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.

In the past, there was limited research relating to the role of sleep in respiratory diseases. Physicians treating these patients tended to focus mainly on the daily disabling symptoms, overlooking the possible significant role of coexisting sleep disorders such as obstructive sleep apnoea (OSA).Nowadays, OSA has been recognised as an important, highly prevalent comorbidity for respiratory diseases such as COPD, asthma and interstitial lung diseases (ILDs). Overlap syndrome refers to the coexistence of chronic respiratory disease and OSA in the same patient. Although, in the past, overlap syndromes have been poorly studied, recent data underline that they result in increased morbidity and mortality compared with either underlying disorder alone. OSA and respiratory disease may be of different severity, and this, along with the existence of various clinical phenotypes, points to the necessity of an individualised therapeutic plan. Early recognition and OSA management could offer key benefits, such as improved sleep, quality of life and disease outcomes.Educational aims: Describe pathophysiological aspects of OSA in chronic respiratory diseases such as COPD, asthma and ILDs. Understand the bidirectional clinical importance when OSA coexists in chronic respiratory diseases. Review current knowledge of treatment strategies towards an individualised therapeutic plan resulting in patient-centric outcomes.

Introduction: Patients with overlap syndrome (OS), that is obstructive sleep apnea (OSA) and chronic obstructive pulmonary disease (COPD), are at increased risk of acute exacerbations related to COPD (AECOPD). We assessed the effect of CPAP compliance on AECOPD, symptoms and pulmonary function in OS patients.Methods: Consecutive OS patients underwent assessment at baseline and at 12 months under treatment with CPAP of: AECOPD and hospitalizations, COPD Assessment Test (CAT) and modified British Medical Research Council (mMRC) questionnaires, pulmonary function testing and 6‐min walking test (6MWT).Results: In total, 59 patients (54 males) with OS were followed for 12 months and divided post hoc according to CPAP compliance into: group A with good (≥4 h CPAP use/night, n = 29) and group B with poor (<4 h CPAP use/night, n = 30) CPAP compliance. At 12 months, group A showed improvements in FEV₁ (p = 0.024), total lung capacity (p = 0.024), RV/TLC (p = 0.003), 6MWT (p < 0.001) and CAT (p < 0.001). COPD exacerbations decreased in patients with good CPAP compliance from baseline to 12 months (17 before vs. 5 after, p = 0.001), but not in those with poor compliance (15 before vs. 15 after, p = 1). At multivariate regression analysis, COPD exacerbations were associated with poor CPAP compliance (β = 0.362, 95% CI: 0.075–0.649, p = 0.015).Conclusions: When compared to poorly compliant patients, OS patients with good CPAP compliance had a lower number of AECOPD and showed improved lung function and COPD related symptoms.

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.

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.