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Background: Sleep disturbance is a major health issue among people with type 2 diabetes mellitus (T2DM). The Pittsburgh Sleep Quality Index (PSQI) has been the most widely used instrument to measure subjective sleep disturbance.
Aim: The aim of this study was to determine the impact of sleeping factor structure of the PSQI as potential predictor for glycosylated hemoglobin A1c (HbA1C) among people living with T2DM in the Turkish community to facilitate its use in the clinical practice and research.
Subjects And Methods: This is a cross-sectional study and participants were between the age group of 25 and 65 years old who visited the diabetes and endocrinology department of Mega Medipol University Teaching Hospital, Istanbul. The PSQI was conducted on 871 patients with T2DM. Good sleep quality was defined as PSQI score <5. Multivariate logistic regression analysis was used to estimate the associated risk factors for the T2DM.
Results: The current study showed significant differences between male and female patients with respect to their age in years, body mass index (BMI) (kg/m), physical activity, smoking habit, sheesha smoking, income, family history of metabolic syndrome, coronary heart disease (CHD), and PSQI. The results revealed significant differences between HbA1c ≤7 and females and HbA1c >7 T2DM patients with respect to gender, BMI (kg/m), CHD, and PSQI. The study demonstrated significant differences between sleeping categories PSQI as good, average, and poor sleeping among T2DM patients with respect to age and gender. Meanwhile, significant differences were reported between sleeping categories among T2DM patients with respect to their: number of sleeping hours, wake-up time, sleeping time, HbA1c, fasting blood glucose, uric acid, and systolic and diastolic blood pressure. This study showed very strong statistically significant correlations between low HbA1c and poor sleep quality in patients with T2DM patients, including subjective sleep quality r = 0.763, sleep latency r = 0.327, sleep duration r = 0.472, habitual sleep efficiency r = 0.575, sleep disturbances r = 0.564, use of sleep medication r = 0.728, and daytime dysfunction r = 0.734. Multivariate stepwise logistic regression analysis revealed that Vitamin D (mmol/L) (P < 0.001), HbA1c (P < 0.001), duration of DM (P < 0.001), uric acid (mmol/L) (P < 0.001), systolic blood pressure mmHg (P = 0.006), diastolic blood pressure mmHg (P = 0.015), and BMI (P = 0.024) were considered at higher risk as the predictors for sleeping quality among T2DM patients.
Conclusion: The results suggest a strong positive correlation between PSQI with HbA1c levels, systolic and diastolic blood pressure, age, BMI, among type 2 diabetic patients. This study ascertains that poor sleep quality may be due to elevated level of HbA1c, metabolic syndrome, diabetes, obesity, and/or hypertension.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8015959 | PMC |
http://dx.doi.org/10.4103/aam.aam_51_19 | DOI Listing |
BMC Ophthalmol
December 2024
National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 3250027, China.
Objectives: To analyze the influence of daily activity-related factors associated with COVID-19 infection on the occurrence of acute angle closure (AAC).
Methods: A multicenter hospital-based study was conducted at 23 ophthalmic centers in 17 provincial-level regions across China to recruit patients with confirmed AAC during the post-lockdown time of COVID-19 (P-TOC) from Dec 7, 2022, to Jan 17, 2023, and three lockdown time of COVID-19 (TOC) periods, which included the TOC-2022 (Sep 7, 2022 - Dec 6, 2022), TOC-2021(Sep 7, 2021 - Jan 6, 2022) and TOC-2020 (Sep 7, 2020 - Jan 6, 2021). Patient information, including demographic, a questionnaire on daily activity changes during the AAC period, COVID-19 history, and eye examination results, was collected.
J Transl Med
December 2024
Department of Rheumatology and Immunology, Third Affiliated Hospital of Sun Yat-Sen University, 600 Tianhe Road, Tianhe District, Guangzhou, China.
Background: Corona virus disease 2019 (COVID-19) reinfection, particularly short-term reinfection, poses challenges to the management of rheumatic diseases and may increase adverse clinical outcomes. This study aims to develop machine learning models to predict and identify the risk of short-term COVID-19 reinfection in patients with rheumatic diseases.
Methods: We developed four prediction models using explainable machine learning to assess the risk of short-term COVID-19 reinfection in 543 patients with rheumatic diseases.
Background: Cardiovascular disease (CVD) prevalence varies widely among Asian American adults. The American Heart Association added healthy sleep to its metrics to define ideal cardiovascular health. Little is known about the association between sleep and CVD prevalence among Asian subgroups.
View Article and Find Full Text PDFJ Am Heart Assoc
December 2024
National Institute for Stroke and Applied Neurosciences, School of Clinical Sciences Auckland University of Technology Auckland New Zealand.
Background: Poststroke fatigue affects ≈50% of patients with stroke, causing significant personal, societal, and economic burden. In the FASTER (Fatigue After Stroke Educational Recovery) study, we assessed a group-based educational intervention for poststroke fatigue.
Methods And Results: Two hundred patients with clinically significant fatigue were included and randomized to either a general stroke education control or fatigue management group (FMG) intervention and assessed at baseline, 6 weeks, and 3 months.
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