDS (OR = 2.19; 95% CI: 1.43–3.35; p < 0.001). Significant interaction effects were observed between time and both sex and depression. At follow-up, males had significantly higher odds of EDS compared to females. Participants with depression at follow-up had substantially higher odds of EDS (OR = 4.09; 95% CI: 1.67–10.00) compared to those without depression at baseline. Conclusion: This study observed a modest increase in the prevalence of EDS over a three-year period. Longitudinal analysis revealed that changes in EDS prevalence were most strongly associated with male sex and co-morbid conditions, including heart disease, trouble sleeping due to coughing or snoring, and depression. These findings highlight the multifactorial nature of EDS and underscore the importance of considering both biological and environmental influences over time.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».