The role of physical activity on obstructive sleep apnea severity and hypoxic load, and the mismatch between subjective and objective physical activity assessments.

Obesity is the primary risk factor for the development of obstructive sleep apnea, and physical inactivity plays an important role. However, most studies have either only evaluated physical activity subjectively or objectively in obstructive sleep apnea. The objectives of this study were: (i) to assess the relationship between obstructive sleep apnea severity (both apnea-hypopnea index and desaturation parameters) and both objectively and subjectively measured physical activity after adjustment for anthropometry and body composition parameters; and (ii) to assess the relationship between objective and subjective physical activity parameters and whether obstructive sleep apnea severity has a modulatory effect on this relationship. Fifty-four subjects (age 47.7 ± 15.0 years, 46% males) were categorized into groups according to obstructive sleep apnea severity: no obstructive sleep apnea; mild obstructive sleep apnea; and moderate-to-severe obstructive sleep apnea. All subjects were evaluated with subjective and objective physical activity, anthropometric and body composition measurements, and 3-night self-applied polysomnography. A one-way ANOVA was used to evaluate the differences between the three obstructive sleep apnea severity groups and multiple linear regression to predict obstructive sleep apnea severity. Differences in subjectively reported sitting time (p ≤ 0.004) were found between participants with moderate-to-severe obstructive sleep apnea, and those with either mild or no obstructive sleep apnea (p = 0.004). Age, body mass index and neck circumference explained 63.3% of the variance in the apnea-hypopnea index, and age, body mass index and visceral adiposity explained 67.8% of the variance in desaturation parameters. The results showed that the person's physical activity does not affect obstructive sleep apnea severity. A weak correlation was found between objective and subjective physical activity measures, which could be relevant for healthcare staff encouraging patients with obstructive sleep apnea to increase their physical activity.

Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls.

Introduction: Visual sleep scoring has several shortcomings, including inter-scorer inconsistency, which may adversely affect diagnostic decision-making. Although automatic sleep staging in adults has been extensively studied, it is uncertain whether such sophisticated algorithms generalize well to different pediatric age groups due to distinctive EEG characteristics. The preadolescent age group (10-13-year-olds) is relatively understudied, and thus, we aimed to develop an automatic deep learning-based sleep stage classifier specifically targeting this cohort. Methods: A dataset (n = 115) containing polysomnographic recordings of Icelandic preadolescent children with sleep-disordered breathing (SDB) symptoms, and age and sex-matched controls was utilized. We developed a combined convolutional and long short-term memory neural network architecture relying on electroencephalography (F4-M1), electrooculography (E1-M2), and chin electromyography signals. Performance relative to human scoring was further evaluated by analyzing intra- and inter-rater agreements in a subset (n = 10) of data with repeat scoring from two manual scorers. Results: The deep learning-based model achieved an overall cross-validated accuracy of 84.1% (Cohen's kappa κ = 0.78). There was no meaningful performance difference between SDB-symptomatic (n = 53) and control subgroups (n = 52) [83.9% (κ = 0.78) vs. 84.2% (κ = 0.78)]. The inter-rater reliability between manual scorers was 84.6% (κ = 0.78), and the automatic method reached similar agreements with scorers, 83.4% (κ = 0.76) and 82.7% (κ = 0.75). Conclusion: The developed algorithm achieved high classification accuracy and substantial agreements with two manual scorers; the performance metrics compared favorably with typical inter-rater reliability between manual scorers and performance reported in previous studies. These suggest that our algorithm may facilitate less labor-intensive and reliable automatic sleep scoring in preadolescent children.

The Sleep Revolution project: the concept and objectives.

Obstructive sleep apnea is linked to severe health consequences such as hypertension, daytime sleepiness, and cardiovascular disease. Nearly a billion people are estimated to have obstructive sleep apnea with a substantial economic burden. However, the current diagnostic parameter of obstructive sleep apnea, the apnea-hypopnea index, correlates poorly with related comorbidities and symptoms. Obstructive sleep apnea severity is measured by counting respiratory events, while other physiologically relevant consequences are ignored. Furthermore, as the clinical methods for analysing polysomnographic signals are outdated, laborious, and expensive, most patients with obstructive sleep apnea remain undiagnosed. Therefore, more personalised diagnostic approaches are urgently needed. The Sleep Revolution, funded by the European Union's Horizon 2020 Research and Innovation Programme, aims to tackle these shortcomings by developing machine learning tools to better estimate obstructive sleep apnea severity and phenotypes. This allows for improved personalised treatment options, including increased patient participation. Also, implementing these tools will alleviate the costs and increase the availability of sleep studies by decreasing manual scoring labour. Finally, the project aims to design a digital platform that functions as a bridge between researchers, patients, and clinicians, with an electronic sleep diary, objective cognitive tests, and questionnaires in a mobile application. These ambitious goals will be achieved through extensive collaboration between 39 centres, including expertise from sleep medicine, computer science, and industry and by utilising tens of thousands of retrospectively and prospectively collected sleep recordings. With the commitment of the European Sleep Research Society and Assembly of National Sleep Societies, the Sleep Revolution has the unique possibility to create new standardised guidelines for sleep medicine.

In-person vs video hookup instructions: a comparison of home sleep apnea testing quality.

STUDY OBJECTIVES: The high prevalence of obstructive sleep apnea (OSA) in the general population makes diagnosing OSA a high priority. Typically, patients receive in-person instructions to hook up the home sleep apnea test devices. Using recorded video instructions would save health care personnel time and improve access to OSA diagnostics for patients in remote areas. The aim of this study was to compare the quality of home sleep apnea test recordings when using in-person and video hookup instructions in a randomized study. METHODS: A total of 100 patients aged 18 to 70 years with suspected OSA were randomized to receive either in-person or video hookup instructions for the Nox T3 device (Nox Medical, Reykjavik, Iceland). The overall quality of the resulting sleep studies was analyzed by determining the number of technically invalid studies. The recording quality of 4 sensors (pulse oximeter, nasal cannula, thorax and abdominal respiratory inductance plethysmography belts) was assessed by checking for signal artifacts. RESULTS: No significant difference was found between the 2 groups in any quality index. Only 1 (2%) and 2 (3.9%) sleep studies were technically invalid in the in-person and video instructions group, respectively. The average ± standard deviation recording quality of the 4 sensors combined was 94.8% ± 13.6% for the in-person and 96.0% ± 11.0% for the video instructions group. CONCLUSIONS: This study found no difference in home sleep apnea test recording quality between the 2 groups. Video hookup instructions are therefore viable and an important step toward a telemedicine-based way of diagnosing OSA. CITATION: Horne AF, Olafsdottir KA, Arnardottir ES. In-person vs video hookup instructions: a comparison of home sleep apnea testing quality. J Clin Sleep Med. 2022;18(8):2069-2074.

Obstructive sleep apnoea in the general population: highly prevalent but minimal symptoms.

The aim was to assess the prevalence of obstructive sleep apnoea (OSA) as defined by an apnoea-hypopnea index (AHI) ≥15 in the middle-aged general population, and the interrelationship between OSA, sleep-related symptoms, sleepiness and vigilance.A general population sample of 40-65-year-old Icelanders was invited to participate in a study protocol that included a type 3 sleep study, questionnaire and a psychomotor vigilance test (PVT).Among the 415 subjects included in the study, 56.9% had no OSA (AHI <5), 24.1% had mild OSA (AHI 5-14.9), 12.5% had moderate OSA (AHI 15-29.9), 2.9% had severe OSA (AHI ≥30) and 3.6% were already diagnosed and receiving OSA treatment. However, no significant relationship was found between AHI and subjective sleepiness or clinical symptoms. A relationship with objective vigilance assessed by PVT was only found for those with AHI ≥30. Subjects already on OSA treatment and those accepting OSA treatment after participating in the study were more symptomatic and sleepier than others with similar OSA severity, as assessed by the AHI.In a middle-aged general population, approximately one in five subjects had moderate-to-severe OSA, but the majority of them were neither symptomatic nor sleepy and did not have impaired vigilance.