Nature and Science of Sleep最新文献

{"title":"Diagnosing OSA and Insomnia at Home Based Only on an Actigraphy Total Sleep Time and RIP Belts an Algorithm \"Nox Body Sleep™\".","authors":"Damien Leger, Maxime Elbaz","doi":"10.2147/NSS.S431650","DOIUrl":"10.2147/NSS.S431650","url":null,"abstract":"<p><strong>Purpose: </strong>The COVID-19 pandemic has influenced clinical sleep protocols with stricter hospital disinfection requirements. Facing these new rules, we tested if a new artificial intelligence (AI) algorithm: The Nox BodySleep™ (NBS) developed without airflow signals for the analysis of sleep might assess pertinently sleep in patients with Obstructive Sleep Apnea (OSA) and chronic insomnia (CI) as a control group, compared to polysomnography (PSG) manual scoring.</p><p><strong>Patients-methods: </strong>NBS is a recurrent neural network model that estimates Wake, NREM, and REM states, given features extracted from activity and respiratory inductance plethysmography (RIP) belt signals (Nox A1 PSG). Sleep states from 139 PSG studies (CI N = 72; OSA N = 67) were analyzed by NBS and compared to manually scored PSG using positive percentage agreement, negative percentage agreement, and overall agreement metrics. Similarly, we compared common sleep parameters and OSA severity using sleep states estimated by NBS for each recording and compared to manual scoring using Bland-Altman analysis and intra-class correlation coefficient.</p><p><strong>Results: </strong>For 127,170 sleep epochs, an overall agreement of 83% was reached for Wake, NREM and REM states (92% for REM states in CI patients) between NBS and manually scored PSG. Overall agreement for estimating OSA severity was 100% for moderate-severe OSA and 91% for minimal OSA. The absolute errors of the apnea-hypopnea index (AHI) and total sleep time (TST) were significantly lower for the NBS compared to no scoring of sleep. The intra-class correlation was higher for AHI and significantly higher for TST using the NBS compared to no scoring of sleep.</p><p><strong>Conclusion: </strong>NBS gives sleep states, parameters and AHI with a good positive and negative percentage agreement, compared with manually scored PSG.</p>","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11194000/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141443081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Latent Profiles and Transitions of Bedtime Procrastination Among Chinese College Students: The Predictive Roles of Anxiety, Depression, Problematic Smartphone Use and Self-Control.","authors":"Lan Hong, Huihui Xu, Jiaqi Zheng, Xiujian Lin, Lijun Wang, Chengjia Zhao, Xiaolian Tu, Jingjing Zhang, Ke Zhao, Guohua Zhang","doi":"10.2147/NSS.S462055","DOIUrl":"10.2147/NSS.S462055","url":null,"abstract":"<p><strong>Background: </strong>Bedtime procrastination (BP) has become an important factor affecting individual well-being. This study aimed to assess the stability and changes in BP and examine risk and protective factors.</p><p><strong>Methods: </strong>The study recruited 1423 respondents. Latent profile analysis was used to identify subgroups of BP and latent transition analysis to determine transition probabilities for each subgroup. Logistic regression examined associations between identified classes and related factors.</p><p><strong>Results: </strong>Three subgroups of BP were identified. In terms of stability and changes, the moderate bedtime procrastination group showed the highest stability (66%), followed by the severe bedtime procrastination group (62.4%), and the mild bedtime procrastination group had a 52% probability of switching to moderate bedtime procrastination. In terms of influencing factors, more problematic phone use (PSU) (OR: 1.08; 95% CI = 1.05-1.12), more depression (OR: 1.17; 95% CI = 1.06-1.29) and anxiety (OR: 1.16; 95% CI = 1.05-1.28) are all factors that aggravate the transition from mild to moderate sleep procrastination. Similarly, PSU (OR: 1.15; 95% CI = 1.12-1.19), anxiety (OR: 1.10; 95% CI = 1.06-1.14), and depression (OR: 1.10; 95% CI = 1.06-1.14) increased the risk of severe bedtime procrastination. Self-control emerged as a protective factor against BP.</p><p><strong>Conclusion: </strong>This study identified three subgroups of BP at two time points and the rule of transition for each subgroup. Our findings indicate that BP were relatively stable, with some changes over time. The results also highlight the important function that PSU, depression, anxiety, and self-control can play in preventing and intervening in BP.</p>","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11192292/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141443083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Difficulty Falling Asleep is Associated with Poorer Therapeutic Outcomes in Unilateral Hypoglossal Nerve Stimulation.","authors":"Johannes Pordzik, Katja Petrowski, Katharina Ludwig, Christopher Seifen, Christoph Matthias, Haralampos Gouveris","doi":"10.2147/NSS.S459690","DOIUrl":"10.2147/NSS.S459690","url":null,"abstract":"<p><strong>Purpose: </strong>The coexistence of insomnia and obstructive sleep apnea (OSA) is very prevalent. Hypoglossal nerve stimulation (HGNS) is an established second-line therapy for patients suffering OSA. Studies investigating the effect of the different aspects of insomnia on the therapeutic outcome are largely missing. Therefore, this study aimed to understand the impact of the different aspects of insomnia on the therapeutic outcome under HGNS therapy in clinical routine.</p><p><strong>Patients and methods: </strong>This is a retrospective study including 30 consecutive patients aged 55.40 ± 8.83 years (8 female; 22 male) undergoing an HGNS implantation in our tertiary medical center between 2020 and 2023. All patients underwent preoperative polysomnography (PSG) according to AASM. First follow-up PSG was performed 95.40 ± 39.44 days after activation (30 patients) and second follow-up PSG was performed 409.89 ± 122.52 days after activation (18 patients). Among others, the following PSG-related parameters were evaluated: apnea-hypopnea index (n/h) (AHI) and oxygen desaturation index (n/h) (ODI). Insomnia was assessed by the insomnia severity index (ISI) questionnaire. Preoperatively, all patients included filled out each ISI item. Spearman's-rho correlation coefficient was calculated for correlations.</p><p><strong>Results: </strong>Preoperative score of ISI item 1 (difficulty falling asleep) was 1.93 ± 1.34 and preoperative cumulative ISI score (item1-7) was 18.67 ± 5.32. Preoperative AHI was 40.61 ± 12.02 (n/h) and preoperative ODI was 38.72 ± 14.28 (n/h). In the second follow-up, the mean difference in AHI was ∆ 10.47 ± 15.38 (n/h) and the mean difference in ODI was ∆ 8.17 ± 15.67 (n/h). Strong significant correlations were observed between ISI item 1 (difficulty falling asleep) and both ∆ AHI (r: -0.65, <i>p</i>=0.004) and ∆ ODI (r: -0.7; <i>p</i>=0.001) in the second follow-up.</p><p><strong>Conclusion: </strong>Difficulty falling asleep may hence negatively influence HGNS therapeutic outcome. Insomnia-related symptoms should be considered in the preoperative patient evaluation for HGNS.</p>","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11192637/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141443082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Obstructive Sleep Apnea as a Key Contributor to Mental Stress-Induced Myocardial Ischemia in Female Angina Patients with No Obstructive Coronary Artery Disease.","authors":"Fengyao Liu, Haochen Wang, Bingqing Bai, Han Yin, Yuting Liu, Yu Wang, Quanjun Liu, Shuxia Wang, Huan Ma, Qingshan Geng","doi":"10.2147/NSS.S445219","DOIUrl":"10.2147/NSS.S445219","url":null,"abstract":"<p><strong>Purpose: </strong>Mental stress induced myocardial ischemia (MSIMI) is regarded as the primary cause of the angina with no obstructive coronary artery disease (ANOCA). Obstructive sleep apnea (OSA) is autonomously linked to obstructive coronary heart disease, hypertension, and sudden cardiac death. Similar to the impact of psychological stress on the cardiovascular system, individuals with OSA experience periodic nocturnal hypoxia, resulting in the activation of systemic inflammation, oxidative stress, endothelial dysfunction, and sympathetic hyperactivity. The contribution of OSA to MSIMI in ANOCA patients is unclear. To explore the prevalence of OSA in ANOCA patients and the correlation between OSA and MSIMI, a prospective cohort of female ANOCA patients was recruited.</p><p><strong>Patients and methods: </strong>We recruited female patients aged 18 to 75 years old with ANOCA and evaluated MSIMI using positron emission tomography-computed tomography. Subsequently, Level III portable monitors was performed to compare the relationship between OSA and MSIMI.</p><p><strong>Results: </strong>There is higher REI (7.8 vs 2.6, <i>P</i>=0.019), ODI (4.7 vs 9.2, <i>P</i>=0.028) and percentage of OSA (67.74% vs 33.33%, <i>P</i>=0.004) in MSIMI patients. The patients diagnosed with OSA demonstrated higher myocardial perfusion imaging scores (SSS: 1.5 vs 3, <i>P</i> = 0.005, SDS: 1 vs 3, P = 0.007). Adjusted covariates, the risk of developing MSIMI remained 3.6 times higher in OSA patients (β=1.226, OR = 3.408 (1.200-9.681), <i>P</i> = 0.021).</p><p><strong>Conclusion: </strong>Patients with MSIMI exhibit a greater prevalence of OSA. Furthermore, the myocardial blood flow perfusion in patients with OSA is reduced during mental stress.</p>","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11192149/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141443084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of COVID-19 on Sleep Services Use and Its Recovery.","authors":"Amin Ramezani, Amir Sharafkhaneh, Ahmed S BaHammam, Samuel T Kuna, Javad Razjouyan","doi":"10.2147/NSS.S456214","DOIUrl":"10.2147/NSS.S456214","url":null,"abstract":"<p><strong>Purpose: </strong>The COVID-19 pandemic affected the utilization of various healthcare services differentially. Sleep testing services utilization (STU), including Home Sleep Apnea Testing (HSAT) and Polysomnography (PSG), were uniquely affected. We assessed the effects of the pandemic on STU and its recovery using the Veterans Health Administration (VHA) data.</p><p><strong>Patients and methods: </strong>A retrospective cohort study from the VHA between 01/2019 and 10/2023 of veterans with age ≥ 50. We extracted STU data using Current Procedural Terminology codes for five periods based on STU and vaccination status: pre-pandemic (Pre-Pan), pandemic sleep test moratorium (Pan-Mor), and pandemic pre-vaccination (Pan-Pre-Vax), vaccination (Pan-Vax), and postvaccination (Pan-Post-Vax). We compared STU between intervals (Pre-Pan as the reference).</p><p><strong>Results: </strong>Among 261,371 veterans (63.7±9.6 years, BMI 31.9±6.0 kg/m², 80% male), PSG utilization decreased significantly during Pan-Mor (-56%), Pan-Pre-Vax (-61%), Pan-Vax (-42%), and Pan-Post-Vax (-36%) periods all compared to Pre-Pan. HSAT utilization decreased significantly during the Pan-Mor (-59%) and Pan-Pre-Vax (-9%) phases compared to the Pre-Pan and subsequently increased during Pan-Vax (+6%) and Pan-Post-Vax (-1%) periods. Over 70% of STU transitioned to HSAT, and its usage surged five months after the vaccine Introduction.</p><p><strong>Conclusion: </strong>Sleep testing services utilization recovered differentially during the pandemic (PSG vs HSAT), including a surge in HSAT utilization post-vaccination.</p>","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11179655/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141331474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differences and Risk Factors of Peripheral Blood Immune Cells in Patients with Obstructive Sleep Apnea.","authors":"Ying Jiang, Chuankai Lin, Min Xu, Taiwen Zhu, Xuhong Li, Wei Wang","doi":"10.2147/NSS.S458098","DOIUrl":"10.2147/NSS.S458098","url":null,"abstract":"<p><strong>Introduction: </strong>Obstructive sleep apnea (OSA) is a respiratory disorder characterized by chronic intermittent hypoxia and fragmented sleep, leading to inflammatory response and oxidative stress. However, the differences in immune inflammatory response in OSA patients with different severity remain unclear.</p><p><strong>Purpose: </strong>This study aims to examine the differences in peripheral blood immune cells and their risk factors in OSA patients.</p><p><strong>Patients and methods: </strong>A total of 277 snoring patients from the Sleep Respiratory Disorder Monitoring Center of Zhongnan Hospital of Wuhan University were recruited in this study. According to the diagnosis and severity criteria of OSA, the included patients were further divided into simple snoring, mild, moderate, and severe groups. Peripheral blood immune cell counts including white blood cells, neutrophils, lymphocytes, monocytes, eosinophils, basophils, red blood cells, platelets, and polysomnography indicators were collected from the patients.</p><p><strong>Results: </strong>Compared with simple snoring patients, the OSA patients had increased circular monocyte and basophil count levels. In addition, correlation analysis results indicated that monocyte count was positively associated with chronic obstructive pulmonary disease (COPD), smoking, apnea-hypopnea index (AHI), the longest apnea duration, and Oxygen desaturation index (ODI), and negatively correlated with average SpO₂ in snoring patients. Finally, multiple linear regression analysis revealed that AHI, COPD, smoking, and maximum heart rate were independent predictors of monocyte count.</p><p><strong>Conclusion: </strong>OSA patients had a significant increase in their peripheral blood monocyte count. AHI, COPD, smoking, and maximum heart rate were risk factors for increased peripheral blood monocyte count in OSA patients. These findings suggest that peripheral blood monocytes can be considered an inflammatory biomarker of OSA.</p>","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11178088/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141331473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Changed Nocturnal Sleep Structure and Higher Anxiety, Depression, and Fatigue in Patients with Narcolepsy Type 1.","authors":"Jieyang Yu, Yanan Zhang, Lijia Cai, Qingqing Sun, Wanru Li, Junfang Zhou, Jianmin Liang, Zan Wang","doi":"10.2147/NSS.S452665","DOIUrl":"10.2147/NSS.S452665","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to evaluate nocturnal sleep structure and anxiety, depression, and fatigue in patients with narcolepsy type 1 (NT1).</p><p><strong>Methods: </strong>Thirty NT1 patients and thirty-five healthy controls were enrolled and evaluated using the Epworth sleepiness scale (ESS), Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, Fatigue Severity Scale (FSS), polysomnography, multiple sleep latency test, and brain function state monitoring. Statistical analyses were performed using SPSS Statistics for Windows, version 23.0. Benjamini-Hochberg correction was performed to control the false discovery rate.</p><p><strong>Results: </strong>Apart from typical clinical manifestations, patients with NT1 are prone to comorbidities such as nocturnal sleep disorders, anxiety, depression, and fatigue. Compared with the control group, patients with NT1 exhibited abnormal sleep structure, including increased total sleep time (<i>P</i> _adj=0.007), decreased sleep efficiency (<i>P</i> _adj=0.002), shortening of sleep onset latency (<i>P</i> _adj<0.001), elevated wake after sleep onset (<i>P</i> _adj=0.002), increased N1% (<i>P</i> _adj=0.006), and reduced N2%, N3%, and REM% (<i>P</i> _adj=0.007, <i>P</i> _adj<0.001, <i>P</i> _adj=0.013). Thirty-seven percent of patients had moderate to severe obstructive sleep apnea-hypopnea syndrome. And sixty percent of patients were complicated with REM sleep without atonia. Patients with NT1 displayed increased anxiety propensity (<i>P</i> _adj<0.001), and increased brain fatigue (<i>P</i> _adj=0.020) in brain function state monitoring. FSS scores were positively correlated with brain fatigue (<i>P</i> _adj<0.001) and mean sleep latency was inversely correlated with FSS scores and brain fatigue (<i>P</i> _adj=0.013, <i>P</i> _adj=0.029). Additionally, ESS scores and brain fatigue decreased after 3 months of therapy (<i>P</i>=0.012, <i>P</i>=0.030).</p><p><strong>Conclusion: </strong>NT1 patients had abnormal nocturnal sleep structures, who showed increased anxiety, depression, and fatigue. Excessive daytime sleepiness and fatigue improved after 3 months of treatment with methylphenidate hydrochloride prolonged-release tablets in combination with venlafaxine.</p>","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11170032/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141317836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Shared Genetic Architecture Between Obstructive Sleep Apnea and Body Mass Index.","authors":"Peng Zhou, Ling Li, Zehua Lin, Xiaoping Ming, Yiwei Feng, Yifan Hu, Xiong Chen","doi":"10.2147/NSS.S459136","DOIUrl":"10.2147/NSS.S459136","url":null,"abstract":"<p><strong>Purpose: </strong>The reciprocal comorbidity of obstructive sleep apnea (OSA) and body mass index (BMI) has been observed, yet the shared genetic architecture between them remains unclear. This study aimed to explore the genetic overlaps between them.</p><p><strong>Methods: </strong>Summary statistics were acquired from the genome-wide association studies (GWASs) on OSA (N_case = 41,704; N_control = 335,573) and BMI (N_overall = 461,460). A comprehensive genome-wide cross-trait analysis was performed to quantify global and local genetic correlation, infer the bidirectional causal relationships, detect independent pleiotropic loci, and investigate potential comorbid genes.</p><p><strong>Results: </strong>A positive significant global genetic correlation between OSA and BMI was observed (<i>r</i> _g = 0.52, <i>P</i> = 2.85e-122), which was supported by three local signal. The Mendelian randomization analysis confirmed bidirectional causal associations. In the meta-analysis of cross-traits GWAS, a total of 151 single-nucleotide polymorphisms were found to be pleiotropic between OSA and BMI. Additionally, we discovered that the genetic association between OSA and BMI is concentrated in 12 brain regions. Finally, a total 134 expression-tissue pairs were observed to have a significant impact on both OSA and BMI within the specified brain regions.</p><p><strong>Conclusion: </strong>Our comprehensive genome-wide cross-trait analysis indicates a shared genetic architecture between OSA and BMI, offering new perspectives on the possible mechanisms involved.</p>","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11166156/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141307790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Machine Learning Model for Predicting Sleep and Wakefulness Based on Accelerometry, Skin Temperature and Contextual Information","authors":"Aleksej Logacjov, Eivind Schjelderup Skarpsno, Atle Kongsvold, Kerstin Bach, Paul Jarle Mork","doi":"10.2147/nss.s452799","DOIUrl":"https://doi.org/10.2147/nss.s452799","url":null,"abstract":"<strong>Purpose:</strong> Body-worn accelerometers are commonly used to estimate sleep duration in population-based studies. However, since accelerometry-based sleep/wake-scoring relies on detecting body movements, the prediction of sleep duration remains a challenge. The aim was to develop and evaluate the performance of a machine learning (ML) model to predict accelerometry-based sleep duration and to explore if this prediction can be improved by adding skin temperature data, circadian rhythm based on the estimated midpoint of sleep, and cyclic time features to the model.<br/><strong>Patients and Methods:</strong> Twenty-nine adults (17 females), mean (SD) age 40.2 (15.0) years (range 17– 70) participated in the study. Overnight polysomnography (PSG) was recorded in a sleep laboratory or at home along with body movement by two accelerometers with an embedded skin temperature sensor (AX3, Axivity, UK) positioned at the low back and thigh. The PSG scoring of sleep/wake was used as ground truth for training the ML model.<br/><strong>Results:</strong> Based on pure accelerometer data input to the ML model, the specificity and sensitivity for predicting sleep/wake was 0.52 (SD 0.24) and 0.95 (SD 0.03), respectively. Adding skin temperature data and contextual information to the ML model improved the specificity to 0.72 (SD 0.20), while sensitivity remained unchanged at 0.95 (SD 0.05). Correspondingly, sleep overestimation was reduced from 54 min (228 min, limits of agreement range [LoAR]) to 19 min (154 min LoAR).<br/><strong>Conclusion:</strong> An ML model can predict sleep/wake periods with excellent sensitivity and moderate specificity based on a dual-accelerometer set-up when adding skin temperature data and contextual information to the model.<br/><br/><strong>Keywords:</strong> actigraphy, epidemiology, sedentary behaviors, sleep quality, supervised machine learning, support vector machines<br/>","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141253122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sleep Habits and Disturbances Among Tunisian Adults: A Cross-Sectional Online Survey","authors":"Sameh Msaad, Nouha Ketata, Nesrine Kammoun, Rahma Gargouri, Rim Khemakhem, Sourour Abid, Saeb Bader, Sabrine Efidha, Narjes Abid, Jamel El Ghoul, Imen Sahnoun, Hazem Altalaa, Jihen Jdidi, Mohamed Jlidi, Nadia Keskes Boudaouara, Imen Gargouri, Najla Bahloul, Samy Kammoun","doi":"10.2147/nss.s456879","DOIUrl":"https://doi.org/10.2147/nss.s456879","url":null,"abstract":"<strong>Background:</strong> Sleep quality and disturbances have gained heightened scholarly attention due to their well-established association with both mental and physical health. This study aims to assess sleep-wake habits and disturbances in Tunisian adults.<br/><strong>Methodology:</strong> This cross-sectional study employed an online questionnaire to assess 3074 adults ≥ 18 years. Primary outcomes, including sleep quality, daytime vigilance, mood, and subjective well-being, were measured using validated questionnaires [the Pittsburgh Sleep Quality Index (PSQI), the Insomnia Severity Index (ISI), the Epworth Sleepiness Scale (ESS), the Patient Health Questionnaire (PHQ)-9, and the World Health Organisation-Five Well-Being Index (WHO-5)].<br/><strong>Results:</strong> Less than two-thirds (n= 1941; 63.1%) of participants were females and the mean age was 36.25± 13.56. The prevalence of poor sleep quality was 53.8% when defined as a PSQI &gt; 5. The prevalence of insomnia, short sleep duration, long sleep duration, EDS, severe depression, and poor well-being were 14.5%, 34.7%, 12.3%, 32.4%, 7.4%, and 40.2%, respectively. Some factors were associated with an increased likelihood of poor sleep quality, including female gender, chronic hypnotics use, internet use close to bedtime, daily time spent on the internet &gt; 3 hours, smoking, university- level education, nocturnal work, severe depression, impaired well-being status, insomnia, and EDS.<br/><strong>Conclusion:</strong> The high prevalence of sleep-wake disturbances among Tunisian adults emphasizes the need for an appropriate screening strategy for high-risk groups. Individuals with unhealthy habits and routines were significantly more likely to experience these kinds of disturbances. Consequently, there is a pressing need for educational programs on sleep to foster healthier sleep patterns.<br/><br/>","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141252743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}