The economic cost of sleep disorders

Serum vitamin D concentrations and sleep disorders: insights from NHANES 2011-2016 and Mendelian Randomization analysis

OBJECTIVE

This investigation seeks to examine the association between serum vitamin D concentrations and the prevalence of sleep disorders, additionally elucidating the causal relationship via Mendelian Randomization (MR) analysis.

MATERIALS AND METHODS

This research employed data from the National Health and Nutrition Examination Survey (NHANES) 2011-2016, focusing on adults aged 20-50 years reporting sleep disorders. The research encompassed 4913 American adults. Weighted multivariable logistic regression models and cubic spline analyses were utilized to evaluate the association between serum vitamin D concentrations and the incidence of sleep disorders. Additionally, a two-sample Mendelian Randomization analysis was performed to evaluate the potential causal link between serum vitamin D concentrations and the risk of sleep disorders.

RESULTS

Within the 2011-2016 NHANES cohort of the U.S. population, a notable inverse association was detected between serum vitamin D concentrations and sleep disorders (β =  - 3.81, 95% CI: - 6.10 to - 1.52, p = 0.003). After multivariate adjustments, a higher incidence of sleep disorders was associated with lower vitamin D Concentrations (OR 1.52, 95% CI 1.10-2.10, trend p = 0.014). Restricted cubic spline regression analysis indicated a linear association between serum vitamin D concentrations and sleep disorders(non-linearity p > 0.05). Lastly, the two-sample MR analysis yielded evidence supporting a potential causal connection between serum vitamin D concentrations and sleep disorders, with each unit increase in genetically predicted serum vitamin D reducing the odds ratio to 0.78 (95% CI 0.61-0.99, p = 0.044).

CONCLUSIONS

These results imply that lower vitamin D concentrations in the population might correlate with a heightened risk of sleep disorders, suggesting the importance of considering vitamin D supplementation when treating sleep disorders.

High Prevalence of Obstructive Sleep-Disordered Breathing in Pediatric Patients With Turner Syndrome

OBJECTIVE

Girls with Turner syndrome (TS) often have features that have been associated with obstructive sleep-disordered breathing (oSDB). However, little is known about oSDB in TS. Herein, we aimed to characterize oSDB in young patients with TS and identify associated risk factors.

STUDY DESIGN

Retrospective cross-sectional study.

SETTING

Tertiary care pediatric hospital.

METHODS

We reviewed medical records for patients diagnosed with TS seen at our institution between October 1, 2007 and December 31, 2019 with the first outpatient visit before age 6 years. The prevalence of oSDB was compared to the general pediatric population with 1-sample binomial proportion tests. Clinical characteristics were compared between those diagnosed with oSDB and those without oSDB, and risk factors for oSDB were identified.

RESULTS

Of 151 patients with TS, 73 (48%) were diagnosed with oSDB which is 4-fold higher than the general pediatric population (12%, P < 0.0001). In the multivariable model, adenoid, tonsillar, and inferior turbinate hypertrophy, birthweight, failure to thrive, and older age at the last clinic visit were all associated with increased odds for oSDB.

CONCLUSION

Young children with TS have a high prevalence of oSDB and thus should be screened for oSDB. Polysomnography should be performed in those with associated risk factors and symptoms oSDB. Treatment of oSDB is imperative as individuals with TS are already at increased risk of behavioral problems, neurocognitive deficits, and growth impairment that may be worsened with oSDB.

Application of LightGBM hybrid model based on TPE algorithm optimization in sleep apnea detection

Introduction

Sleep apnoea syndrome (SAS) is a serious sleep disorder and early detection of sleep apnoea not only reduces treatment costs but also saves lives. Conventional polysomnography (PSG) is widely regarded as the gold standard diagnostic tool for sleep apnoea. However, this method is expensive, time-consuming and inherently disruptive to sleep. Recent studies have pointed out that ECG analysis is a simple and effective diagnostic method for sleep apnea, which can effectively provide physicians with an aid to diagnosis and reduce patients' suffering.

Methods

To this end, in this paper proposes a LightGBM hybrid model based on ECG signals for efficient detection of sleep apnea. Firstly, the improved Isolated Forest algorithm is introduced to remove abnormal data and solve the data sample imbalance problem. Secondly, the parameters of LightGBM algorithm are optimised by the improved TPE (Tree-structured Parzen Estimator) algorithm to determine the best parameter configuration of the model. Finally, the fusion model TPE_OptGBM is used to detect sleep apnoea. In the experimental phase, we validated the model based on the sleep apnoea ECG database provided by Phillips-University of Marburg, Germany.

Results

The experimental results show that the model proposed in this paper achieves an accuracy of 95.08%, a precision of 94.80%, a recall of 97.51%, and an F1 value of 96.14%.

Discussion

All of these evaluation indicators are better than the current mainstream models, which is expected to assist the doctor's diagnostic process and provide a better medical experience for patients.

Standardized Extract of Valeriana officinalis Improves Overall Sleep Quality in Human Subjects with Sleep Complaints: A Randomized, Double-Blind, Placebo-Controlled, Clinical Study

INTRODUCTION

Sleep deficit or poor sleep leads to ill-health, whereas sleep deprivation for longer periods of time increases the risk of developing adverse conditions associated with poor quality of life, and high socioeconomic impact. The treatments for sleep disturbances include melatonin and over-the-counter medicines like diphenhydramine and doxylamine, all of which have negative side effects. Valerian (Valeriana officinalis L.) is a traditional herb and the most preferred alternate sleep solution to manage sleep complaints.

METHODS

Eighty adult subjects with sleep complaints were randomized in 1:1 ratio to receive either V. officinalis extract (VE) or placebo for 8 weeks in a double-blind, placebo-controlled, parallel, clinical study. Primary efficacy endpoints included the Pittsburgh Sleep Quality Index (PSQI) and sleep latency using wrist actigraphy (WA), as well as a number of secondary endpoints, including sleep parameters such as actual sleep time and sleep efficiency using WA, the Epworth Sleepiness Scale (ESS), the Beck Anxiety Inventory (BAI), the Visual Analogue Scale (VAS) for the feeling of waking up refreshed, and a tertiary endpoint of sleep parameters using polysomnography (PSG) in a subset of 20 subjects per group. Safety parameters included physical examination, vital sign measurements, hematology, and clinical chemistry tests. Adverse events and serious adverse events were monitored throughout the study period.

RESULTS

Seventy-two subjects (35 and 37 subjects in the placebo and VE groups, respectively) completed the study and were included in the efficacy assessments. On Days 14, 28, and 56, the PSQI Total Score in the VE group decreased significantly (p < 0.05) compared to the placebo group. Further, the VE group showed significant improvements (p < 0.05) in sleep latency and actual sleep time on Days 3, 14, 28, and 56, and sleep efficiency on Days 14, 28, and 56, as evaluated by WA. There was a decrease (p < 0.05) in anxiety (BAI) on Days 14, 28, and 56, daytime drowsiness (ESS) on Days 28 and 56, and an increased feeling of waking up refreshed (VAS) on Days 28 and 56 compared to placebo. PSG results carried out in subset of subjects revealed significant improvements (p < 0.05) in total sleep time, sleep latency, and sleep efficiency on Day 56 in the VE group compared to the placebo group. No safety concerns were observed throughout the study.

CONCLUSION

VE supplementation significantly improved various subjective and objective parameters of sleep in young subjects with mild insomnia symptoms, such as overall sleep quality, sleep latency, sleep efficiency, and total sleep time. We also observed decreased anxiety and daytime sleepiness, and improved feeling of being refreshed after waking up with VE supplementation. VE was found to be safe and well tolerated throughout the study.

TRIAL REGISTRATION

Clinical Trials Registry of India: CTRI/2022/05/042818.

An Overview of Systematic Reviews on the Surgical Management of Obstructive Sleep Apnoea

Background

Obstructive Sleep Apnoea (OSA) is a common sleep disorder marked by partial or total obstruction of the upper airway while a person is asleep leading to breathing difficulty, reduced oxygenation and frequent awakenings. This condition affects the general health significantly compromising quality of life. The objective of this overview  is to thoroughly assess the systematic reviews on current surgical therapies for the management of OSA in terms of patient outcomes.

Methods

A thorough literature search was performed from inception till 31st December 2022 using PubMed, and Cochrane databases. Studies evaluating the effectiveness and safety of different surgical techniques for the management of OSA were considered. The quality of articles was assessed using AMSTAR (A MeaSurement Tool to Assess systematic Reviews) and Glenny et al. checklist.

Results

Out of eighteen studies, only seven studies met the inclusion and exclusion criteria. Results showed that the majority of studies were in the pediatric age group except one systematic  review which assessed the adult age group.

Conclusion

None of the published articles had compared all surgical procedures based on the standard evaluating procedure nor followed all reporting guidelines in the primary studies. For better implementation, further multi center studies are warranted with unique reporting criteria and guidelines about pre- as well as post-operative phases. Future research should concentrate on contrasting strategies, combination therapies, and evaluating long-term effects.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12663-023-02051-x.

Association Between Positive Airway Pressure Adherence and Health Care Costs Among Individuals With OSA

BACKGROUND

The impact of positive airway pressure (PAP) therapy for OSA on health care costs is uncertain.

RESEARCH QUESTION

Are 3-year health care costs associated with PAP adherence in participants from the Tele-OSA clinical trial?

STUDY DESIGN AND METHODS

Participants with OSA and prescribed PAP in the Tele-OSA study were stratified into three PAP adherence groups based on usage patterns over 3 years: (1) high (consistently ≥ 4 h/night), (2) moderate (2-3.9 h/night or inconsistently ≥ 4 h/night), and (3) low (< 2 h/night). Using data from 3 months of the Tele-OSA trial and 33 months of posttrial follow up, average health care costs (2020 US dollars) in 6-month intervals were derived from electronic health records and analyzed using multivariable generalized linear models.

RESULTS

Of 543 participants, 25% were categorized as having high adherence, 22% were categorized as having moderate adherence, and 52% were categorized as having low adherence to PAP therapy. Average PAP use mean ± SD was 6.5 ± 1.0 h, 3.7 ± 1.2 h, and 0.5 ± 0.5 h for the high, moderate, and low adherence groups, respectively. The high adherence group had the lowest average covariate-adjusted 6-month health care costs ± SE ($3,207 ± $251) compared with the moderate ($3,638 ± $363) and low ($4,040 ± $304) adherence groups. Significant cost differences were observed between the high and low adherence groups ($832; 95% CI, $127 to $1,538); differences between moderate and low adherence were nonsignificant ($401; 95% CI, -$441 to $1,243).

INTERPRETATION

In participants with OSA, better PAP adherence was associated with significantly lower health care costs over 3 years. Findings support the importance of strategies to enhance long-term PAP adherence.

Targeted Hypoglossal Nerve Stimulation for Patients With Obstructive Sleep Apnea: A Randomized Clinical Trial

Importance

Evidence is lacking from randomized clinical trials of hypoglossal nerve stimulation in obstructive sleep apnea (OSA).

Objective

To evaluate the safety and effectiveness of targeted hypoglossal nerve stimulation (THN) of the proximal hypoglossal nerve in patients with OSA.

Design, Setting, and Participants

This randomized clinical trial (THN3) was conducted at 20 centers and included 138 patients with moderate to severe OSA with an apnea-hypopnea index (AHI) of 20 to 65 events per hour and body mass index (calculated as weight in kilograms divided by height in meters squared) of 35 or less. The trial was conducted from May 2015 through June 2018. Data were analyzed from January 2022 through January 2023.

Intervention

Implant with THN system; randomized 2:1 to activation at month 1 (treatment) or month 4 (control). All received 11 months of THN with follow-up at months 12 and 15, respectively.

Main Outcomes and Measures

Primary effectiveness end points comprised AHI and oxygen desaturation index (ODI) responder rates (RRs). Treatment responses at months 4 and 12/15 were defined as a 50% or greater reduction in AHI to 20 or less per hour and an ODI decrease of 25% or greater. Coprimary end points comprised (1) month 4 AHI and ODI RR in the treatment greater than the control group and (2) month 12/15 AHI and ODI RR in the entire cohort exceeding 50%. Secondary end points included sleep apnea severity (AHI and ODI) and patient-reported outcomes (Epworth Sleepiness Scale, Functional Outcomes of Sleep Questionnaire, and EQ-5D visual analog scale).

Results

Among 138 participants, the mean (SD) age was 56 (9) years, and 19 (13.8%) were women. Month 4 THN RRs were substantially greater in those in the treatment vs control group (AHI, 52.3% vs 19.6%; ODI, 62.5% vs 41.3%, respectively) with treatment-control standardized mean differences of 0.725 (95% CI, 0.360-1.163) and 0.434 (95% CI, 0.070-0.843) for AHI and ODI RRs, respectively. Months 12/15 RRs were 42.5% and 60.4% for AHI and ODI, respectively. Improvements in AHI, ODI, Epworth Sleepiness Scale, Functional Outcomes of Sleep Questionnaire, and EQ-5D visual analog scale scores were all clinically meaningful (medium to large effect size). Two serious adverse events and 100 nonserious related adverse events were observed from the implant procedure or study protocol.

Conclusions and Relevance

This randomized clinical trial found that THN demonstrated improvements in sleep apnea, sleepiness, and quality of life in patients with OSAs over an extended AHI and body mass index range without prior knowledge of pharyngeal collapse pattern. Clinically meaningful improvements in AHI and patient-reported responses compared favorably with those of distal hypoglossal nerve stimulation trials, although clinically meaningful differences were not definitive for ODI.

Trial Registration

ClinicalTrials.gov Identifier: NCT02263859.

Patient-reported Differences in Obstructive Sleep Apnea Care between Jurisdictions with and without Government Funding for Continuous Positive Airway Pressure

Rationale: Funding for obstructive sleep apnea (OSA) treatment may impact how patients access care, wait times, and costs of care. Objectives: The aim of this study was to compare differences in diagnosis and treatment of OSA between Canadian jurisdictions with and without public funding for continuous positive airway pressure (CPAP). Methods: We administered an anonymous internet survey to Canadian adults reporting a physician diagnosis of OSA. Responses were categorized on the basis of whether the respondent's province provided full or partial funding for CPAP therapy for all patients. We assessed wait times for diagnosis and treatment, patient-borne costs, and model of care delivery compared between jurisdictions with and without universal CPAP funding. Results: We received 600 responses representing all Canadian provinces and territories. The median (interquartile range) age was 59 (49-66) years; 57% were male, and 21% were from rural settings. Patients living in provinces without public CPAP funding (n = 419) were more likely to be diagnosed using home sleep apnea testing (69% vs. 20%; P = 0.00019). Wait times were similar after adjustment for demographics, disease characteristics, and model of care. Although patient-borne costs of care were similar between jurisdictions, patients from regions without CPAP funding reported that cost had a greater influence on the choice of therapy. Sleep specialists were more commonly involved in OSA care in regions with CPAP funding. There was no difference in the current use of therapy between jurisdictions with and without public funding. Conclusions: This survey study demonstrates that public funding for CPAP therapy impacts how Canadians access OSA care but is not associated with differences in wait times or costs. Future research is required to determine the impact of different funding models for OSA care on clinical outcomes.

Sleep profile status based on substance use, lipids and demographic variables in Tabari cohort study

BACKGROUND

This study aims to investigate the situation of sleep profile and its related factors in the Tabari Cohort Tabari (TCS) population.

METHODS

The information of 10255 of the Tabari cohort population in the enrolment phase was used in this study. The sleep profile data was collected and recorded by trained questioners. The sleep duration in day & night, the time interval between going bed and falling asleep, continuous use of sedatives, involuntary nap, limb hypermobility during sleep and shift working were determined for each person. Data analysis was performed by independent T test and Pearson correlation coefficient.

RESULTS

Mean, standard deviation, median, minimum and maximum of sleep duration in this population were 7.6, 1.6, 7.5, 0.5 and 17 h. Frequency of sleeping less than 6 h, 6-10 h and more than 10 h were 1168(11.4%), 8463(82.5%) and 624(6.1%) respectively. Prevalence of sleeping more than 10 h among men and women were 5% and 6.8% respectively (P < 0.001). Prevalence of sedative routine use among men and women were 4.7% and 9.6% respectively (P < 0.001). There were significant relationships between sleep duration and area residence, age group (P < 0.001), education level (P < 0.001), socioeconomic level (P < 0.001), triglyceride (P = 0.002), HDL-cholesterol (P = 0.013) and Cholesterol total (P = 0.021). There was a negative correlation between age and sleep duration (r = -0.062, P < 0.001).

CONCLUSION

The results showed the association of the quality and quantity of sleep with personal, social, environmental and biological factors such as gender, age, economic status, educational status, and lipid profile. Therefore without proper intervention, the incidence of outcomes associated with these risk factors can be predicted in TCS In later years.

Potential mechanisms underlying the accelerated cognitive decline in people with chronic low back pain: A scoping review

A growing body of evidence has shown that people with chronic low back pain (CLBP) demonstrate significantly greater declines in multiple cognitive domains than people who do not have CLBP. Given the high prevalence of CLBP in the ever-growing aging population that may be more vulnerable to cognitive decline, it is important to understand the mechanisms underlying the accelerated cognitive decline observed in this population, so that proper preventive or treatment approaches can be developed and implemented. The current scoping review summarizes what is known regarding the potential mechanisms underlying suboptimal cognitive performance and cognitive decline in people with CLBP and discusses future research directions. Five potential mechanisms were identified based on the findings from 34 included studies: (1) altered activity in the cortex and neural networks; (2) grey matter atrophy; (3) microglial activation and neuroinflammation; (4) comorbidities associated with CLBP; and (5) gut microbiota dysbiosis. Future studies should deepen the understanding of mechanisms underlying this association so that proper prevention and treatment strategies can be developed.

Economic costs and Predictors of occupation-related Injuries in Ethiopian sugar industries from the Employer’s perspective: top-down approach and friction method

Background

For many industrial workers, occupational injuries are a common health and safety concern. However, sufficient information on the economic costs and predictors of occupation-related injuries from the perspective of employers is lacking in developing countries, including Ethiopia. The objective of this study was to close this gap by quantifying the economic costs and predictors of occupation-related injuries in Ethiopian manufacturing industries from the employer’s perspective.

Methods

A cross-sectional study was employed to estimate the employer-side economic cost of occupation-related injuries from December 2021 to March 2022. This study used a top-down approach to compute direct costs, while the friction method was used for indirect cost estimation. Injury data were obtained from the Bureau of Labour and Social Affairs and the industries, while cost data were from workers’ compensation records. The insurance company’s injury compensation record was triangulated with industries’ data. The study collected primary data via an interview-administered, semi-structured questionnaire from 1136 randomly selected injured cases. Statistical analysis was carried out with STATA version 14 software. The study employed a generalized linear model to identify predictors of total cost by considering the non-normal distribution of the total cost. Exponentiate coefficients with a 95% confidence interval were used to express the direction and strength of the association.

Results

The survey participation rate was 100%. From the perspective of the employers, the total cost of occupation–related injury was 22,587,635.32 Ethiopian birr (537,800.84 $).Indirect and direct costs accounted for 65.86 and 34.14% of the overall expenses, respectively. Long-term absence from work (exp (b) = 0.85), having a sleeping disorder (exp (b) = 0.90), co-morbidity (exp (b) = 0.85), and severity (type) of injury (exp (b) = 1.11) were predictors significantly associated with the total cost variability in the fully adjusted model.

Conclusions

Employers’ toll of occupation-related injuries has severe economic implications. The influential factors that elevated the total cost variation were: long-term absence from work, unsafe acts of the workers, having a sleeping disorder, co-morbidity, and severity (type) of injury. Therefore, the identified modifiable factors are the areas of intervention to reduce the cost of occupation-related injuries.

The associations of BMI, chronic conditions and lifestyle factors with insomnia symptoms among older adults in India

Background

The aim of the study was to estimate the prevalence of insomnia symptoms and to examine the associations of body mass index (BMI), chronic diseases, and lifestyle factors with self-reported insomnia symptoms among older people in India.

Methods

We conducted a cross-sectional study using data from the baseline wave of the Longitudinal Ageing Study in India (LASI) that was collected during 2017–18. A sample of 31,358 older adults aged 60 and above was included in the analyses. Descriptive statistics and bivariate and multivariable analyses were performed to obtain the results.

Results

In this study, insomnia symptoms were reported by around 36 percent of older adults aged 60 and above. After controlling for socio-demographic factors, insomnia symptoms were positively associated with the risk of being underweight [AOR: 1.289, CI: 1.211–1.372] and negatively associated with obesity/overweight [AOR: 0.928, CI: 0.872–0.990] as compared to older adults with normal BMI. The odds of insomnia symptoms were higher among those who reported the following chronic conditions, i.e., hypertension [AOR:1.356, CI:1.278–1.438], diabetes [AOR:1.160, CI:1.074–1.254], chronic lung diseases [AOR:1.485, CI:1.351–1.632], bone-related diseases [AOR:1.561, CI:1.458–1.670] and any psychiatric disorders [AOR:1.761, CI:1.495–2.074]. In addition, older adults who were physically active [AOR: 0.850, CI:0.804–0.900] were less likely to report insomnia symptoms.

Conclusions

The study suggests a high prevalence of insomnia symptoms among the older population in India. Early identification of the signs of insomnia in older population is crucial, as is timely treatment for any kind of sleep problems. In addition, nutrition-based interventions and individual disease-specific management programs may help minimize the stressful situations in later life and develop a good night’s sleep for the older population.

Association of obstructive sleep apnea and opioids use on adverse health outcomes: A population study of health administrative data

Rationale

Despite the high prevalence of obstructive sleep apnea (OSA) and concurrent use of opioid therapy, no large-scale population studies have investigated whether opioid use and pre-existing OSA may interact synergistically to increase the risk of adverse health consequences. To address this knowledge gap, we conducted a retrospective cohort study using provincial health administrative data to evaluate whether the combined presence of opioid use and OSA increases the risk of adverse health consequences, such as mortality, hospitalizations, and emergency department (ED) visits; and if it does, whether this co-occurrence has synergistic clinical relevance.

Methods

We included all adults who underwent a diagnostic sleep study in Ontario, Canada, between 2013 and 2016. Individuals were considered exposed to opioids if they filled a prescription that overlapped with the date of their sleep study (Opioid+). Individuals with at least a 50% probability of having a diagnosis of moderate to severe OSA (OSA+) were identified using a previously externally validated case-ascertainment model. The primary outcome was all-cause mortality; secondary outcomes were all-cause or ischemic heart disease hospitalizations, all-cause ED visits, and motor vehicle collisions (MVC) requiring hospital or ED visit. We used multivariable Cox regression models to compare hazards between four mutually exclusive groups: (1) Opioid+ OSA+; (2) Opioid+ OSA-; (3) Opioid- OSA+, and (4) OSA- Opioid- (reference for comparison). Relative excess risks due to interaction (RERI) were calculated to test for additive interaction.

Results

Of 300,663 adults who underwent a sleep study, 15,713 (5.2%) were considered as Opioid+ and 128,351 (42.7%) as OSA+. Over a median of two years, 6,223 (2.1%) died from any cause. Regardless of OSA status, opioid use at the date of the sleep study was associated with an increased hazard for all-cause mortality with the greatest hazard associated with Opioid+ OSA- (adjusted hazard ratio [aHR]: 1.75, 95% CI 1.57–1.94), but not Opioid+ OSA+ (aHR: 1.14, 95% CI 1.02–1.27) as hypothesized. Regardless of OSA status, opioid use at the date of the sleep study was associated with an increased hazard for all secondary outcomes. Opioid+ OSA+ was associated with the greatest hazards of all-cause hospitalizations (aHR 1.55, 95% CI 1.49–1.61) and MVC (aHR of 1.39; 95% CI 1.09–1.77); however, no statistically significant synergistic effects were observed.

Conclusions

Adults referred for sleep disorder assessment who used opioids had a significantly increased hazard of adverse health outcomes than those who did not, regardless of whether they had a high probability of moderate to severe OSA. The use of opioids and OSA was associated with the greatest hazard of all-cause hospitalizations and MVC requiring hospital or ED visit. The interaction of opioids and OSA did not confer a synergistic risk for poor outcomes.

Sleep Deficiency in Obstructive Sleep Apnea

Sleep deficiency in patients with obstructive sleep apnea includes abnormal quality, timing, and duration of sleep, and the presence of other comorbid conditions. These include insomnia, circadian misalignment disorders, and periodic limb movements of sleep. The co-occurrence of these conditions with obstructive sleep apnea likely plays a role in the pathogenesis, clinical presentation, and management of obstructive sleep apnea. Considering these conditions and their treatment in evaluating sleep deficiency in obstructive sleep apnea may help to improve patient outcomes. However, future research is needed to understand the intersection between obstructive sleep apnea and these disorders.

Adherence Index: sleep depth and nocturnal hypoventilation predict long-term adherence with positive airway pressure therapy in severe obstructive sleep apnea

STUDY OBJECTIVES

Treatment of obstructive sleep apnea with positive airway pressure (PAP) devices is limited by poor long-term adherence. Early identification of individual patients' probability of long-term PAP adherence would help in their management. We determined whether conventional polysomnogram (PSG) scoring and measures of sleep depth based on the odds ratio product would predict adherence with PAP therapy 12 months after it was started.

METHODS

Patients with obstructive sleep apnea referred to an academic sleep center had split-night PSG, arterial blood gases, and a sleep questionnaire. Multiple linear regression analysis of conventional PSG scoring and the odds ratio product both during diagnostic PSG and PAP titration provided an "Adherence Index," which was correlated with PAP use 12 months later.

RESULTS

Patients with obstructive sleep apnea (n = 236, apnea-hypopnea index 72.2 ± 34.1 events/h) were prescribed PAP therapy (82% received continuous PAP, 18% received bilevel PAP). Each patient's adherence with PAP therapy 12 months later was categorized as "never used," "quit using," "poor adherence," and "good adherence." PSG measures that were most strongly correlated with PAP adherence were apnea-hypopnea index and odds ratio product during nonrapid eye movement sleep; the additional contribution of nocturnal hypoxemia to this correlation was confined to those with chronic hypoventilation treated with bilevel PAP. The Adherence Index derived from these measures, during both diagnostic PSG and PAP titration, was strongly correlated with PAP adherence 12 months later.

CONCLUSIONS

Long-term adherence with PAP therapy can be predicted from diagnostic PSG in patients with severe obstructive sleep apnea, which may facilitate a precision-based approach to PAP management.

CITATION

Younes MK, Beaudin AE, Raneri JK, Gerardy BJ, Hanly PJ. Adherence Index: sleep depth and nocturnal hypoventilation predict long-term adherence with positive airway pressure therapy in severe obstructive sleep apnea. J Clin Sleep Med. 2022:18(8):1933-1944.

The association between sleep disorders on employment and income among adults in the United States

STUDY OBJECTIVES

To examine the association between sleep disorders, employment status, and income among US adults aged 18-64 years.

METHODS

The 2018-2019 Medical Expenditure Panel Survey medical conditions file was queried for adults aged 18-64 years with a diagnosis of a sleep disorder (International Classification of Diseases, 10th Revision, code G47.x) and linked to the household dataset. Comparisons in rates of wage, supplemental, and social security income as well as mean wage income were made between those with and without sleep disorders. Multivariate analyses, adjusting for demographics and comorbidities, were conducted.

RESULTS

A sleep disorder was diagnosed in 4.4% ± 0.2% of the study population (approximately 8.8 ± 0.4 million adults aged 18-64 years in the United States; mean age 46.9 years, 55.8% female). Adults with sleep disorders were less likely to have wage income from employment (adjusted odds ratio 0.5, 95% confidence interval 0.4-0.6, P < .001) and more likely to have Supplemental Security (1.8, 1.4-2.4, P < .001) and Social Security income (2.0, 1.6-2.4, P < .001) compared with those without sleep disorders. Among wage-earning adults, those with sleep disorders had $2,496 less mean annual wage income compared with those without a sleep disorder ($20,445 vs $22,941, P = .007), adjusting for demographics and comorbidities.

CONCLUSIONS

There are significantly higher indirect costs associated with sleep disorders. This study specifically demonstrates lower employment rates, lower wages, and higher welfare income utilization among those with sleep disorders despite using a very conservative estimate of sleep disorder prevalence.

CITATION

Huyett P, Bhattacharyya N. The association between sleep disorders, employment, and income among adults in the United States. J Clin Sleep Med. 2022;18(8):1967-1972.

Obstructive Sleep Apnea Affects Lacrimal Gland Function

Purpose

To determine the effect of obstructive sleep apnea syndrome (OSA) on lacrimal gland function and its mechanism.

Methods

Male mice aged seven to eight weeks were housed in cages with cyclic intermittent hypoxia to mimic OSA, and the control group was kept in a normal environment. Slit-lamp observation, fluorescein staining, and corneal sensitivity detection are used to assess cornea changes. Tear secretion was detected by phenol red cotton thread, and the pathological changes of lacrimal gland were observed by hematoxylin and eosin staining, oil red O staining, cholesterol and triglyceride kits, immunofluorescence staining, immunohistochemical staining, real-time polymerase chain reaction, transmission electron microscopy, and Western blot.

Results

Studies revealed a decreased tear secretion, corneal epithelial defects and corneal hypersensitivity. Myoepithelial cell damage, abnormal lipid accumulation, reduced cell proliferation, increased apoptosis and inflammatory cell infiltration in the lacrimal gland were also seen. Hifα and NF-κB signaling pathways, moreover, were activated, while Pparα was downregulated, in the lacrimal glands of OSA mice. Fenofibrate treatment significantly alleviated pathological changes of the lacrimal gland induced by OSA.

Conclusion

OSA disturbs the Hifα/Pparα/NF-κB signaling axis, which affects lacrimal gland structure and function and induces dry eye.

A jerk-based algorithm ACCEL for the accurate classification of sleep–wake states from arm acceleration

Arm acceleration data have been used to measure sleep–wake rhythmicity. Although several methods have been developed for the accurate classification of sleep–wake episodes, a method with both high sensitivity and specificity has not been fully established. In this study, we developed an algorithm, named ACceleration-based Classification and Estimation of Long-term sleep–wake cycles (ACCEL) that classifies sleep and wake episodes using only raw accelerometer data, without relying on device-specific functions. The algorithm uses a derivative of triaxial acceleration (jerk), which can reduce individual differences in the variability of acceleration data. Applying a machine learning algorithm to the jerk data achieved sleep–wake classification with a high sensitivity (>90%) and specificity (>80%). A jerk-based analysis also succeeded in recording periodic activities consistent with pulse waves. Therefore, the ACCEL algorithm will be a useful method for large-scale sleep measurement using simple accelerometers in real-world settings.

•

An algorithm for sleep-wake classification based on arm acceleration is presented

•

The algorithm only uses a derivative of triaxial arm acceleration (jerk)

•

The algorithm can accurately detect temporal awake during sleep

Sleep EEG characteristics associated with total sleep time misperception in young adults: an exploratory study

Background

Power spectral analysis (PSA) is one of the most commonly-used EEG markers of cortical hyperarousal, and can help to understand subjective–objective sleep discrepancy (SOD). Age is associated with decreased sleep EEG activity; however, the PSA of young adults is currently limited. Thus, this study aimed to examine the correlation of spectral EEG power with total sleep time (TST) misperception in young patients.

Methods

Forty-seven young adults were recruited and underwent a polysomnography recording in a sleep laboratory. Clinical records and self-report questionnaires of all patients were collected, and were used to categorize patients into a good sleeper (GS) group (n = 10), insomnia with a low mismatch group (IWLM, n = 19) or participant with a high mismatch group (IWHM, n = 18). PSA was applied to the first 6 h of sleep.

Results

IWHM patients exhibited a higher absolute power and relative beta/delta ratio in the frontal region compared to the GS group. No significant difference was observed between the IWLM and GS groups. No significant difference in the above parameters was observed between the IWHM and IWLM groups. Moreover, The SOD of TST was positively correlated with frontal absolute power and the relative beta/delta ratio (r = 0.363, P = 0.012; r = 0.363, P = 0.012), and absolute beta EEG spectral power (r = 0.313, P = 0.032) as well as the number of arousals.

Conclusions

Increased frontal beta/delta ratio EEG power was found in young patients with a high mismatch but not in those with a low mismatch, compared with good sleepers. This suggests that there exists increased cortical activity in IWHM patients. In addition, the frontal beta/delta ratio and the number of arousals was positively correlated with the SOD of TST.

Plasma profiling reveals a blood-based metabolic fingerprint of obstructive sleep apnea

INTRODUCTION

Obstructive sleep apnea (OSA) is a chronic, heterogeneous and multicomponent disorder with associated cardiovascular and metabolic alterations. Despite being the most common sleep-disordered breathing, it remains a significantly undiagnosed condition.

OBJECTIVE

We examined the plasma metabolome and lipidome of patients with suspected OSA, aiming to identify potential diagnosis biomarkers and to provide insights into the pathophysiological mechanisms underlying the disease. Additionally, we evaluated the impact of continuous positive airway pressure (CPAP) treatment on the circulating metabolomic and lipidomic profile.

MATERIAL AND METHODS

Observational-prospective-longitudinal study including 206 consecutive subjects referred to the sleep unit. OSA was defined as an apnea-hypopnoea index ≥ 15 events/h after polysomnography (PSG). Patients treated with CPAP were followed-up for 6 months. Untargeted plasma metabolomic and lipidomic profiling was performed using liquid chromatography coulpled to massspectrometry.

RESULTS

A plasma profile composed of 33 metabolites (mainly glycerophospholipids and bile acids) was identified in OSA vs. non-OSA patients. This profile correlated with specific PSG measures of OSA severity related to sleep fragmentation and hypoxemia. Machine learning analyses disclosed a 4-metabolites-signature that provided an accuracy (95% CI) of 0.98 (0.95-0.99) for OSA detection. CPAP treatment was associated with changes in 5 plasma metabolites previously altered by OSA.

CONCLUSIONS

This analysis of the circulating metabolome and lipidome reveals a molecular fingerprint of OSA, which was modulated after effective CPAP treatment. Our results suggest blood-based biomarker candidates with potential application in the personalized management of OSA and suggest the activation of adaptive mechanisms in response to OSA-derived hypoxia.

Incremental health care utilization and expenditures for sleep disorders in the United States

STUDY OBJECTIVES

To determine the incremental increases in health care utilization and expenditures associated with sleep disorders.

METHODS

Adults with a diagnosis of a sleep disorder (International Classification of Diseases, 10th Revision, code G47.x) within the medical conditions file of the 2018 Medical Expenditure Panel Survey medical conditions file were identified. This dataset was then linked to the consolidated expenditures file and comparisons in health care utilization and expenditures were made between those with and without sleep disorders. Multivariate analyses, adjusted for demographics and comorbidities, were conducted for these comparisons.

RESULTS

Overall, 5.6% ± 0.2% of the study population had been diagnosed with a sleep disorder, representing approximately 13.6 ± 0.6 million adults in the United States. Those with sleep disorders were more likely to be non-Hispanic, White, and female, with a higher proportion with public insurance and higher Charlson Comorbidity Scores. Adults with sleep disorders were found to have increased utilization of office visits (16.3 ± 0.8 vs 8.7 ± 0.3, P < .001), emergency room visits (0.52 ± 0.03 vs 0.37 ± 0.02, P < .001), and prescriptions (39.7 ± 1.2 vs 21.9 ± 0.4, P < .001) vs those without sleep disorders. The additional incremental health care expenses for those with sleep disorders were increased in all examined measures: total health care expense ($6,975 ± $800, P < .001), total office-based expenditures ($1,694 ± $277, P < .001), total prescription expenditures ($2,574 ± $364, P < .001), and total self-expenditures for prescriptions ($195 ± $32, P < .001).

CONCLUSIONS

Sleep disorders are associated with significantly higher rates of health care utilization and expenditures. By using the conservative prevalence estimate found in this study, the overall incremental health care costs of sleep disorders in the United States represents approximately $94.9 billion.

CITATION

Huyett P, Bhattacharyya N. Incremental health care utilization and expenditures for sleep disorders in the United States. J Clin Sleep Med. 2021;17(10):1981-1986.

Brain mitochondrial dysfunction and driving simulator performance in untreated obstructive sleep apnea

It is challenging to determine which patients with obstructive sleep apnea (OSA) have impaired driving ability. Vulnerability to this neurobehavioral impairment may be explained by lower brain metabolites levels involved in mitochondrial metabolism. This study compared markers of brain energy metabolism in OSA patients identified as vulnerable vs resistant to driving impairment following extended wakefulness. 44 patients with moderate-severe OSA underwent 28hr extended wakefulness with three 90min driving simulation assessments. Using a two-step cluster analysis, objective driving data (steering deviation and crashes) from the 2nd driving assessment (22.5 h awake) was used to categorise patients into vulnerable (poor driving, n = 21) or resistant groups (good driving, n = 23). ¹ H magnetic resonance spectra were acquired at baseline using two scan sequences (short echo PRESS and longer echo-time asymmetric PRESS), focusing on key metabolites, creatine, glutamate, N-acetylaspartate (NAA) in the hippocampus, anterior cingulate cortex and left orbito-frontal cortex. Based on cluster analysis, the vulnerable group had impaired driving performance compared with the resistant group and had lower levels of creatine (PRESS p = ns, APRESS p = 0.039), glutamate, (PRESS p < 0.01, APRESS p < 0.01), NAA (PRESS p = 0.038, APRESS p = 0.035) exclusively in the left orbito-frontal cortex. Adjusted analysis, higher glutamate was associated with a 21% (PRESS) and 36% (APRESS) reduced risk of vulnerable classification. Brain mitochondrial bioenergetics in the frontal brain regions are impaired in OSA patients who are vulnerable to driving impairment following sleep loss. These findings provide a potential way to identify at risk OSA phenotype when assessing fitness to drive, but this requires confirmation in larger future studies.

The public health burden of obstructive sleep apnea

Obstructive sleep apnea (OSA) is the most common respiratory disorder of sleep. The vast majority (>80%) of adults with moderate to severe OSA remain undiagnosed. The economic costs associated with OSA are substantial for both the individual and society as a whole; expenses are likely to be underestimated given that the disease remains undiagnosed in such a large percentage of individuals. The economic burden of motor vehicle collisions related to OSA alone is significant; it is estimated that 810,000 collisions and 1400 fatalities from car crashes in the United States were attributable to sleep apnea in 2000. The many health consequences of OSA include daytime sleepiness, reduced quality of life, decreased learning skills, and importantly, neurocognitive impairments that include impaired episodic memory, executive function, attention and visuospatial cognitive functions. Untreated OSA leads to numerous medical problems such as cardiovascular diseases that can potentially increase healthcare utilization. Untreated patients with sleep apnea consume a disproportionate amount of healthcare resources, expenditures that decrease after treatment. The gold-standard management of OSA remains treatment with CPAP (Continuous Positive Airway Pressure), which is effective in eliminating sleep fragmentation and preserving nocturnal oxygenation, thereby improving daytime sleepiness and quality of life. However, its impacts in reversing neurocognitive function are still uncertain. A significant impediment to CPAP effectiveness is low adherence rates (ranges from 50% to 75%). It is commonly accepted that CPAP improves excessive drowsiness; hence meliorates attention, and accumulating data suggest that CPAP improves a variety of other outcomes such as the risk of motor vehicle crashes.

Primary care and sleep unit agreement in management decisions for sleep apnea: a prospective study in Spain

STUDY OBJECTIVES

Involvement of primary care teams in the care of patients with OSA is a focus of interest. The study objective was to compare diagnostic and therapeutic agreement between decisions taken by primary care professionals and sleep unit specialists.

METHODS

This was a prospective multicenter study conducted at primary care and specialized care centers in the urban area of Barcelona, Spain. Men and women aged 18-75 years who visited the participating primary care centers for any reason were recruited. Both primary care physicians and sleep specialists made a diagnostic and therapeutic decision with clinical data and results of a home sleep apnea test. All patients were finally assessed with respiratory polygraphy or polysomnography as a gold-standard test.

RESULTS

A total of 229 patients underwent a home sleep apnea test and were evaluated at the primary care centers and the sleep units. Diagnostic agreement using the same tools and excluding indeterminate decisions was 69.8% (Cohen's kappa = 0.64; 95% confidence interval, 0.56-0.72). Agreement for therapeutic decisions (PAP vs conservative treatment) was obtained in 82.5% of patients (Cohen's kappa = 0.62; 95% confidence interval, 0.51-0.73), increasing to 92.5% (Cohen's kappa = 0.49, 95% confidence interval, 0.40-0.58) when indeterminate options were excluded. As compared with the final therapeutic decisions made at the sleep unit with respiratory polygraphy/polysomnography, primary care physicians agreed regarding 83.3% (Cohen's kappa = 0.62; 95% confidence interval, 0.49-0.74) of patients.

CONCLUSIONS

Primary care professionals may assume an important role in the management of OSA in coordination with sleep centers, identifying patients who require specific treatment and should be referred to specialized care.

CLINICAL TRIAL REGISTRATION

Registry: ClinicalTrials.gov; Name: PASHOS Project: Advanced Platform for Sleep Apnea Syndrome Assessment; URL: https://clinicaltrials.gov/ct2/show/NCT02591979; Identifier: NCT02591979.

Obstructive sleep apnea, sleep duration and chronic kidney disease in patients with coronary artery disease

BACKGROUND

Limited evidence is available addressing the potential role of sleep disorders on renal function. Here, we aimed to explore the associations of obstructive sleep apnea (OSA) and sleep duration (SD) with renal function in subjects with high cardiovascular risk.

METHODS

Consecutive subjects with coronary artery disease (CAD) underwent clinical evaluation, sleep study to define OSA and one-week wrist actigraphy to objectively measure SD. OSA was defined by an apnea-hypopnea index (AHI) of ≥15 events/hour. The estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI equation. We analyzed the associations of OSA and SD with continuous eGFR values and according to the presence of CKD (eGFR<60 mL/min/1.73 m²) after adjusting for multiple confounding factors.

RESULTS

We studied 242 subjects (62.8% men). The frequency of OSA was 55.4% and the median SD was 412.8 (363.4-457.25) min. There was no difference in the eGFRs between participants with and without OSA (69.3 ± 19.1 vs. 74.6 ± 19.3 mL/min/1.73 m², p = 0.72) and the rate of eGFR <60 mL/min/1.73 m² (34.3% vs. 25.9%; p = 0.21). Similarly, we did not find differences in patients in eGFR for those with SD ≥ 6 h versus SD < 6 h (72.5 ± 20.3 vs. 71.4 ± 19.1 mL/min/1.73 m², p = 0.72). In the linear regression analysis, AHI was independently associated with an eGFR<60 mL/min/1.73 m² in the unadjusted model [-0.15 (-0.27 to -0.04)], (P = 0.01), but not in the adjusted models. Analyses of continuous SD or the stratification in SD ≥ 6 h or <6 h also revealed neutral results on eGFR.

CONCLUSION

OSA severity and SD were not independently associated with CKD in subjects with CAD.

Effect of exercise training on improving sleep disturbances: a systematic review and meta-analysis of randomized control trials

OBJECTIVES

Exercise training has beneficial effects on various aspects of health. This study aimed to investigate the effect of exercise training on the improvement of sleep disturbances using systematic review and meta-analysis of randomized control trials.

METHOD

Four indexes of scientific information including PubMed, Web of Science, Scopus, and the Cochrane library were selected and all manuscripts of these sources were searched in English until January 2021. The studies were screened and finally, the studies were entered into meta-analysis and the Standardized Mean Difference (SMD) was calculated, and the analyzes were performed based on the random effects method. Publication bias and heterogeneity were examined in all analyzes.

RESULT

A total of 32 studies were included in the meta-analysis. Meta-analysis showed that exercise training is effective in improving sleep quality (SMD = -0.85 and confidence interval (CI) was -1.16-0.54; P < 0.001). Exercise training improving insomnia (SMD = -0.87 and CI was -1.68-0.06; P = 0.036). Exercise training improves sleepiness (SMD = -0.38 and CI was -0.68-0.07; P = 0.016), obstructive sleep apnea (SMD = -0.40 and CI was -0.67-0.14; P = 0.003) and restless legs syndrome (SMD = -1.02 and CI was -1.56-0.49; P < 0.001).

DISCUSSION

Exercise training has beneficial effects on a variety of sleep disturbances and therefore it can be said that providing the necessary conditions for exercise training can play a major role in promoting health, especially since this type of intervention is a non-pharmacological intervention.

The Social And Economic Cost Of Sleep Disorders

STUDY OBJECTIVES

To estimate economic cost of common sleep disorders in Australia for 2019-2020.

METHODS

Costs were estimated for obstructive sleep apnea (OSA), insomnia, and restless legs syndrome (RLS) using prevalence, financial, and nonfinancial data from national databases. These included: (1) financial costs associated with health care, informal care, productivity losses, non-medical accident costs, deadweight loss from taxation/welfare inefficiencies; and (2) nonfinancial costs associated with loss of well-being. They were expressed in US dollars ($).

RESULTS

Estimated overall cost of sleep disorders in Australia in 2019-2020 (population: 25.5 million) was $35.4 billion (OSA $13.1 billion; insomnia $13.3 billion, RLS $9.0 billion). Of this, the financial cost component was $10.0 billion, comprised of: health system costs $0.7 billion; productivity losses $7.7 billion; informal care $0.2 billion; other, mainly non-medical accident costs, $0.4 billion; and deadweight losses $1.0 billion. For moderate to severe OSA syndrome, insomnia unrelated to other conditions and RLS, financial costs represented $16,717, $21,982, and $16,624 per adult with the condition for the year, respectively. The nonfinancial cost was $25.4 billion.

CONCLUSIONS

The economic costs associated with sleep disorders are substantial. The financial component of $10.0 billion is equivalent to 0.73% of Australian gross domestic product. The nonfinancial cost of $25.4 billion represents 3.2% of total Australian burden of disease for the year. Health system costs of these disorders are low relative to those associated with their consequences, suggesting greater expenditure on detection, treatment and prevention is warranted.

A method based on cardiopulmonary coupling analysis for sleep quality assessment with FPGA implementation

The relevance of sleep quality examination for clinical diagnosis is increasing with the discovery of new relationships with several diseases and the overall wellness. This assessment is commonly performed by conducting interviews with the subjects, evaluating the self-report and psychological variables. However, this approach has a major constraint since the subject is a poor self-observer of sleep behaviors. To address this issue, a method based on the examination of a physiological signal was developed. Specifically, the single-lead electrocardiogram signal was examined to estimate the cardiopulmonary coupling between the electrocardiogram derived respiration signal and the normal-to-normal sinus interbeat interval series. A one dimensional array was created from the coupling signal and was fed to a convolutional neural network to estimate the sleep quality. The age-related cyclic alternating pattern rate percentages in healthy subjects was considered as the classification reference. An accuracy of 91 % was attained by the developed model, with an area under the receiver operating characteristic curve of 97 %. The performance is in the upper range of the reported performance by the works presented in the state of the art, advocating the relevance of the proposed method. The model was implemented in a small field programmable gate array board. Hence, a home monitoring device was created, composed of a processing unit, a sensing module and a display unit. The device is resilient, easy to self-assemble and operate, and can conceivably be employed for clinical analysis.

Role of physical activity and fitness on sleep in sedentary middle-aged adults: the FIT-AGEING study

The association of physical activity and fitness with sleep still remains unclear since there is a lack of studies in this field of research using objective measurements of these variables. This study aimed to investigate the association of objectively-measured sedentariness, physical activity levels, and physical fitness with sleep quantity and quality in sedentary middle-aged adults. A total of 74 volunteers (52.7% women; aged 53.7 ± 5.1) were recruited for the present study. Cardiorespiratory fitness was measured through a maximal treadmill test, and muscular strength by extension and flexion peak torque, and by the hand grip test. Physical activity and objective sleep parameters were determined through accelerometry, and subjective sleep by the Pittsburgh Sleep Quality Index (PSQI). Reduced levels of sedentariness, greater VO_2max, and greater muscular strength were positively related to improved objective sleep quantity and quality (all P ≤ 0.05). Furthermore, higher levels of overall physical activity, VO_2max, and muscular strength were related to better subjective sleep quantity and quality (all P ≤ 0.05). Reduced sedentariness and increased physical activity and fitness may be a potential prevention and/or treatment pathway to reduce sleep disturbances and, in general, to improve patients physical and psychological health for a successful aging process.

Machine and Deep Learning in Molecular and Genetic Aspects of Sleep Research

Epidemiological sleep research strives to identify the interactions and causal mechanisms by which sleep affects human health, and to design intervention strategies for improving sleep throughout the lifespan. These goals can be advanced by further focusing on the environmental and genetic etiology of sleep disorders, and by development of risk stratification algorithms, to identify people who are at risk or are affected by, sleep disorders. These studies rely on comprehensive sleep-related data which often contains complex multi-dimensional physiological and molecular measurements across multiple timepoints. Thus, sleep research is well-suited for the application of computational approaches that can handle high-dimensional data. Here, we survey recent advances in machine and deep learning together with the availability of large human cohort studies with sleep data that can jointly drive the next breakthroughs in the sleep-research field. We describe sleep-related data types and datasets, and present some of the tasks in the field that can be targets for algorithmic approaches, as well as the challenges and opportunities in pursuing them.

Development of a support vector machine learning and smart phone Internet of Things-based architecture for real-time sleep apnea diagnosis

Background

The breathing disorder obstructive sleep apnea syndrome (OSAS) only occurs while asleep. While polysomnography (PSG) represents the premiere standard for diagnosing OSAS, it is quite costly, complicated to use, and carries a significant delay between testing and diagnosis.

Methods

This work describes a novel architecture and algorithm designed to efficiently diagnose OSAS via the use of smart phones. In our algorithm, features are extracted from the data, specifically blood oxygen saturation as represented by SpO2. These features are used by a support vector machine (SVM) based strategy to create a classification model. The resultant SVM classification model can then be employed to diagnose OSAS. To allow remote diagnosis, we have combined a simple monitoring system with our algorithm. The system allows physiological data to be obtained from a smart phone, the data to be uploaded to the cloud for processing, and finally population of a diagnostic report sent back to the smart phone in real-time.

Results

Our initial evaluation of this algorithm utilizing actual patient data finds its sensitivity, accuracy, and specificity to be 87.6%, 90.2%, and 94.1%, respectively.

Discussion

Our architecture can monitor human physiological readings in real time and give early warning of abnormal physiological parameters. Moreover, after our evaluation, we find 5G technology offers higher bandwidth with lower delays ensuring more effective monitoring. In addition, we evaluate our algorithm utilizing real-world data; the proposed approach has high accuracy, sensitivity, and specific, demonstrating that our approach is very promising.

Conclusions

Experimental results on the apnea data in University College Dublin (UCD) Database have proven the efficiency and effectiveness of our methodology. This work is a pilot project and still under development. There is no clinical validation and no support. In addition, the Internet of Things (IoT) architecture enables real-time monitoring of human physiological parameters, combined with diagnostic algorithms to provide early warning of abnormal data.

Greedy based convolutional neural network optimization for detecting apnea

BACKGROUND AND OBJECTIVE

Sleep apnea is a common sleep disorder, usually diagnosed using an expensive, highly specialized, and inconvenient test called polysomnography. A single SpO2 sensor based on an automated classification system can be developed to simplify the apnea detection. The main objective of this work is to develop a classifier based on a convolution neural network with the capability of detecting apnea events from one dimensional SpO2 signal. However, to find an optimum convolution neural network structure is a daunting task is usually done by a trial-and-error method. To solve this problem, a method is proposed to save time and simplify the process of searching for an optimum convolution neural network structure.

METHODS

Greedy based optimization is proposed to search for an optimized convolution neural network structure. Three different variants of greedy based optimization are proposed: the topology transfer, the weighted-topology transfer with rough estimation, and the weighted-topology transfer with fine tuning. The subject independent and the cross-database test are performed for the analysis.

RESULTS

Considering the balance between the execution time and the performance, the weighted-topology transfer with rough estimation is the best. An accuracy of 88.49% for the HuGCDN2008 database and 95.14% for the Apnea-ECG database are obtained for apnea events detection per minute. Regarding the apnea patient detection, also referred to as global classification, an accuracy of 95.71% is achieved for the HuGCDN2008 database, and 100% is achieved for the AED database without removing any subjects from both databases.

CONCLUSIONS

The proposed one-dimensional convolution neural network performs better in a similar situation than those presented in the literature. The greedy based methods, mainly the weighted-topology transfer with rough estimation, is an alternative method to extensive trial and error method.

Effect of an Inflatable Air Mattress with Variable Rigidity on Sleep Quality

Several studies, wherein the structure or rigidity of a mattress was varied, have been conducted to improve sleep quality. These studies investigated the effect of variation in the surface characteristics of mattresses on sleep quality. The present study developed a mattress whose rigidity can be varied by controlling the amount of air in its air cells. To investigate the effect of the variable rigidity of the air mattress on sleep quality, participants (Male, Age: 23.9 ± 2.74, BMI: 23.3 ± 1.60) were instructed to sleep on the air mattress under different conditions, and their sleep quality was subjectively and objectively investigated. Subjectively, sleep quality is assessed based on the participants’ evaluations of the depth and length of their sleep. Objectively, sleep is estimated using the sleep stage information obtained by analysing the movements and brain waves of the participants during their sleep. A subjective assessment of the sleep quality demonstrates that the participants’ sleep was worse with the adjustment of the air mattress than that without; however, the objective sleep quality results demonstrates an improvement in the sleep quality when the rigidity of the air mattress is varied based on the participant’s preference. This paper proposes a design for mattresses that can result in more efficient sleep than that provided by traditional mattresses.

Cognitive profiles in obstructive sleep apnea: a cluster analysis in sleep clinic and community samples

STUDY OBJECTIVES

Although cognitive dysfunction is a recognized consequence of untreated obstructive sleep apnea (OSA), the deficit pattern is heterogeneous. Understanding this heterogeneity may identify those at risk of cognitive deficits and guide intervention strategies. To facilitate understanding, we examined whether distinct profiles of neuropsychological performance were present in OSA and, if so, how they are related to other OSA features.

METHODS

We studied sleep clinic (n = 121) and community (n = 398) samples with moderate-severe OSA (apnea-hypopnea index ≥ 15 events/h). Attention and memory were assessed using the Cognitive Drug Research system. Sleep was assessed using polysomnography in the clinic sample and dual channel (flow, oximetry) portable monitoring in the community sample. Latent profile analysis was used to determine structure of cognitive clusters. Discriminant function analysis was used to examine associations between nocturnal and diurnal features of OSA and profile membership.

RESULTS

Both samples were best characterized by a 3-profile solution: (1) strong thinkers (performed well across most domains and showed greater cognitive reserve); (2) inattentive fast thinkers (strong processing speed but poor ability to maintain attention); and (3) accurate slow thinkers (strengths in maintaining attention but poor processing speed). Profile membership was associated with mean overnight oxygen saturation and cognitive reserve in the clinic sample and the presence of cardiovascular disease and/or diabetes in the community sample.

CONCLUSIONS

These findings help explain the diversity of outcomes in previous studies of cognitive dysfunction in OSA by demonstrating that individual differences in cognitive reserve, nocturnal oxygen saturation, and comorbidities affect how cognition is impacted by OSA.

Poor sleep quality and associated factors among prisoners of the Diredawa correctional facility in eastern Ethiopia

BACKGROUND

Impaired sleep quality affects judgment, psychomotor skills, memory, decision-making, concentration, and attention. It might also contribute to the development of new physical health problems, as well as exacerbating already existing physical problems. Despite this, there is a scarcity of research done in Africa including Ethiopia that addressed this issue. So this study assessed the quality of sleep and related factors among prisoners of the Diredawa correctional facility, Diredawa, eastern Ethiopia.

METHODS

A cross-sectional study was done using a simple random sampling technique to recruit 421 participants from May 21 to June 21; 2017. A semi-structured questionnaire, Pittsburgh Sleeps Quality Index (PSFIG), Patient Health Questionnaire-9 (PQ-9), and Sleep Hygiene Index (SHI) were used to assess participants' socio-demographic data, sleep quality, depression, and sleep hygiene, respectively. The collected data were entered into EPA-data 3.1 and analyzed by using SPAS-20. Descriptive and analytical statistics were used. Bivariate and multivariable logistic regression with odds ratio and 95% CI were employed. The statistical significance was declared at p value < 0.05.

RESULTS

A total of 421 out of 423 prisoners were interviewed, resulting in a response rate of 99.5%. The prevalence of poor sleep quality was 227 (53.9%) with 95% CI (49.2, 58.7). Having co-morbidity of depression (adjusted odds ratio; OAR = 3.47, 95% CI 1.38,8.76), lifetime use of cigarette (OAR = 2.16, 95% CI 1.21,5.58), marijuana and hashish (OAR = 5.02, 95% CI 1.63,15.46), current use of coffee (OAR = 2.75, CI 1.37, 7.05), poor sleep hygiene (OAR = 3.19, CI 1.32,7.69), committing assault crime (OAR = 4.12, CI 1.29,10.63) and crime of rape (OAR = 5.57, CI (1.45, 13.89) were the associated factors for poor sleep quality in this study.

CONCLUSION

More than half of the participants (53.9%) have poor sleep quality. Depression, lifetime use of cigarettes, using cannabis and hashish, current use of coffee, poor sleep hygiene, and crime types were the associated factors that should be taken into consideration and evaluated early to minimize poor sleep quality.

Brief digital sleep questionnaire powered by machine learning prediction models identifies common sleep disorders

INTRODUCTION

We developed and validated an abbreviated Digital Sleep Questionnaire (DSQ) to identify common societal sleep disturbances including insomnia, delayed sleep phase syndrome (DSPS), insufficient sleep syndrome (ISS), and risk for obstructive sleep apnea (OSA).

METHODS

The DSQ was administered to 3799 community volunteers, of which 2113 were eligible and consented to the study. Of those, 247 were interviewed by expert sleep physicians, who diagnosed ≤2 sleep disorders. Machine Learning (ML) trained and validated separate models for each diagnosis. Regularized linear models generated 15-200 features to optimize diagnostic prediction. Models were trained with five-fold cross-validation (repeated five times), followed by robust validation testing. ElasticNet models were used to classify true positives and negatives; bootstrapping optimized probability thresholds to generate sensitivities, specificities, accuracies, and area under the receiver operating curve (AUC).

RESULTS

Compared to reference subgroups, physician-diagnosed sleep disorders were marked by DSQ evidence of sleeplessness (insomnia, DSPS, OSA), sleep debt (DSPS, ISS), airway obstruction during sleep (OSA), blunted circadian variability in alertness (DSPS), sleepiness (DSPS and ISS), increased alertness (insomnia) and global impairment in sleep-related quality of life (all sleep disorders). ElasticNet models validated each diagnosis with high sensitivity (80-83%), acceptable specificity (63-69%), high AUC (0.80-0.85) and good accuracy (agreement with physician diagnoses, 68-73%).

DISCUSSION

A brief DSQ readily engaged and efficiently screened a large population for common sleep disorders. Powered by ML, the DSQ can accurately classify sleep disturbances, demonstrating the potential for improving the sleep, health, productivity and safety of populations.

Global burden of sleep-disordered breathing and its implications

One-seventh of the world's adult population, or approximately one billion people, are estimated to have OSA. Over the past four decades, obesity, the main risk factor for OSA, has risen in striking proportion worldwide. In the past 5 years, the WHO estimates global obesity to affect almost two billion adults. A second major risk factor for OSA is advanced age. As the prevalence of the ageing population and obesity increases, the vulnerability towards having OSA increases. In addition to these traditional OSA risk factors, studies of the global population reveal select contributing features and phenotypes, including extreme phenotypes and symptom clusters that deserve further examination. Untreated OSA is associated with significant comorbidities and mortality. These represent a tremendous threat to the individual and global health. Beyond the personal toll, the economic costs of OSA are far-reaching, affecting the individual, family and society directly and indirectly, in terms of productivity and public safety. A better understanding of the pathophysiology, individual and ethnic similarities and differences is needed to better facilitate management of this chronic disease. In some countries, measures of the OSA disease burden are sparse. As the global burden of OSA and its associated comorbidities are projected to further increase, the infrastructure to diagnose and manage OSA will need to adapt. The use of novel approaches (electronic health records and artificial intelligence) to stratify risk, diagnose and affect treatment are necessary. Together, a unified multi-disciplinary, multi-organizational, global approach will be needed to manage this disease.

Body Composition Impact on Sleep in Young Adults: The Mediating Role of Sedentariness, Physical Activity, and Diet

Obesity and sleep disturbances are both related to endocrine and metabolic alterations, cardiovascular disease, and impaired daytime functioning and mood. However, the bidirectional relationship between these conditions and the underlying mechanisms still remain unclear. This study aimed to investigate the potential association of anthropometric and body composition parameters with sleep in young adults, considering the mediating role of sedentariness, physical activity, and diet. A total of 187 adults aged 18–25 (35.29% men) participated in the study. Body mass index (BMI), waist–hip ratio, and waist–height ratio were calculated, and a dual-energy X-ray absorptiometry scanner was used to assess body composition. Sedentary time and physical activity, as well as sleep duration and quality, were objectively and subjectively measured using accelerometry and the Pittsburgh Sleep Quality Index. An inverse association was found between BMI and total sleep time (β = −0.165, p = 0.029). Waist–hip ratio and lean mass index were also negatively associated with total sleep time (β = −0.222, p = 0.007, and β = −0.219, p = 0.004) and sleep efficiency (β = −0.174, p = 0.037, and β = −0.188, p = 0.013). Sedentary time moderated by sex explained the association of BMI with total sleep time such that a high BMI was related to higher sedentariness in men which, in turn, was significantly associated with shorter sleep duration. Sedentary time is, therefore, a link/risk factor mediating the association of high BMI with short sleep duration in healthy young men.

Facial Emotion Recognition and Executive Functions in Insomnia Disorder: An Exploratory Study

BACKGROUND

Clinical and experimental findings suggest that insomnia is associated with changes in emotional processing and impairments in cognitive functioning. In the present study, we investigate the relationship between facial emotion recognition and executive functioning among individuals with insomnia as well as healthy controls.

METHOD

A total of 11 individuals (mean age 31.3 ± 9.4) diagnosed with insomnia disorder and 15 control participants (mean age 24.8 ± 4.6) took part in the study. Participants responded to a facial emotion recognition task which presented them with static and dynamic stimuli, and were evaluated with regard to cognition, sleep, and mood.

RESULTS

Compared to controls, we found that participants with insomnia performed worse in the recognition of the facial emotion of fear (p = 0.001; η p 2 = 0.549; β = 0.999) and had lower scores in tests of verbal comprehension and perceptual organization (104.00 vs. 115.00, U = 135.5; p = 0.004; Cohen's, 2013 d = 1.281). We also found a relationship between facial emotion recognition and performance in cognitive tests, such as those related to perceptual organization, cognitive flexibility, and working memory.

CONCLUSION

Results suggest that participants with insomnia may present some impairment in executive functions as well as in the recognition of facial emotions with negative valences (fear and sadness).

Sleep disordered breathing (SDB) in neonates and implications for its long-term impact

Sleep-disordered breathing (SDB) is a significant cause of morbidity in neonates and young infants. SDB occurs more commonly in preterm infants and in neonates with underlying syndromes. Recent evidence shows that infants with obstructive sleep apnoea (OSA) or SDB have greater health care resource utilization, including longer hospital stay. Management of SDB includes non-invasive ventilation or surgical interventions tailored to the patient. Screening high risk newborns should allow for early diagnosis and timely therapeutic intervention for this population. However, the thresholds for diagnosing SDB and for guiding and implementing treatment in neonates remain unclear. A collective effort is required to standardize the practice worldwide. This article will discuss neonatal sleep physiology and characteristics of neonatal sleep, with an emphasis on the epidemiology and diagnosis of SDB in neonates and its implications for long term outcomes.

The future of sleep health: a data-driven revolution in sleep science and medicine

In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human-computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-of-the-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.

An Oximetry Based Wireless Device for Sleep Apnea Detection

Sleep related disorders can severely disturb the quality of sleep. Among these disorders, obstructive sleep apnea (OSA) is highly prevalent and commonly undiagnosed. Polysomnography is considered to be the gold standard exam for OSA diagnosis. Even though this multi-parametric test provides highly accurate results, it is time consuming, labor-intensive, and expensive. A non-invasive and easy to self-assemble home monitoring device was developed to address these issues. The device can perform the OSA diagnosis at the patient’s home and a specialized technician is not required to supervise the process. An automatic scoring algorithm was developed to examine the blood oxygen saturation signal for a minute-by-minute OSA assessment. It was performed by analyzing statistical and frequency-based features that were fed to a classifier. Afterward, the ratio of the number of minutes classified as OSA to the time in bed in minutes was compared with a threshold for the global (subject-based) OSA diagnosis. The average accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve for the minute-by-minute assessment were, respectively, 88%, 80%, 91%, and 0.86. The subject-based accuracy was 95%. The performance is in the same range as the best state of the art methods for the models based only on the blood oxygen saturation analysis. Therefore, the developed model has the potential to be employed in clinical analysis.

Clinician and patient barriers to the recognition of insomnia in family practice: a narrative summary of reported literature analysed using the theoretical domains framework

BACKGROUND

Insomnia is a common sleep complaint, with 10% of adults in the general population experiencing insomnia disorder, defined as lasting longer than three months in DSM-5. Up to 50% of patients attending family practice experience insomnia, however despite this, symptoms of insomnia are not often screened for, or discussed within this setting. We aimed to examine barriers to the assessment and diagnosis of insomnia in family practice from both the clinician and patient perspective.

METHODS

The present article identified research that has examined barriers to assessing insomnia from the clinician's and the client's perspectives following MEDLINE and Google Scholar searches, and then classified these barriers using the theoretical domains framework.

RESULTS

The most common barriers from the clinician's perspective were related to Knowledge, Skills, and the Environmental Context. From the patient perspective, barriers identified included their Beliefs about the consequences of Insomnia, Social Influences, and Behavioural Regulation of Symptoms.

CONCLUSIONS

Utilising this theoretical framework, we discuss options for bridging the gap between the identification and subsequent management of insomnia within the family practice setting. To assist clinicians and those in community health care to overcome the Knowledge and Skills barriers identified, this article provides existing relevant clinical criteria that can be utilised to make a valid diagnosis of insomnia.

Restless legs syndrome and periodic limb movements of sleep

Restless legs syndrome describes a curious need to stretch and move in response to an unpleasant feeling in the legs. For most people who have this, as they are drifting off to sleep there is a twitch of the toe, foot and ankle followed by another and then another. The limb movements are strikingly periodic and are described as such by frustrated bed partners. The pathophysiology remains debated but is likely to involve dopaminergic dysfunction and ascending arousal systems. Despite a prevalence of 5-10% in the general population, the variable nature of symptoms and difficulty describing the dysaesthesia often leads to delay in treatment. This article outlines the diagnostic criteria, medication and comorbidities that modify symptoms, the impact of restless legs syndrome on health, and symptomatic management. Several licensed therapies are effective but the dopamine agonists in particular can cause challenging side effects and paradoxically worsen symptoms over time. Case histories are reviewed to guide physicians.

A Review of Current Tools Used for Evaluating the Severity of Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is a common and heterogeneous disease characterized by episodic collapse within the upper airways, which leads to reduced ventilation and adverse consequences, including hypoxia, hypercapnia, sleep fragmentation, and long-term effects such as cardiovascular comorbidities. The clinical diagnosis of OSA and its severity classification are often determined based on the apnea-hypopnea index (AHI), defining the number of apneic and hypopnea events per hour of sleep. However, the limitations of the AHI to assess disease severity have necessitated the exploration of other metrics for additional information to reflect the complexity of OSA. Novel parameters such as the hypoxic burden have the potential to better capture the main features of OSA by maximizing the information available from the polysomnogram. These emerging measures have described multidimensional qualities of sleep-disordered breathing events and breathing irregularity and will ultimately result in better management of OSA.