The aim was to validate a new seven‐item “TASC” (Trøndelag Apnoea Score) proxy for obstructive sleep apnoea (OSA) against polysomnography in the general population. Objectives included validation against different polysomnographic criteria, stratification by age and gender, and estimation of OSA prevalence. From the fourth wave of the Trøndelag Health Study (HUNT4), 1,201 participants were randomly invited to a substudy focusing on sleep and headaches, of whom 232 accepted and 84 (64% women, mean age 55.0 years, and standard deviation 11.5 years) underwent polysomnography. The TASC proxy sums seven binary items for snoring, observed breathing pauses, restricted daytime activities, hypertension, body mass index (≥30 kg/m2), age (≥50 years), and gender (male). A single night of ambulatory (home) polysomnography was analysed using both the recommended and optional hypopnoea criteria of the American Academy of Sleep Medicine (AASM). We found 65% sensitivity and 87% specificity (Cohen’s κ = 0.53, 95% confidence interval 0.34−0.72) for TASC ≥ 3 against AHI ≥ 15 (recommended AASM criteria). Validity was similar against AHI ≥ 30 but lower against AHI ≥ 5 and against the optional AASM criteria. Sensitivity and overall validity were higher among men and those above 50 years of age. The prevalence of an apnoea‐hypopnoea index (AHI) of at least 5, 15, or 30 using the recommended (and optional) AASM criteria was 73% (46%), 37% (18%), or 15% (5%). A seven‐item TASC proxy for OSA showed good validity and may be useful in screening and epidemiological settings. Sensitivity, specificity, and validity vary considerably by cut‐off, by polysomnographic scoring criteria, and by gender and age strata.
Background. This study is aimed at determining the quality of life, mental health, and adherence to continuous positive airway pressure (CPAP) therapy for obstructive sleep apnea (OSA) among Thai OSA patients during the coronavirus disease 2019 (COVID-19) pandemic as this data has been lacking. Methods. A cross-sectional study was conducted at a university hospital between September 2021 and April 2022. OSA patients aged 18 years or older who required home CPAP treatment were included. Sleep Apnea Quality of Life Index (SAQLI) and Depression Anxiety Stress Scales-21 (DASS-21) were used to assess quality of life and mental health, respectively. Results. A total of 142 participants (62% male) were included, with a mean age of 54.4±14.7 years and a body mass index of 29.9±6.8 kg/m2. Polysomnographic data showed a mean apnea-hypopnea index of 48.0±32.4 events/hour and a mean lowest oxygen saturation of 79.2±12.2%. Severe OSA was observed in 66.9%. CPAP compliance was reported in 50.7%. The SAQLI score was 2.32±1.12. Depression, anxiety, and stress scores in DASS-21 were 2.89±3.31, 3.94±3.67, and 4.82±4.00, respectively. Compared to the CPAP compliance group, the CPAP noncompliance group had higher daily activity scores in SAQLI (2.98±1.25 vs. 2.45±1.33, P=0.015). Conclusions. The quality of life for Thai OSA patients during the COVID-19 era was moderate degree. Poor CPAP compliance was significantly associated with limited daily activity. Enhancing CPAP compliance could improve the quality of life in these patients. This trial is registered with TCTR20211104004.
Perioperative sleep disturbances may impact healing and negatively affect the patient's perception of well-being. Therefore, accurately assessing postoperative sleep characteristics is necessary to treat sleep disturbances. This study is a secondary data analysis of research investigating the association between sleep and cognition in a perioperative setting. This study compares sleep characteristics between the St. Mary's Hospital Sleep Questionnaire and WatchPAT, a portable sleep apnea testing device. The goal of this study is to compare an objective measurement of sleep quality (WatchPAT) with a traditional questionnaire. One hundred and one patients who underwent elective, noncardiac surgical procedures wore a WatchPAT and completed the St. Mary's Hospital Sleep Questionnaire for three nights: two preoperative and one postoperative night. In the preoperative period, a Bland-Altman analysis showed an agreement Watch PAT and the St Mary's hospital sleep questionnaire except for sleep fragmentation. A good to fair correlation during the preoperative period was observed with both sleep latency and total sleep time. In the postoperative period, no correlation was observed between the St. Mary's Hospital Sleep Questionnaire data and WatchPAT data. Our study indicates that some potential factors affecting sleep and cognition such as admission type, depression, anesthesia type, and sleep apnea may limit patients' ability to report their sleep characteristics after surgery. Therefore, relying solely on one sleep assessment method is not advisable.
Obstructive sleep apnea (OSA) pathologically stresses the cardiovascular system. Apneic events cause significant oscillatory surges in nocturnal blood pressure (BP). Trajectories of these surges vary widely. This variability challenges the quantification, characterization, and mathematical modeling of BP surge dynamics. We present a method of aggregating trajectories of apnea-induced BP surges using a sample-by-sample averaging of continuously recorded BP. We applied the method to recordings of overnight BP (average total sleep time: 4.77 ± 1.64 h) for 10 OSA patients (mean AHI: 63.5 events/h; range: 18.3-105.4). We studied surges in blood pressure due to obstructive respiratory events separated from other such events by at least 30 s (274 total events). These events increased systolic (SBP) and diastolic (DBP) BP by 19 ± 7.1 mmHg (14.8%) and 11 ± 5.6 mmHg (15.5%), respectively, relative to mean values during wakefulness. Further, aggregated SBP and DBP peaks occurred on average 9 s and 9.5 s after apnea events, respectively. Interestingly, the amplitude of the SBP and DBP peaks varied across sleep stages, with mean peak ranging from 128.8 ± 12.4 to 166.1 ± 15.5 mmHg for SBP and from 63.1 ± 8.2 to 84.2 ± 9.4 mmHg for DBP. The aggregation method provides a high level of granularity in quantifying BP oscillations from OSA events and may be useful in modeling autonomic nervous system responses to OSA-induced stresses.
Aim . This study aimed to determine the association between quality of sleep and metabolic syndrome (MetS) and physical activity level in young adults at the University of Ghana. Method . In a cross-sectional design, 340 university students, aged between 20-30 years were recruited. Quality of sleep was assessed using the Pittsburgh Sleep Quality index (PSQI) and physical activity with the short form of the International Physical Activity Questionnaire (IPAQ-SF). Poor quality of sleep was defined as a global PSQI score>5 and low physical activity level as those not meeting the criteria for vigorous-moderate physical activity. Anthropometric features and blood pressures were measured, and fasting blood samples were collected from the participants to measure plasma levels of glucose, lipid profile, urea, and creatinine. MetS was defined using the Joint Scientific Statement criteria. Results . In our study population of young adults from Ghana, the prevalence of poor quality of sleep as measured by PSQI was 54.1%, and MetS was 12.4%. MetS was associated with poor quality of sleep in females [OR (95 % CI) = 2.11 (1.04–4.25), p = 0.038] and entire study participants [2.18 (1.09–4.37) p = 0.029] in both crude and adjusted models; no association was found in male participants. Low physical activity status was not associated with poor sleep status. Obesity [1.32 (1.02–3.56), p = 0.043], but not overweight [0.99 (0.58–2.34), p = 0.862], was associated with poor quality of sleep. Conclusion . Young adults in a Ghanaian university have a high prevalence of poor quality of sleep and is associated with MetS and obesity. Physical activity status was not associated with poor quality of sleep.
