The aim of our study was to assess university students’ sleep quality and fatigue before and during the academic exam period and identify potential associated factors. A Web-based survey was completed by 940 students of 20 different Tertiary Institutions including demographics, sleep habits, exercise, caffeine, tobacco, alcohol use, subjective sleep quality (Pittsburgh Sleep Quality Index—PSQI), and fatigue (Fatigue severity scale—FSS) at the beginning of the semester and during the examination period. During the exam period, PSQI (8.9 vs. 6.1, p < 0.001) and FSS scores (36.9 vs. 32.7, p < 0.001) were significantly elevated compared to the pre-exam period. An increase in the PSQI score was associated with age (β = 0.111, p = 0.011), presence of chronic disease (β = 0.914, p = 0.006), and depressive symptoms (β = 0.459, p = 0.001). An increase in the FSS score was associated with female gender (β = 1.658, p < 0.001), age, (β = 0.198, p = 0.010), increase in smoking (β = 1.7, p = 0.029), coffee/energy drinks consumption (β = 1.988, p < 0.001), decreased levels of physical exercise (β = 1.660, p < 0.001), and depressive symptoms (β = 2.526, p < 0.001). In conclusion, our findings indicate that exam periods have a negative impact on the sleep quality and fatigue levels of university students. Potential factors were identified that could contribute to the formulation of strategies for improved sleep quality and wellness.

Background and Objectives: To evaluate the influence of obstructive sleep apnea (OSA)-related symptoms on prevalent cardiovascular disease (CVD) in a large clinical population of patients. Materials and Methods: A total of 2127 patients (mean age 55 years, 24% women) underwent diagnostic polysomnography and were evaluated using the Epworth sleepiness scale (ESS), the Athens Insomnia Scale (AIS), and the Beck Depression Inventory (BDI). We investigated the predictive value of OSA-associated symptoms for prevalent cardiovascular disease, after adjustment for relevant confounding factors including age, obesity, and co-morbidities. Results: Patients with OSA and CVD were older and had a higher Body Mass Index (BMI); the percentage of obese patients was also higher (83% vs. 70%, p < 0001). They also had greater neck, waist, and hip circumferences and a higher waist-to-hip ratio. Excessive daytime sleepiness (ESS ≥ 10) [odds ratio (95% CI) 1.112 (0.708–1.748), p = 0.64], insomnia symptoms (AIS ≥ 6) [odds ratio (95% CI) 0.748 (0.473–1.184), p = 0.21], frequent awakenings [odds ratio (95% CI) 1.599 (1.019–2.508), p = 0.06], and nocturia [odds ratio (95% CI) 1.359 (0.919–2.009), p = 0.124] were not associated with CVD after adjustment for the previous confounders. On the other hand, depressive symptoms (BDI ≥ 10) independently predicted prevalent CVD [odds ratio (95% CI) 1.476 (1.154–1.887), p = 0.002]. Further analysis in subgroups stratified by age, BMI, and gender demonstrated that depressive symptoms predicted prevalent CVD but only in the subgroup of younger (age group < 60 years), obese (BMI group ≥ 30), and male (OR = 1.959, 95% CI = 1.209–3.175, p = 0.006) OSA patients. Conclusions: OSA patients with CVD were more likely to complain of less typical OSA symptoms and depressive symptoms compared to patients without CVD in this large clinical patient cohort, supportingthecomplexity and heterogeneityof OSA.

Overlap syndrome (OVS) is a distinct clinical entity that seems to result in potential cardiovascular consequences. We aimed to estimate the prevalence and risk factors for OVS in OSA patients and analyze clinical and PSG characteristics associated with OVS. In this cross-sectional study, 2616 patients evaluated for OSA underwent type-1 polysomnography (PSG). They were grouped as pure OSA (AHI > 15/h) and OVS patients. Demographics, PSG data, pulmonary function tests and arterial blood gases (ABGs) were compared between groups after adjustments for confounders. OSA was diagnosed in 2108 out of 2616 patients. Of those, 398 (19%) had OVS. Independent predictors of OVS were older age [OR: 5.386 (4.153–6.987)], current/former smoking [OR: 11.577 (7.232–18.532)], BMI [OR: 2.901 (2.082–4.044)] and ABG measurements [PaCO₂ ≥ 45 OR: 4.648 (3.078–7.019), PO₂ [OR: 0.934 (0.920–0.949)], HCO₃⁻ [OR: 1.196 (1.133–1.263), all p < 0.001]. OVS was also associated with prevalent hypertension [OR: 1.345 (1.030–1.758), p = 0.03] and cardiovascular disease [OR: 1.617 (1.229–2.126), p < 0.001], depressive symptoms [OR: 1.741 (1.230–2.465), p = 0.002] and nocturia [OR: 1.944 (1.378–2.742), p < 0.001], as well as with indices of OSA severity. Disturbances in sleep architecture were more prominent in OVS expressed by lower %N3 and REM% and higher arousal index. Our data suggest that OVS is prevalent among OSA patients, with distinct clinical and PSG characteristics. These characteristics could be utilized as predictive factors for early identification and further evaluation of these patients towards desirable patient-reported outcomes.

Background/Objectives: Factors underlying excessive daytime sleepiness (EDS) in obstructive sleep apnea (OSA) are not fully understood. We investigated whether polysomnography (PSG) parameters differed between non-sleepy and sleepy (based on the Epworth Sleepiness Scale (ESS)) OSA patients with the same disease severity, which may play a role in the presence of EDS. Methods: A total of 1307 patients, without cardiovascular, metabolic, respiratory, or inflammatory comorbidities, diagnosed with OSA (apnea–hypopnea index (AHI) ≥ 5 per hour of sleep) with type 1 PSG were included. Based on the AHI, patients were classified into mild- (AHI 5–14.9, n = 236), moderate- (AHI 15–29.9, n = 367), and severe-OSA (AHI ≥ 30, n = 704) groups. These groups were further divided into two subgroups based on the ESS, the most convenient and widely used tool to assess excessive daytime sleepiness: sleepy (ESS > 10) and non-sleepy (ESS ≤ 10). PSG data were compared between groups, and multivariable logistic regression was used to identify differences after adjustment for confounders. Results: For the entire population, male sex, younger age, obesity, depression, increased wakefulness after sleep onset (WASO), the arousal index, shorter sleep latency, and all indices of OSA severity (AHI, oxygen desaturation index, mean and lowest resting room air pulse oximetry (SpO₂), and sleep time with oxygen saturation < 90% (TST90)) were significantly associated with EDS. The arousal index consistently showed a strong association with EDS across all OSA severity groups. Moderate-OSA sleepy patients were younger, with shorter sleep latency and increased indices of OSA severity, excluding the AHI. Severe-OSA sleepy patients were younger, males, and obese; had depression, decreased slow-wave sleep (SWS) and sleep latency, and increased WASO; and presented an increase in all indices of OSA severity. Conclusions: Our results suggest that male sex, younger age, obesity, the presence of depression, WASO, lower sleep efficiency, the arousal index, and all indices of OSA severity may account for the presence or absence of EDS in OSA patients and could be useful for exploring the underlying pathophysiological mechanisms for precision medicine.

The aim of our study was to assess university students’ sleep quality and fatigue before and during the academic exam period and identify potential associated factors. A Web-based survey was completed by 940 students of 20 different Tertiary Institutions including demographics, sleep habits, exercise, caffeine, tobacco, alcohol use, subjective sleep quality (Pittsburgh Sleep Quality Index - PSQI) and fatigue (Fatigue severity scale – FSS) at the beginning of semester and at examination period. During exam period, PSQI (8.9 vs 6.1, p<0.001) and FSS scores (36.9 vs 32.7, p<0.001) were significantly elevated compared to pre-exam period. Increase of PSQI score was associated with age (β=0.111, p=0.011), presence of chronic disease (β=0.914, p=0.006), and depressive symptoms (β=0.459, p=0.001). Increase of FSS score was associated with female gender (β=1.658, p<0.001), age, (β=0.198, p=0.010), increase in smoking (β=1.7, p=0.029), coffee/energy drinks consumption (β=1.988, p<0.001), decreased levels of physical exercise (β=1.660, p<0.001), and depressive symptoms (β=2.526, p<0.001). In conclusion, our results show that sleep quality and fatigue are affected in university students during exam periods. Potential factors were identified which may lead to development of strategies for better quality of sleep, and wellbeing.

Background/Objectives: There are controversies regarding the effect of obstructive sleep apnea (OSA) treatment with positive airway pressure (PAP) on diabetes-related outcomes. Therefore, we aimed to explore the association of PAP adherence with diabetes-related outcomes in patients with type 2 diabetes mellitus (T2DM) and OSA. Methods: In this prospective study, we included T2DM patients diagnosed with OSA during an 8-year period (2015–2023). PAP adherence (optimal usage for > 6 h/night for ≥ 70% of nights), hemoglobin A1c (HbA1c), body mass index (BMI), systolic (SBP) and diastolic blood pressure (DBP), daytime sleepiness (Epworth Sleepiness Scale—ESS) and cardiovascular (CVD) events were recorded. Comparisons of the change in diabetes-related outcomes (follow-up to baseline) in the adherent and non-adherent groups were assessed by analysis of covariance to adjust for relevant confounders. Results: Of the 355 patients included, 199 (56%) were PAP adherent. The mean follow-up period was 5.5 years. At the end of the follow up period, the PAP adherent group achieved a greater decrease in HbA1c levels (−1.4 vs. −0.3), SBP (−10.1 vs. −5.5), DBP (−2.9 vs. −0.55) and ESS (−5.9 vs. −4.2) compared to the non-adherent groups. Conclusions: Achieving optimal PAP adherence in patients with T2DM and OSA was associated with improved diabetes-related outcomes. Therefore, current practices need to be modified to incorporate systematic assessment and treatment of OSA in these patients.