Effect of particulate matter on continuous positive airway pressure adherence in obstructive sleep apnea patients.

Background: Sleep quality could be affected by air pollution, especially for particulate matter with a diameter of less than 10 microns (PM10) and particulate matter with a diameter of less than 2.5 microns (PM2.5). However, no direct study demonstrates the relationship and impact of air pollution especially PM10 and PM2.5 on continuous positive airway pressure (CPAP) adherence. Thus, we aimed to study the correlation between PM10, PM2.5, and low CPAP adherence in subjects with obstructive sleep apnea (OSA). Methods: We conducted a time-series study from August 2016 to May 2022 in Chiang Mai, Thailand. The data from 2,686 visits of CPAP compliance records from 839 OSA patients' electronic medical records at the Sleep Disorders Center, Center of Medical Excellence, Chiang Mai University, Chiang Mai, Thailand were reviewed. The level of adherence was determined utilizing the provided data. Low CPAP adherence was defined as using CPAP for less than 240 minutes per night or less than 70% of nights (i.e., <5 nights/week) in the previous month. The correlation between the monthly average of PM10 and PM2.5 and the rate of low CPAP adherence was analyzed using generalized linear mixed model (GLMM) after adjustment for confounding factors. Results: There was no effect of an increase in PM10 and PM2.5 on low CPAP adherence [adjusted risk ratio (RR) =0.97; 95% confidence interval (CI): 0.87, 1.09; P value =0.624 and adjusted RR =0.93; 95% CI: 0.81, 1.08; P value =0.350 for PM10 and PM2.5, respectively]. Conclusions: There was no effect of particulate matter on CPAP adherence in OSA patients.

Factors influencing continuous positive airway pressure adherence in elderly with obstructive sleep apnea.

Background: Continuous positive airway pressure (CPAP) is the most effective treatment for symptomatic obstructive sleep apnea (OSA). The identification of actual predictors of CPAP adherence in real-world practice is essential since it enhances more individualized management for the patient. CPAP acceptance and adherence in elderly patients with OSA have the same challenges but the conclusion remains unclear. Therefore, our aim was to explore the factors influencing the adherence of CPAP in elderly OSA patients. Methods: The retrospective observational study was conducted from OSA patients' computerized medical records at Sleep Disorders Center, Center of Medical Excellence, Chiang Mai University Hospital, Chiang Mai, Thailand between 2018 and 2020. Multivariable risk regression analyses were performed to evaluate the independent factors associated with CPAP non-acceptance and CPAP non-adherence. Results: Of the 1,070 patients who underwent overnight polysomnography (PSG), 336 (31.4%) were elderly. Of 759 patients who accepted CPAP treatment, 221 (29.1%) were elderly, including 27 (12.2%) non-adherences, 139 (62.9%) adherences and 55 (24.8%) loss follow-up. Elderly patients with adverse attitudes toward CPAP use affected adherence to treatment [adjusted risk ratio (RR) =4.59, 95% CI: 1.79, 11.78, P=0.002]. Female was also associated with low CPAP adherence with adjusted RR =3.10 (95% CI: 1.07, 9.01), P=0.037. Conclusions: In our largest cohort to date, elderly OSA patients treated with CPAP over long-term follow-ups demonstrated that adherence rates were associated with personal life issues and adverse attitudes towards treatment as well as health problems. Female was also associated with low CPAP adherence. Therefore, in the elderly with OSA, the indication and treatment of CPAP should be customized individually, and if prescribed, regular monitoring to address noncompliance and tolerance should be considered.

Automatic Driver Drowsiness Detection Using Artificial Neural Network Based on Visual Facial Descriptors: Pilot Study.

Purpose: Driving while drowsy is a major cause of traffic accidents globally. Recent technologies for detection and alarm within automobiles for this condition are limited by their reliability, practicality, cost, and lack of clinical validation. In this study, we developed an early drowsiness detection algorithm and device based on the "gold standard brain biophysiological signal" and facial expression digital data. Methods: The data were obtained from 10 participants. Artificial neural networks (ANN) were adopted as the model. Composite features of facial descriptors (ie, eye aspect ratio (EAR), mouth aspect ratio (MAR), face length (FL), and face width balance (FWB)) extracted from two-second video frames were investigated. Results: The ANN combined with the EAR and MAR features had the most sensitivity (70.12%) while the ANN combined with the EAR, MAR, and FL features had the most accuracy and specificity (60.76% and 58.71%, respectively). In addition, by applying the discrete Fourier transform (DFT) to the composite features, the ANN combined with the EAR and MAR features again had the highest sensitivity (72.25%), while the ANN combined with the EAR, MAR, and FL features had the highest accuracy and specificity (60.40% and 54.10%, respectively). Conclusion: The ANN with DFT combined with the EAR, MAR, and FL offered the best performance. Our direct driver sleepiness detection system developed from the integration of biophysiological information and internal validation provides a valuable algorithm, specifically toward alertness level.