This study aims to examine the relationship between stress and sleep of undergraduate students during the course of the COVID-19 pandemic. The possibly correlational relationship between these two parameters was determined with respect to how this population was faring during recent times, two years into the pandemic. The study also served to examine how this relationship changed over time based on three time frames: before the pandemic, during the advent of the lockdown (March 2020-May 2020), and during recent times. A survey was developed to collect data from the undergraduate participants for correlation and regression analysis to determine the relationship between stress and sleep quality as well as how the relationship has changed over time. In addition to questions pertaining to the subjective stress levels and subjective sleep quality of the respondents, questions adapted from the Pittsburgh Sleep Quality Index (PSQI) were used to serve as an objective measure of sleep quality. The survey was created through the Qualtrics online survey software and distributed through social media such as Reddit and Discord. The participant inclusion criteria included: 1) be a current undergraduate student at a university or community college, and 2) be at least the age of 18. One-hundred-four full responses were collected out of a total of 138 that began the survey. The results indicated a high prevalence of sleep problems among the participants, with the majority of them (58.65%) having poor sleep quality within the last month. A moderate negative correlation between stress levels and sleep quality was found as well. This was based upon a moderate positive correlation between subjective stress level, and PSQI score the month prior to participants taking the survey. However, stress was not a good predictor in determining sleep quality alone. Further, the results were inconclusive on how the relationship between stress and sleep quality changed from before to during the duration of the lockdown. Overall, this study indicated the need to further research into how stress and other factors affect the sleep quality of not just undergraduate students but other populations susceptible to poor sleep.

Currently, sleep staging is accomplished is by clinical polysomnography (PSG). By extracting features from different combinations of electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) signals, this study uses neural network to perform sleep staging. A whole night and complete sleep stage contains wake stage, rapid eye movement (REM) stage, stage 1, stage 2, and slow wave sleep (SWS) stage. This project focuses on the classification of wake stage and light-sleep (stage 1 and 2). These three stages are classified by a two-step process. At first, wake stage and light-sleep are divided into two parts. Second, light sleep is divided into stage 1 and stage 2. For a fixed number of channels, this work identifies the best combination of signal channels. In addition, by simultaneously considering the Neighboring epochs Rule classifier, this work also introduces an empirical rule to improve the classification accuracy. Among the tested databases which contain two Medicine center and sixteen sets of different signal channels, the best results are obtained from the group of patients with the low average RDI value. They include the group that has a mean 15% SWS and the group that uses CPAP. As a whole, the combinative features of four channels are better results of classification. For our best results, the sensitivity and PPV of wake and stage 2 varies from 85%~88%, and those of stage 1 are respective 44.84% and 53.61%. And the total classification of sleep staging is 84.59%. Apparently, the research has satisfactory results on sleep staging. Keywords: Sleep Medicine, Sleep stage, Neural Networks

50 Patienten wurden mit einem herausnehmbaren unterkiefervorverlagernden Schienensystem im Zeitraum der Jahre 2000 bis 2002 behandelt. Die schlafmedizinisch-diagnostischen Untersuchungen vor Therapiebeginn erfolgten bei 25 Patienten ambulant und bei den anderen 25 Patienten stationär. Es wurden schlafmedizinischen Parameter zur Diagnosestellung herangezogen. Hälftig bestanden bei diesen Patienten die schlafmedizinische Diagnosen einer Rhonchopathie (reine Schnarcher, ohne internistische Besonderheiten) und einer leicht bis mittelgradigen obstruktiven Schlafapnoe. Bei 23 Patienten mit einer Rhonchopathie wurde das Funktionsprinzip der Unterkiefervorverlagerung durch den Wilcoxon-Test bei der Veränderung der schlafmedizinischen Parameter Entsättigungsindex, Schnarchindex, Anzahl der Entsättigungen und niederste Entsättigung bestätigt. Bei zwei (8%) der 25 Patienten veränderten sich die Parameter nicht positiv, so dass die Schienentherapie abgebrochen wurde. Bei 20 Patienten mit leicht- bis mittelgradiger Schlafapnoe bestätigte der Wilcoxon-Test die Signifikanz der Veränderungen bei den polysomnographischen Werten AHI , REM und Schlafeffizienz. Die Signifkanzschranke wurde bei dem somnologischem Wert Tiefschlaf nicht erreicht. Obgleich bei 13 Patienten die Schienentherapie aus unterschiedlichen Gründen innerhalb der zwei Untersuchungsjahre abgebrochen werden musste, wurde die Behandlung bei den 37 verbliebenen Patienten (74%) als erfolgreich und zufriedenstellend eingeschätzt.

Since studies have revealed sleeping quality is highly related to our health conditions, sleep-medicine has attracted more and more attention in recent years. Sleep staging is one of the most important elements of sleep-medicine. Traditionally, it¡¦s done by observing the information form of EEG, EOG and EMG signals. But this is almost not possible to achieve at home. Automatic detection of REM sleep is the main goal of this study. Via comparing the classification performances of different combinations of EEG, EOG and EMG signals, this study also tries to simplify the number of signal channels. By using features extracted from EEG, EOG and EMG signals, the back-propagation neural networks are used to distinguish REM and NREM sleep. By refining the outputs of the neural networks, this study extensively test the efficacy of the proposed approach by using databases from two different sleep centers. This work also investigates the influences of the number of signal channels, REM sleep ratio, AHI, and age on classification results. Data acquired from the sleep centers of China Medical University Hospital (CMUH) and Sheng-Mei Hospital are arranged in ten different groups. For our largest datasets, which consists of 1318 subjects from CMUH, the results show that the proposed method achieves 95.5% epoch-to-epoch agreement with Cohen's Kappa 0.833, sensitivity 85.9% and specificity 97.3%. The generalization accuracy is 94.1% with Cohen's Kappa 0.782, sensitivity 78.5% and specificity 97.3%.

A diagnosis of Autism Spectrum Disorder (ASD) can impact the entire family. Parents of children with ASD reportedly have greater stress levels, family conflict, financial concerns, and poor health habits than parents of neurotypical (NT) children. While many parent-focused interventions have been developed, these interventions focus on parent training and child behavior outcomes rather than the health and well-being of the parents. Therefore, this study aimed to examine the effects of a 15-week family judo program on physical and psychosocial health in parents of children with ASD. A total of 18 parents of children with ASD participated in a weekly judo program, with each session lasting 45 minutes. Parents completed online surveys that asked about sociodemographic information and parental stress and wore wrist accelerometers that measured their physical activity and sleep quality. Paired samples t-tests were used to examine parental stress, physical activity, and sleep quality differences in parents of children with ASD pre- and post-judo program. Both a decrease in parental stress (47.77 vs. 41.61, p High-stress levels can also negatively impact physical health and have been linked to poor sleep and low physical activity levels. This is particularly concerning as research suggests that parents of children with neurodevelopmental disorders already report worse sleep quality and lower levels of physical activity than parents of NT children. Furthermore, the treatment of sleep disorders in children with ASD has been related to reductions in parental stress. Additionally, child engagement in health behaviors has been correlated with parent behaviors. The majority of these studies, however, have utilized self-report measures of sleep and physical activity, which are prone to bias. Furthermore, while there are several components that make up sleep quality, the majority of studies have primarily focused on sleep duration rather than other aspects of sleep, such as sleep efficiency. Parental stress can also spill over into the parent-child relationship, resulting in diminished communication quality and decreased optimism about the future. Furthermore, evidence suggests a bidirectional relationship exists between parent/caregiver stress and child ASD symptoms. In other words, increased stress levels of the parent/caregiver may exacerbate the child's ASD symptoms, further worsening parent/caregiver stress. The immediate need for interventions to ease parents' stress and improve the quality of life for both parents and children is apparent. Physical activity has been deemed an intervention to reduce stress and is associated with improved well-being and mental health for both neurotypical and ASD populations. Several studies have demonstrated the benefits of physical activity interventions incorporating mind-body interaction, such as yoga or martial arts, for children with ASD. Martial arts training, such as judo, benefits participants and their families by encompassing mindfulness, balance, strength, and coordination, emphasizing social interaction. Despite the increasing prevalence of ASD, the reported stress on families of children with ASD, and the negative effects of parent stress on both parents and their children with ASD, there are no studies that have examined the benefits of a family-based, mind-body physical activity program on stress and health behaviors in parents of children with ASD. The current study will address this gap by examining the effects of a family judo intervention on parent stress and sleep patterns of parents of children with ASD. There is a significant decrease in parent-reported stress post-judo program. Parents also have increased levels of physical activity. Finally, we see parents reporting decreased stress and improved self-confidence with their children during the semi-structured interviews at the end of the program. These findings may be used to explore further whether a family judo program may lead to better parent and family outcomes, such as increased parental efficacy, improved parent-child bonding, and strengthened family resiliency.

Research suggests that elevated levels of screen time in the evening can negatively impact sleep outcomes (sleep duration, quality, and latency). Yet, there is a lack of evidence exploring if this relationship exists in college students. The purpose of this study is to investigate the relationship between evening screen time use and objectively measured sleep outcomes in US college students. College students (N = 29) junior or senior standing in a Health Sciences major were recruited to participate in this cross-sectional study. Participants wore accelerometers on their wrist for 24hr/day for seven days to capture sleep duration, efficiency, awakenings, and timing. Participants completed a survey regarding their screen time habits and evening usage. Twenty-nine college students (86% female, averaged 21.5 years old, 38% White, 35% Overweight/Obese) provided valid accelerometer data. There were 180 total valid night sleep observations. Average nighttime sleep duration was 433.8 minutes with an average of 22.3 awakenings. The mean sleep efficiency was 87.3%. The average bedtime was 2:26 am, and the average wake time was 8:01 am. The average daily screen time of participants was 419.1 minutes, and after 10:00 pm was 112.2 minutes. Cell phone use before bed was negatively correlated with self-reported sleep duration (r = -0.42; p-value = 0.02), and positively correlated with stimulating screen time content (r = 0.43; p-value = 0.02). Those who napped had a lower daily screen time duration compared to those who did not nap (Mean difference = 91.7 minutes; p-value = 0.05) and had a higher sleep efficiency of 95.8%. Preliminary evidence suggests that a relationship between both evening screen time duration in addition to content, and objectively measured sleep outcomes may exist for US college students. This study provides reliable, objective data of college student sleep that may inform future public health interventions in this population.

The purpose of this study is to examine the relationship between dental and mental health. To dive deeper into seeing this connection, patients with sleep apnea will be asked to share their experiences with the treatments for sleep apnea and how it had affected their mental or dental health.

Interprofessional education has the potential to prepare health career students to be practice-ready and enter the workforce with a collaborative mindset. Respiratory care educators must adequately prepare students to work in this capacity. This emphasis on a team approach to patient-centered care has the ability to impact and improve health outcomes. Throughout the last decade, sleep medicine has experienced fluctuations. Sustainability of sleep labs who only perform diagnostic testing may prove challenging. The role of interprofessional practice in sleep medicine would be to overcome traditional roles (silos) so that multiple skilled practitioners could help identify and treat complex patient conditions. A review of the literature demonstrated how various providers can serve as active members of interprofessional health care teams. The opportunity to expand services and partner with other providers to detect, educate, and treat sleep disordered breathing could help laboratories endure and even thrive in the current health care system.

50 Patienten wurden mit einem herausnehmbaren unterkiefervorverlagernden Schienensystem im Zeitraum der Jahre 2000 bis 2002 behandelt. Die schlafmedizinisch-diagnostischen Untersuchungen vor Therapiebeginn erfolgten bei 25 Patienten ambulant und bei den anderen 25 Patienten stationär. Es wurden schlafmedizinischen Parameter zur Diagnosestellung herangezogen. Hälftig bestanden bei diesen Patienten die schlafmedizinische Diagnosen einer Rhonchopathie (reine Schnarcher, ohne internistische Besonderheiten) und einer leicht bis mittelgradigen obstruktiven Schlafapnoe. Bei 23 Patienten mit einer Rhonchopathie wurde das Funktionsprinzip der Unterkiefervorverlagerung durch den Wilcoxon-Test bei der Veränderung der schlafmedizinischen Parameter Entsättigungsindex, Schnarchindex, Anzahl der Entsättigungen und niederste Entsättigung bestätigt. Bei zwei (8%) der 25 Patienten veränderten sich die Parameter nicht positiv, so dass die Schienentherapie abgebrochen wurde. Bei 20 Patienten mit leicht- bis mittelgradiger Schlafapnoe bestätigte der Wilcoxon-Test die Signifikanz der Veränderungen bei den polysomnographischen Werten AHI , REM und Schlafeffizienz. Die Signifkanzschranke wurde bei dem somnologischem Wert Tiefschlaf nicht erreicht. Obgleich bei 13 Patienten die Schienentherapie aus unterschiedlichen Gründen innerhalb der zwei Untersuchungsjahre abgebrochen werden musste, wurde die Behandlung bei den 37 verbliebenen Patienten (74%) als erfolgreich und zufriedenstellend eingeschätzt.

Nowadays, sleep disorders e.g. sleep apnea —the cessation of airflow at the nose and mouth lasting at least 10 second— are a broadly problem around the world. Direct and indirect costs associated to sleep problems are outsize and the quality of patient life is deteriorated because of it. In addition, Sleep is a fundamental part of everyday life, the lack of it or the poor quality of sleep may lead into the development of important diseases. Sleep studies are usually carried out by specialists by means of polysomnography. Polysomnography is a type of sleep study which is consisting of EEG, EOG, EMG, ECG, respiratory signals and/or many other biosignals which together can be used to determine the state of patient’s sleep and any other issue. Nowadays, visual inspection of these signals forms the “gold standard” in sleep clinics. The cost of monitoring a person overnight, the scarcity of beds available and the uncertainty of whether the results are representative of a normal nights’ sleep means that a move to home diagnostics is likely to be advantageous. Therefore, a necessity for home recorders systems capable of perform this kind of analysis has come out. A state machine based automatic scorer is developed and evaluated in Matlab by using 12 recordings of apnoeic patients from sleep heart health study (SHHS) database. By the analysis of EEG, EOG, EMG, Oxygen saturation (Sao2) and respiratory movements signals, the implemented algorithm is trained and evaluated to detect the five stages of subject’s sleep (Wake, N1, N2, N3, or REM) as well as apnoeic episodes according to guidelines from American Academy of Sleep Medicine (AASM). In the final evaluation of algorithms, the automatic scorer achieved 74±5.27% accuracy for all five stages and Cohen’s kappa of 0.5 for the overall set of 12 patients, being the accuracy better for healthier subjects and reaching in this case 78±4.05%. The analysis of the sleep apnea concluded with a sensitivity of 47.08%, a specificity of 83.38%, and an accuracy of 78.1%. Differences in the performance among patients according to their apnea/hypopnea index were significant.   Key Words: Polysomnography, AASM, Sleep apnea/hypopnea.