Sleep Monitoring Research Articles

Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity

Sociodemographic and lifestyle factors (sleep, physical activity, and sedentary behavior) may predict obesity risk in early adolescence; a critical period during the life course. Analyzing data from 2971 participants (M = 11.94, SD = 0.64 years) wearing Fitbit Charge HR 2 devices in the Adolescent Brain Cognitive Development (ABCD) Study, glass box machine learning models identified obesity predictors from Fitbit-derived measures of sleep, cardiovascular fitness, and sociodemographic status. Key predictors of obesity include identifying as Non-White race, low household income, later bedtime, short sleep duration, variable sleep timing, low daily step counts, and high heart rates (AUCMean = 0.726). Findings highlight the importance of inadequate sleep, physical inactivity, and socioeconomic disparities, for obesity risk. Results also show the clinical applicability of wearables for continuous monitoring of sleep and cardiovascular fitness in adolescents. Identifying the tipping points in the predictors of obesity risk can inform interventions and treatment strategies to reduce obesity rates in adolescents.

Analysis of the Current Application Status for Clinical and Home Monitoring of Obstructive Sleep Apnea-Hypopnea Syndrome

The study provides an overview of the development status of sleep disorder monitoring devices. Currently, polysomnography (PSG) is the gold standard for diagnosing sleep disorders, necessitating multiple leads and requiring overnight monitoring in a sleep laboratory, which can be cumbersome for patients. Nevertheless, the performance of PSG has been enhanced through research on sleep disorder monitoring and sleep staging optimization. An alternative device is the home sleep apnea testing (HSAT), which enables patients to monitor their sleep at home. However, HSAT does not attain the same level of accuracy in sleep staging as PSG, rendering it inappropriate for screening individuals with asymptomatic or mild obstructive sleep apnea-hypopnea syndrome (OSAHS). The study suggests that establishing a Chinese sleep staging database and developing home sleep disorder monitoring devices that can serve as alternatives to PSG will represent a future development direction.

A smart detection method for sleep posture based on a flexible sleep monitoring belt and vital sign signals

People spend approximately one-third of their lives in sleep, but more and more people are suffering from sleep disorders. Sleep posture is closely related to sleep quality, so related detection is very significant. In our previous work, a smart flexible sleep monitoring belt with MEMS triaxial accelerometer and pressure sensor has been developed to detect the vital signs, snore events and sleep stages. However, the method for sleep posture detection has not been studied. Therefore, to achieve high performance, low cost and comfortable experience, this paper proposes a smart detection method for sleep posture based on a flexible sleep monitoring belt and vital sign signals measured by a MEMS Inertial Measurement Unit (IMU). Statistical analysis and wavelet packet transform are applied for the feature extraction of the vital sign signals. Then the algorithm of recursive feature elimination with cross-validation is introduced to further extract the key features. Besides, machine learning models with 10-fold cross validation process, such as decision tree, random forest, support vector machine, extreme gradient boosting and adaptive boosting, were adopted to recognize the sleep posture. 15 subjects were recruited to participate the experiment. Experimental results demonstrate that the detection accuracy of the random forest algorithm is the highest among the five machine learning models, which reaches 96.02 %. Therefore, the proposed sleep posture detection method based on the flexible sleep monitoring belt is feasible and effective.

Analysis of the factors influencing of sleep quality in intensive care unit awake patients based on a structural equation model: A cross-sectional study.

The objective of this study was to construct and validate a structural equation model (SEM) to identify factors associated with sleep quality in awake patients in the intensive care unit (ICU) and to assist in the development of clinical intervention strategies. In this cross-sectional study, 200 awake patients who were cared for in the ICU of a tertiary hospital in China were surveyed via several self-report questionnaires and wearable actigraphy sleep monitoring devices. Based on the collected data, structural equation modelling analysis was performed using SPSS and AMOS statistical analysis software. The study is reported using the STROBE checklist. The fit indices of the SEM were acceptable: χ2/df = 1.676 (p < .001) and RMSEA = .058 (p < 0.080). Anxiety/depression had a direct negative effect on the sleep quality of awake patients cared for in the ICU (β = -.440, p < .001). In addition, disease-freeness progress had an indirect negative effect on the sleep quality of awake patients cared for in the ICU (β = -.142, p < .001). Analgesics had an indirect negative effect on the sleep quality of awake patients cared for in the ICU through pain and sedatives (β = -.082, p < .001). Sedation had a direct positive effect on the sleep quality of conscious patients cared for in the ICU (β = .493; p < .001). The results of the SEM showed that the sleep quality of awake patients cared for in the ICU is mainly affected by psychological and disease-related factors, especially anxiety, depression and pain, so we can improve the sleep quality of patients through psychological intervention and drug intervention.

A telemonitoring intervention design for patients with poorly controlled type 2 diabetes: protocol for a feasibility study.

Maintaining optimal glycemic control in type 2 diabetes (T2D) is difficult. Telemedicine has the potential to support people with poorly regulated T2D in the achievement of glycemic control, especially if the telemedicine solution includes a telemonitoring component. However, the ideal telemonitoring design for people with T2D remains unclear. Therefore, the aim of this feasibility study is to evaluate the feasibility of two telemonitoring designs for people with non-insulin-dependent T2D with a goal of identifying the optimal telemonitoring intervention for a planned future large-scale randomized controlled trial. This 3-month randomized feasibility study will be conducted in four municipalities in North Denmark starting in January 2024. There will be 15 participants from each municipality. Two different telemonitoring intervention designs will be tested. One intervention will include self-monitoring of blood glucose (SMBG) combined with sleep and mental health monitoring. The second intervention will include an identical setup but with the addition of blood pressure and activity monitoring. Two municipalities will be allocated to one intervention design, whereas the other two municipalities will be allocated to the second intervention design. Qualitative interviews with participants and clinicians will be conducted to gain insight into their experiences with and acceptance of the intervention designs and trial procedures (e.g., blood sampling and questionnaires). In addition, sources of differences in direct intervention costs between the two alternative interventions will be investigated. Telemonitoring has the potential to support people with diabetes in achieving glycemic control, but the existing evidence is inconsistent, and thus, the optimal design of interventions remains unclear. The results of this feasibility study are expected to produce relevant information about telemonitoring designs for people with T2D and help guide the design of future studies. A well-tested telemonitoring design is essential to ensure the quality of telemedicine initiatives, with goals of user acceptance and improved patient outcomes. ClinicalTrials.gov, ID: NCT06134934 . Registered November 1, 2023. The feasibility trial has been approved (N-20230026) by the North Denmark Region Committee on Health Research Ethics (June 5, 2023).

Effect of antihypertensive agents on sleep apnea and ambulatory blood pressure in patients with hypertension: A randomized controlled trial

BackgroundObstructive sleep apnea (OSA) and hypertension are common conditions that may be linked through sympathetic activation and water retention. We hypothesized that diuretics, which reduce the body water content, may be more effective than amlodipine, a blood pressure (BP)-lowering agent implicated with edema, in controlling OSA in patients with hypertension. We also aimed to compare the effects of these treatments on ambulatory blood pressure monitoring (ABPM). MethodsIn a randomized, double-blind clinical trial, we compared the effects of chlorthalidone/amiloride 25/5 mg with amlodipine 10 mg on OSA measured by portable sleep monitor and BP measured by ABPM. The study included participants older than 40 who had moderate OSA (10–40 apneas/hour of sleep) and BP within the systolic range of 140–159 mmHg or diastolic range of 90–99 mmHg. ResultsThe individuals in the experimental groups were comparable in age, gender, and other relevant characteristics. Neither the combination of diuretics nor amlodipine alone reduced the AHI after 8 weeks of treatment (AHI 26.3 with diuretics and 25.0 with amlodipine. P = 0.713). Both treatments significantly lowered office, 24-h, and nighttime ABP, but the two groups had no significant difference. ConclusionChlorthalidone associated with amiloride and amlodipine are ineffective in decreasing the frequency of sleep apnea episodes in patients with moderate OSA and hypertension. Both treatments have comparable effects in lowering both office and ambulatory blood pressure. The notion that treatments could offer benefits for both OSA and hypertension remains to be demonstrated. Trial registration clinicaltrials. gov identifierNCT01896661.

EarSleep: In-ear Acoustic-based Physical and Physiological Activity Recognition for Sleep Stage Detection

Since sleep plays an important role in people's daily lives, sleep monitoring has attracted the attention of many researchers. Physical and physiological activities occurring in sleep exhibit unique patterns in different sleep stages. It indicates that recognizing a wide range of sleep activities (events) can provide more fine-grained information for sleep stage detection. However, most of the prior works are designed to capture limited sleep events and coarse-grained information, which cannot meet the needs of fine-grained sleep monitoring. In our work, we leverage ubiquitous in-ear microphones on sleep earbuds to design a sleep monitoring system, named EarSleep1, which interprets in-ear body sounds induced by various representative sleep events into sleep stages. Based on differences among physical occurrence mechanisms of sleep activities, EarSleep extracts unique acoustic response patterns from in-ear body sounds to recognize a wide range of sleep events, including body movements, sound activities, heartbeat, and respiration. With the help of sleep medicine knowledge, interpretable acoustic features are derived from these representative sleep activities. EarSleep leverages a carefully designed deep learning model to establish the complex correlation between acoustic features and sleep stages. We conduct extensive experiments with 48 nights of 18 participants over three months to validate the performance of our system. The experimental results show that our system can accurately detect a rich set of sleep activities. Furthermore, in terms of sleep stage detection, EarSleep outperforms state-of-the-art solutions by 7.12% and 9.32% in average precision and average recall, respectively.

Executive function performance in children and adolescent patients with narcolepsy type 1

ObjectiveThe executive function profile in patients with narcolepsy type 1 (NT1) has been mentioned; however, limited research exists on children and adolescent patients with NT1.This study aims to assess executive function in children and adolescent patients with NT1 in China, examine potential influencing factors and evaluate the short-term treatment effect on executive function. Methods53 NT1 patients (36 males, age 12.2 ± 3.4 years) and 37 healthy controls (23 males, age 12.2 ± 2.5 years) underwent self-reported measures assessing subjective sleepiness, depression, anxiety and sleep quality. A comprehensive neuropsychological test was administered to assess executive function domains, including processing speed, inhibitory control, cognitive flexibility and working memory. These assessments were repeated in NT1 patients after three-day regular drug treatment. ResultsNT1 patients exhibited higher levels of excessive daytime sleepiness, depression, anxiety, and poor sleep quality compared to healthy controls. Patients showed impaired processing speed, inhibitory control and cognitive flexibility (p < 0.05), whereas working memory was unaffected (p > 0.05). Regression analysis revealed that parameters from sleep monitoring, such as sleep efficiency and sleep latency, were correlated with executive function performance after controlling for age, gender, and education years. The short-term treatment led to improvements in inhibitory control, cognitive flexibility, and working memory. ConclusionThe findings showed that executive function was impaired among children and adolescent patients with NT1, which was associated with objective sleep parameters. Furthermore, this study emphasizes the necessity of neuropsychological assessments and early interventions among children and adolescent NT1 patients.

The effect of facilitated tucking and white noise on stress and sleep of newborns receiving nasal continuous positive airway pressure

ObjectiveTo investigate the effects of facilitated tucking and white noise on stress and sleep in neonates receiving nasal continuous positive airway pressure (CPAP). MethodThis study was conducted as a randomised controlled experimental study of neonates receiving nasal CPAP in neonatal intensive care units. The study sample consisted of 108 newborns (facilitated tucking (n = 36), white noise (n = 36) and control (n = 36)) receiving nasal CPAP support in the NICU. The neonates' sleep parameters were recorded by actigraphy for 24 h. Data were collected using the Neonatal Descriptive Information Form, the Sleep Tracking Form and the Neonatal Stress Scale. Percentage, mean, chi-squared and one-way ANOVA were used for data analysis. ResultsIt was found that 50.9% of the newborns were female, their mean gestational age was 33.54 ± 3.38, their mean height was 43.56 ± 5.12, and their mean weight was 2139.23 ± 827.82. The total sleep time of the neonates in the facilitated tucking and white noise group increased by 3 h, their sleep efficiency increased by 20% and their mean stress scores decreased (p < 0.05). ConclusionFacilitated tucking and white noise each showed a similar improvement in sleep duration and sleep efficiency and a reduction in stress scores in neonates receiving nasal CPAP. Close monitoring of sleep in this population and supportive care practices are recommended. Practice implicationsThe findings of this study may help to reduce sleep problems and stress levels in the clinical care of neonates in the NICU through developmental nursing practices.

Routine, Routine, Routine: Sleep Regularity and its Association with Sleep Metrics in Professional Rugby Union Athletes

BackgroundMaintaining a consistent sleep and wake time is often reported as a key component of circadian rhythmicity and quality sleep. However, the impact of sleep onset and offset time variability on overall sleep outcomes are underreported in elite athlete populations. This study investigated the relationship between sleep onset and offset time variability using the sleep regularity index (SRI) and measures of sleep and well-being in professional rugby union athletes. Twenty-three professional male rugby union athletes (mean ± SD, age: 23 ± 3 y) underwent sleep monitoring via wrist actigraphy for three weeks during a pre-season phase of training and completed a daily wellness questionnaire. Median SRI was calculated and used to stratify the trainees into two quantile groups: >76.4 SRI (Regular, n = 11) and < 76.4 SRI (Irregular, n = 12).ResultsThe regular sleep group showed significantly longer total sleep duration (p = 0.02, d = 0.97) compared to the irregular group (7:42 ± 0:29 vs. 7:18 ± 0:20 h: min per night, respectively). Furthermore, while not statistically significant, the regular sleep group showed greater sleep efficiency and less wake episodes compared to irregular sleepers, as demonstrated by moderate effect sizes (d = 0.71 and 0.69, respectively).ConclusionsThe results from this study indicate that minimizing variability in sleep onset and offset time is beneficial for increasing sleep duration and may improve sleep efficiency during pre-season training in elite male rugby union athletes. This study provides evidence for the importance of including sleep-wake routines as a key component of sleep education interventions.

A study on pulmonary function in children with obstructive sleep apnea hypopnea syndrome.

To investigate the pulmonary function of children with obstructive sleep apnea syndrome. A total of 328 children aged 3 to 12 years old who were evaluated for a sleep disorder from January 2022 to June 2023 were selected as the observation group, classified into mild, moderate, and severe categories based on the apnea hypopnea index. The number of children with mild, moderate, and severe obstructive sleep apnea is 228, 62, and 28 respectively. Additionally, 126 healthy individuals aged 3 to 13 years old undergoing health examinations during the same period were selected as the control group. All subjects underwent sleep respiratory monitoring, pulmonary function tests, and impulse oscillometry. Comparative analysis was performed on pulmonary function indices (forced vital capacity, maximum ventilation, inspiratory capacity, total lung capacity, and inspiratory reserve volume), and respiratory impedance indices (resonant frequency, total respiratory impedance, viscous resistance at 5 Hz, 20 Hz, and 35 Hz). Pulmonary function indices were also compared among patients in the observation group with mild, moderate, and severe conditions. In the observation group, the FVC pre% of patients decreased by 10.5 ± 5.99 compared to the control group. The MVV of the control group decreased by 28.10 ± 2.22 compared to patients in the observation group. The IC of the control group decreased by 0.68 ± 0.44 compared to patients in the observation group. The TLC of the control group decreased by 1.354 ± 0.51 compared to patients in the observation group. The ERV of the control group decreased by 0.53 ± 0.30 compared to patients in the observation group. Additionally, the Fres, Zrs, R5, R20, and R35 of the observation group were higher than those of the control group by 10.73 ± 0.18, 1.78 ± 0.24, 0.11 ± 0.17, 0.86 ± 0.13, and 0.02 ± 0.21, respectively. In sum, the pulmonary function indices of the observation group were significantly lower than those of the control group, while the respiratory impedance indices were higher (P < 0.05). Within the observation group, the pulmonary function indices of severe patients were lower than those of moderate and mild patients, and moderate patients had lower pulmonary function indices than mild patients (P < 0.05). The pulmonary function of children with obstructive sleep apnea syndrome is impaired and varies in severity. There are significant differences in pulmonary function, underscoring the importance of monitoring pulmonary function in these children for clinical assessment and treatment prognosis.

SleepBoost: a multi-level tree-based ensemble model for automatic sleep stage classification.

Neurodegenerative diseases often exhibit a strong link with sleep disruption, highlighting the importance of effective sleep stage monitoring. In this light, automatic sleep stage classification (ASSC) plays a pivotal role, now more streamlined than ever due to the advancements in deep learning (DL). However, the opaque nature of DL models can be a barrier in their clinical adoption, due to trust concerns among medical practitioners. To bridge this gap, we introduce SleepBoost, a transparent multi-level tree-based ensemble model specifically designed for ASSC. Our approach includes a crafted feature engineering block (FEB) that extracts 41 time and frequency domain features, out of which 23 are selected based on their high mutual information score (> 0.23). Uniquely, SleepBoost integrates three fundamental linear models into a cohesive multi-level tree structure, further enhanced by a novel reward-based adaptive weight allocation mechanism. Tested on the Sleep-EDF-20 dataset, SleepBoost demonstrates superior performance with an accuracy of 86.3%, F1-score of 80.9%, and Cohen kappa score of 0.807, outperforming leading DL models in ASSC. An ablation study underscores the critical role of our selective feature extraction in enhancing model accuracy and interpretability, crucial for clinical settings. This innovative approach not only offers a more transparent alternative to traditional DL models but also extends potential implications for monitoring and understanding sleep patterns in the context of neurodegenerative disorders. The open-source availability of SleepBoost's implementation at https://github.com/akibzaman/SleepBoost can further facilitate its accessibility and potential for widespread clinical adoption.

Assessment of Stress and Well-Being of Japanese Employees Using Wearable Devices for Sleep Monitoring Combined With Ecological Momentary Assessment: Pilot Observational Study.

Poor sleep quality can elevate stress levels and diminish overall well-being. Japanese individuals often experience sleep deprivation, and workers have high levels of stress. Nevertheless, research examining the connection between objective sleep assessments and stress levels, as well as overall well-being, among Japanese workers is lacking. This study aims to investigate the correlation between physiological data, including sleep duration and heart rate variability (HRV), objectively measured through wearable devices, and 3 states (sleepiness, mood, and energy) assessed through ecological momentary assessment (EMA) and use of rating scales for stress and well-being. A total of 40 office workers (female, 20/40, 50%; mean age 40.4 years, SD 11.8 years) participated in the study. Participants were asked to wear a wearable wristband device for 8 consecutive weeks. EMA regarding sleepiness, mood, and energy levels was conducted via email messages sent by participants 4 times daily, with each session spaced 3 hours apart. This assessment occurred on 8 designated days within the 8-week timeframe. Participants' stress levels and perception of well-being were assessed using respective self-rating questionnaires. Subsequently, participants were categorized into quartiles based on their stress and well-being scores, and the sleep patterns and HRV indices recorded by the Fitbit Inspire 2 were compared among these groups. The Mann-Whitney U test was used to assess differences between the quartiles, with adjustments made for multiple comparisons using the Bonferroni correction. Furthermore, EMA results and the sleep and HRV indices were subjected to multilevel analysis for a comprehensive evaluation. The EMA achieved a total response rate of 87.3%, while the Fitbit Inspire 2 wear rate reached 88.0%. When participants were grouped based on quartiles of well-being and stress-related scores, significant differences emerged. Specifically, individuals in the lowest stress quartile or highest subjective satisfaction quartile retired to bed earlier (P<.001 and P=.01, respectively), whereas those in the highest stress quartile exhibited greater variation in the midpoint of sleep (P<.001). A multilevel analysis unveiled notable relationships: intraindividual variability analysis indicated that higher energy levels were associated with lower deviation of heart rate during sleep on the preceding day (β=-.12, P<.001), and decreased sleepiness was observed on days following longer sleep durations (β=-.10, P<.001). Furthermore, interindividual variability analysis revealed that individuals with earlier midpoints of sleep tended to exhibit higher energy levels (β=-.26, P=.04). Increased sleep variabilities, characterized by unstable bedtime or midpoint of sleep, were correlated with elevated stress levels and diminished well-being. Conversely, improved sleep indices (eg, lower heart rate during sleep and earlier average bedtime) were associated with heightened daytime energy levels. Further research with a larger sample size using these methodologies, particularly focusing on specific phenomena such as social jet lag, has the potential to yield valuable insights. UMIN-CTR UMIN000046858; https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000053392.

BAFNet: Bottleneck Attention Based Fusion Network for Sleep Apnea Detection.

Sleep apnea (SA) is a common sleep-related breathing disorder that tends to induce a series of complications, such as pediatric intracranial hypertension, psoriasis, and even sudden death. Therefore, early diagnosis and treatment can effectively prevent malignant complications SA incurs. Portable monitoring (PM) is a widely used tool for people to monitor their sleep conditions outside of hospitals. In this study, we focus on SA detection based on single-lead electrocardiogram (ECG) signals which are easily collected by PM. We propose a bottleneck attention based fusion network named BAFNet, which mainly includes five parts of RRI (R-R intervals) stream network, RPA (R-peak amplitudes) stream network, global query generation, feature fusion, and classifier. To learn the feature representation of RRI/RPA segments, fully convolutional networks (FCN) with cross-learning are proposed. Meanwhile, to control the information flow between RRI and RPA networks, a global query generation with bottleneck attention is proposed. To further improve the SA detection performance, a hard sample scheme with k-means clustering is employed. Experiment results show that BAFNet can achieve competitive results, which are superior to the state-of-the-art SA detection methods. It means that BAFNet has great potential to be applied in the home sleep apnea test (HSAT) for sleep condition monitoring.

Associations of sleep disorders with serum neurofilament light chain levels in Parkinson’s disease

BackgroundSleep disorders are a prevalent non-motor symptom of Parkinson’s disease (PD), although reliable biological markers are presently lacking.ObjectivesTo explore the associations between sleep disorders and serum neurofilament light chain (NfL) levels in individuals with prodromal and early PD.MethodsThe study contained 1113 participants, including 585 early PD individuals, 353 prodromal PD individuals, and 175 healthy controls (HCs). The correlations between sleep disorders (including rapid eye movement sleep behavior disorder (RBD) and excessive daytime sleepiness (EDS)) and serum NfL levels were researched using multiple linear regression models and linear mixed-effects models. We further investigated the correlations between the rates of changes in daytime sleepiness and serum NfL levels using multiple linear regression models.ResultsIn baseline analysis, early and prodromal PD individuals who manifested specific behaviors of RBD showed significantly higher levels of serum NfL. Specifically, early PD individuals who experienced nocturnal dream behaviors (β = 0.033; P = 0.042) and movements of arms or legs during sleep (β = 0.027; P = 0.049) showed significantly higher serum NfL levels. For prodromal PD individuals, serum NfL levels were significantly higher in individuals suffering from disturbed sleep (β = 0.038; P = 0.026). Our longitudinal findings support these baseline associations. Serum NfL levels showed an upward trend in early PD individuals who had a higher total RBDSQ score (β = 0.002; P = 0.011) or who were considered as probable RBD (β = 0.012; P = 0.009) or who exhibited behaviors on several sub-items of the RBDSQ. In addition, early PD individuals who had a high total ESS score (β = 0.001; P = 0.012) or who were regarded to have EDS (β = 0.013; P = 0.007) or who exhibited daytime sleepiness in several conditions had a trend toward higher serum NfL levels.ConclusionSleep disorders correlate with higher serum NfL, suggesting a link to PD neuronal damage. Early identification of sleep disorders and NfL monitoring are pivotal in detecting at-risk PD patients promptly, allowing for timely intervention. Regular monitoring of NfL levels holds promise for tracking both sleep disorders and disease progression, potentially emerging as a biomarker for evaluating treatment outcomes.

OBSTRUCTIVE SLEEP APNEA AND TARGET BP VALUE IN NON-OBESE HYPERTENSIVE PATIENTS

Objective: To assess obstructive sleep apnea impact on the target blood pressure (BP) value achievement in non-obese hypertensive patients. Design and method: In this prospective cross-sectional study were included 305 patients (mean age 60.0 years old [48.0; 67.0], 203 men) with different stages and grades of arterial hypertension (AH). All patients have been receiving antihypertension therapy, prescribed by their cardiologist, for at least 3 weeks before inclusion in the study. During the study, office systolic and dyastolic blood pressure (SBP and DBP resp.) were assessed (oscillometric method, Omron, Japan) and home sleep respiratory monitoring (SomnoChek, Weinmann, Germany) was done in all patients. Results: Two groups were formed: non-target BP group (BP&gt;140/90 mm Hg, n=164) and target BP group (BP&lt;140/90 mm Hg, n=141). Comparative data analysis included such indicators as: age, body mass index (BMI), apnea/hypopnea index (AHI), desaturation index (DI) and minimum oxygen saturation (min SaO2), revealed only differences between age (61.0 years old [50.0; 68.0] versus 57.0 years old [47.0; 66.0] resp., p=0.03) and BMI (32.5 kg/m2 [29.7; 38.1] vs. 31.6 kg/m2 [27.8; 35.5] resp., p=0.02). The analysis of non-obese patients’ data (BMI &lt;30 kg/m2, n=100) didn’t reveal differences by age, but a higher value of AHI and DI were determined in patients with non-target BP, as well as higher BMI; similar differences were not found in obese patients (BMI&gt;30 kg/m2, n=205) (Table 1). In two-factor logistic regression analysis (included AHI and BMI) AHI was the only predictor of non-target BP in patients without obesity, while BMI had no predictive value (Table 2). Conclusions: Even mild or moderate obstructive sleep apnea may be an important negative factor in achieving BP targets in non-obese patients, unlike the other clinical cases with AH.

Cardiopulmonary Coupling Spectrogram as an Ambulatory Method for Assessing Sleep Disorders in Patients With Autoimmune Encephalitis.

Sleep disorders are a common and important clinical feature in patients with autoimmune encephalitis (AE); however, they are poorly understood. We aimed to evaluate whether cardiopulmonary coupling (CPC), an electrocardiogram-based portable sleep monitoring technology, can be used to assess sleep disorders in patients with AE. Patients fulfilling the diagnostic criteria of AE were age- and sex-matched with recruited healthy control subjects. All patients and subjects received CPC testing between August 2020 and December 2022. Demographic data, clinical information, and Pittsburgh Sleep Quality Index (PSQI) scores were collected from the medical records. Data analysis was performed using R language programming software. There were 60 patients with AE (age 26.0 [19.8-37.5] years, male 55%) and 66 healthy control subjects (age 30.0 [25.8-32.0] years, male 53%) included in this study. Compared with healthy subjects, patients with AE had higher PSQI scores (7.00 [6.00-8.00] vs 3.00 [2.00-4.00], p < 0.001), lower sleep efficiency (SE 80% [71%-87%] vs 92% [84%-95%], p < 0.001), lower percentage of high-frequency coupling (25% [14%-43%] vs 45% [38%-53%], p < 0.001), higher percentage of REM sleep (19% ± 9% vs 15% ± 7%, p < 0.001), higher percentage of wakefulness (W% 16% [11%-25%] vs 8% [5%-16%], p = 0.074), higher low-frequency to high-frequency ratio (LF/HF 1.29 [0.82-2.40] vs 0.91 [0.67-1.29], p = 0.001), and a higher CPC-derived respiratory disturbance index (9.78 [0.50-22.2] vs 2.95 [0.40-6.53], p < 0.001). Follow-up evaluation of 14 patients showed a decrease in the PSQI score (8.00 [6.00-9.00] vs 6.00 [5.00-7.00], p = 0.008), an increased SE (79% [69%-86%] vs 89% [76%-91%], p = 0.030), and a decreased W% (20% [11%-30%] vs 11% [8%-24], p = 0.035). Multiple linear regression indicated that SE (-7.49 [-9.77 to -5.21], p < 0.001) and LF/HF ratio (0.37 [0.13-0.6], p = 0.004) were independent factors affecting PSQI scores in patients with AE. Sleep disorders with autonomic dysfunction are common in patients with AE. Improvements in the PSQI score and SE precede the restoration of sleep microstructural disruption in the remission stage. CPC parameters may be useful in predicting sleep disorders in patients with AE.

Higher ambient temperatures may worsen obstructive sleep apnea: A nationwide smartwatch-based analysis of 6.2 million person-days

Obstructive sleep apnea (OSA) is a serious type of sleep disorder that can lead to cardiometabolic and neurocognitive diseases. We utilized smart device-based photoplethysmography technology to collect sleep data from the Chinese population from 2019 to 2022. Distributed lag nonlinear models combined with a generalized nonlinear model or a linear mixed effects model were used to investigate the short-term associations between daily temperature and indicators of OSA severity. We included a total of 6,232,056 d of sleep monitoring data from 51,842 participants with moderate to severe risk of OSA from 313 Chinese cities. The relationships between ambient temperature and OSA exacerbation, apnea-hypopnea index (AHI), and minimum oxygen saturation (MinSpO2) were almost linear and present only on the same day. Higher temperatures were associated with a greater risk of OSA exacerbation, with an 8.4% (95% confidence interval (CI): 7.6%–9.3%) increase per 10 °C increase in temperature. A 10 °C increase in daily temperature corresponded to an AHI increase of 0.70 events/h (95% CI: 0.65–0.76) and a MinSpO2 decrease of 0.18% (95% CI: 0.16%–0.19%). Exposure to elevated temperatures during the night can also lead to adverse effects. The effects of higher temperatures on OSA severity were stronger among men, participants with a body mass index ≥ 24 kg/m2, those aged 45 years and older, individuals with a history of hypertension and diabetes, and during the cold season. This large-scale, nationwide, longitudinal study provides robust evidence suggesting that higher ambient temperatures may immediately worsen OSA.

Habitual Sleep and Muscle Sympathetic Nerve Activity in Humans

Short habitual sleep duration and inadequate sleep effciency are associated with poor cardiovascular health. Although a potential mediator of this relationship is augmented sympathetic activity, only one study to date has assessed the relationship between objectively measured habitual sleep duration and direct muscle sympathetic nerve activity (MSNA) in humans. Moreover, the proportion of resting MSNA that can be independently explained by habitual sleep while controlling for other key variables remains unknown. The purpose of the present study was to assess the independent relationship between total sleep time (TST), sleep effciency (SE), and MSNA in a large population of young males and females. We hypothesized that short TST and low SE would be associated with elevated MSNA and blood pressure. A minimum 7 days of at-home, objective actigraphy sleep monitoring was collected in 66 healthy young adults (35 males, 31 females; 24±1 years; 26±1 kg/m2). In each participant, the average of three seated brachial blood pressures was recorded and used to determine resting mean arterial blood pressure (MAP). All participants underwent an autonomic function test that simultaneously assessed heart rate (HR, electrocardiogram), beat-to-beat blood pressure (finger plethysmography) and MSNA (microneurography) during 10 minutes of quiet rest. Resting MAP (83±1 mmHg), HR (64±1 beats/min) and MSNA (19±1 bursts/min) were normally distributed. Habitual TST was significantly correlated with MSNA (r=-0.265, p=0.032), but was not associated with resting MAP (r=-0.153, p=0.219) or HR (r=-0.205, p=0.098). SE was not associated with any of the outcome variables (all p&gt;0.25). Multiple regression was used to test the independent relationship between TST, MAP, HR, and MSNA while accounting for effects of age, sex, and body mass index (BMI). The regression model significantly predicted MAP (R2=0.253, p=0.001) and MSNA (R2=.211, p=0.005), but not HR (R2=.047, p=0.560). TST did not independently explain any additional variance in MAP (ΔR2=0.004, p=0.562) beyond that ascribed to the effects of age, sex, and BMI. In contrast, TST independently explained an additional 5% of the variance in MSNA (ΔR2=0.053, p=0.048). In a large sample of healthy young males and females, our results indicate that habitual TST explains only a minor proportion of the observed variance in resting MSNA between participants. NIH (AA-024892). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Portable Low-Cost Home Sleep Monitor using Wemos D1 Mini

Sleeps disorders are a common disease overlooked by many people. Sleep disorder have many types and kinds and often associated with other severe illness such as diabetes, stroke, obesity and many others. Sleep monitors are one of many ways to read all parameters related to sleep and detect sleep disorders the subject has, however access to sleep monitor still expensive and tough to come by. Because of that accessibility, countless development of home sleep monitor occured around the world. However, most of that device still hard to operate and some of them gives error readings of parameters. Based on this, a portable low-cost home sleep monitor was developed using Wemos D1 mini as a microcontroller, MAX30102 as an oxygen level sensor, MPU6050 as an accelerometer, DS18B20 as a breathing flow sensor, and MAX9814 as a microphone. Each of the sensors read and give value to microcontroller and store the data to cloud and display the result in user’s gadget. The aim of this development is to detect sleep disorders associated with each reading of sensors used and determine sleep quality as an early detection of symptoms before referring to professional relaled to sleep disorders or doctors.