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BACKGROUND: Poor sleep quality is now a cause of sexual dysfunction. AIM: To investigate variations in sleep quality among patients with different types of premature ejaculation (PE) and a control group. METHODS: Patients with PE were categorized into groups according to 4 types: lifelong (LPE), acquired (APE), variable (VPE), and subjective (SPE). Basic demographic information about the participants was first collected, and then clinical data were obtained. OUTCOMES: Outcomes included the 5-item International Index of Erectile Function, Premature Ejaculation Diagnostic Tool, 7-item Generalized Anxiety Disorder, 9-item Patient Health Questionnaire, Pittsburgh Sleep Quality Index, self-estimated intravaginal ejaculation latency time (minutes), and sleep monitoring parameters obtained from a wearable device (Fitbit Charge 2). RESULTS: A total of 215 participants were enrolled in the study, of which 136 patients with PE were distributed as follows: LPE (31.62%), APE (42.65%), VPE (10.29%), and SPE (15.44%). Subjective scales showed that patients with APE were accompanied by a higher prevalence of erectile dysfunction, anxiety, and depression, as well as poorer sleep quality (assessed by the Pittsburgh Sleep Quality Index). The results of objective sleep parameters revealed that average durations of sleep onset latency (minutes) and wake after sleep onset (minutes) in patients with APE (mean ± SD; 20.03 ± 9.14, 55 ± 23.15) were significantly higher than those with LPE (15.07 ± 5.19, 45.09 ± 20.14), VPE (13.64 ± 3.73, 38.14 ± 11.53), and SPE (14.81 ± 4.33, 42.86 ± 13.14) and the control group (12.48 ± 3.45, 37.14 ± 15.01; P < .05). The average duration of rapid eye movement (REM; minutes) in patients with APE (71.34 ± 23.18) was significantly lower than that in patients with LPE (79.67 ± 21.53), VPE (85.93 ± 6.93), and SPE (80.86 ± 13.04) and the control group (86.56 ± 11.93; P < .05). Similarly, when compared with the control group, patients with LPE had significantly longer durations of sleep onset latency and wake after sleep onset and a significantly shorter duration of REM sleep. CLINICAL IMPLICATIONS: Our study suggests that clinicians should pay attention not only to male physical assessment but also to mental health and sleep quality. STRENGTHS AND LIMITATIONS: This study suggests that changes in sleep structure occur in patients with PE, which may provide some direction for future research. However, the cross-sectional study design does not allow us to conclude that sleep is a risk factor for PE. CONCLUSION: After controlling for traditional parameters such as age, erectile dysfunction, anxiety, and depression, sleep parameters are independently associated with PE. Patients with APE and LPE show significant alterations in sleep parameters, with patients with APE having notably poorer sleep quality, whereas patients with VPE and SPE have sleep parameters similar to controls.

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AIM: Although sleep is essential for maintaining good health and well-being, sleep disorders are becoming increasingly prevalent. Probiotics may play a role in sleep regulation; therefore, this study aimed to provide a comprehensive overview of the effects of probiotics on sleep parameters. METHODS: We conducted a systematic literature review and meta-analysis, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology. Relevant placebo-controlled randomized controlled trials examining the effects of probiotics on sleep outcomes were identified through systematic searches in the PubMed, Cochrane Library, and Ichushi databases. Data were extracted from eligible studies and the risk of bias was assessed. Statistical analyses were performed to assess the effects of probiotics on various sleep-related variables. RESULTS: Fifteen randomized controlled trials were included in this review. The decrease in Pittsburgh Sleep Quality Index (PSQI) scores in the probiotics group was significantly lower than that in the placebo group after 4-6 weeks and 8-16 weeks, indicating improved sleep quality. The Oguri-Shirakawa-Azumi (OSA) sleep inventory score was also decreased in the probiotics group for Factor 1 "sleepiness on rising" and Factor 4 "refreshing," indicating improved sleep quality. Some studies however, showed a risk of bias. CONCLUSION: This systematic review and meta-analysis indicated that probiotics may improve sleep quality, as measured by the PSQI and OSA sleep inventory. However, further research is needed to better understand the effects of probiotics on specific sleep parameters and address the limitations of existing studies.

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Background: Pediatric obstructive sleep apnea (OSA) is a multifaceted disorder marked by recurrent upper airway obstruction during sleep, often coexisting with various medical conditions. This study, aimed to comprehensively analyze the Multifaceted Landscape of Pediatric Insights into Prevalence, Severity, and Coexisting Conditions. With a sample of 1928 participants, our study sought to determine the prevalence, severity, and associations between OSA and diverse conditions. Methods: Conducted retrospectively from February 2019 to April 2023, the study included pediatric patients. Data were collected through electronic health records, involving clinical assessments, medical histories, and diagnostic tests to establish OSA and coexisting condition diagnoses. Relationships between sleep parameters, apnea types, and severity indices were evaluated. Results: High OSA prevalence was evident across age groups, with severity peaking between 3 to 12 years. Among the participants, coexisting conditions included allergic rhinitis (59.6%), tonsillar hypertrophy (49.7%), adenoid hypertrophy (28.4%), and obesity (15.3%). Analysis revealed intriguing relationships between different sleep parameters and apnea types. Notable associations were observed between Obstructive Apnea (OA) and Central Apnea (CA), and Mixed Apnea (MA) displayed associations with both OA and CA. Hypopnea correlated directly with the Apnea-Hypopnea Index (AHI), reflecting its role in OSA severity. Conclusion: This study provides a comprehensive understanding of the intricate dynamics between pediatric OSA and coexisting conditions. The prevalence of OSA and its coexistence with various conditions underscore the need for comprehensive evaluation and management strategies. By revealing associations between different sleep parameters and apnea types, the study emphasizes the complexity of OSA diagnosis and management. These findings hold the potential to enhance clinical approaches, ultimately leading to improved care and outcomes for affected children.

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Polysomnography (PSG) is the gold standard for clinical sleep monitoring, but its cost, discomfort, and limited suitability for continuous use present challenges. The flexible electrode sleep patch (FESP) emerges as an economically viable and patient-friendly solution, offering lightweight, simple operation, and self-applicable. Nevertheless, its utilization in young individuals remains uncertain. The objective of this study was to compare sleep data obtained by FESP and PSG in healthy young individuals and analyze agreement for sleep parameters and structure classification. Overnight monitoring with FESP and PSG recordings in 48 participants (mean age: 23 yr) was done. Correlation analysis, Bland-Altman plots, and Cohen's kappa coefficient assessed consistency. Sensitivity, specificity, and predictive values compared classification against PSG. FESP showed strong correlation and consistency with PSG for sleep monitoring. Bland-Altman plots indicated small errors and high consistency. Kappa values (0.70-0.84) suggested substantial agreement for sleep stage classification. Pearson correlation coefficient values for sleep stages (0.75-0.88) and sleep parameters (0.80-0.96) confirm that FESP has a strong application. Intraclass correlation coefficient yielded values between 0.65 and 0.97. In addition, FESP demonstrated an impressive accuracy range of 84.12-93.47% for sleep stage classification. The FESP also features a wearable self-test program with an error rate of no more than 8% for both deep sleep and wake. In young adults, FESP demonstrated reliable monitoring capabilities comparable to PSG. With its low cost and user-friendly design, FESP is a potential alternative for portable sleep assessment in clinical and research applications. Further studies involving larger populations are needed to validate its diagnostic potential.NEW & NOTEWORTHY By comparison with PSG, this study confirmed the reliability of an efficient, objective, low-cost, and noninvasive portable automatic sleep-monitoring device FESP, which provides effective information for long-term family sleep disorder diagnosis and sleep quality monitoring.

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Sleep difficulties are a common symptom in cancer patients at different stages of treatment trajectory and may lead to numerous negative consequences for which management is required. This pilot Randomized Controlled Trial (RCT) aims to assess the potential effectiveness of home-based prehabilitation intervention (prehab) on sleep quality and parameters compared to standard care (SOC) in colorectal cancer patients during the preoperative period and up to 8 weeks after the surgery. One hundred two participants (48.3% female, mean age 65 years) scheduled for elective resection of colorectal cancer were randomized to the prehab (n = 50) or the SOC (n = 52) groups. Recruitment and retention rates were 54% and 72%, respectively. Measures were completed at the baseline and preoperative, 4- and 8-week after-surgery follow-ups. Our mixed models' analyses revealed no significant differences between groups observed over time for all subjective and objective sleep parameters. A small positive change was observed in the perceived sleep quality only at the preoperative time point for the prehabilitation group compared to the SOC group, with an effect size d = 0.11 and a confidence interval (CI) between - 2.1 and - 0.1, p = .048. Prehab group patients with high anxiety showed a significant improvement in the rate of change of sleep duration over time compared to the SOC group, with a difference of 110 min between baseline and 8 weeks after surgery (d = 0.51, 95% CI: 92.3 to 127.7, p = .02). Multimodal prehabilitation intervention is feasible in colorectal cancer patients and may improve sleep duration for patients with high anxiety symptoms. Future large-scale RCTs are needed to confirm our results.

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STUDY OBJECTIVES: Wearable sleep-tracker devices are ubiquitously used to measure sleep; however, the estimated sleep parameters often differ from the gold-standard polysomnography (PSG). It is unclear to what extent we can tolerate these errors within the context of a particular clinical or operational application. Here, we sought to develop a method to quantitatively determine whether a sleep tracker yields acceptable sleep-parameter estimates for assessing alertness impairment. METHODS: Using literature data, we characterized sleep-measurement errors of 18 unique sleep-tracker devices with respect to PSG. Then, using predictions based on the unified model of performance, we compared the temporal variation of alertness in terms of the psychomotor vigilance test mean response time for simulations with and without added PSG-device sleep-measurement errors, for nominal schedules of 5, 8, or 9 hours of sleep/night or an irregular sleep schedule each night for 30 consecutive days. Finally, we deemed a device error acceptable when the predicted differences were smaller than the within-subject variability of 30 milliseconds. We also established the capability to estimate the extent to which a specific sleep-tracker device meets this acceptance criterion. RESULTS: On average, the 18 sleep-tracker devices overestimated sleep duration by 19 (standard deviation = 44) minutes. Using these errors for 30 consecutive days, we found that, regardless of sleep schedule, in nearly 80% of the time the resulting predicted alertness differences were smaller than 30 milliseconds. CONCLUSIONS: We provide a method to quantitatively determine whether a sleep-tracker device produces sleep measurements that are operationally acceptable for fatigue management.

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Background: The interaction between smoking and sleep seems appears to be bidirectional, but few studies evaluated the impact of smoking and its cessation on objective sleep parameters. In this context, this new study aimed to assess the impact of smoking and its cessation on sleep architecture and on ventilatory sleep parameters, particularly the presence of sleep apnea syndrome (apnea-hypopnea index (AHI)≥15). Methods: Patients hospitalized for polysomnographic sleep exploration were compared according to their smoking status: active smokers (AS), former smokers (FS), non-smokers (NoNi). Psychiatric and non-psychiatric co-morbidities and treatment or substance use were taken into account in the analyses. Results: A total of 170 participants were included (N = 37 FS, 39 AS, 86 NoNi). A significant decrease in the mean nocturnal O2 saturation was observed for FS and AS compared to NoNi. No differences were found regarding AHI. Regarding sleep architecture, we observed a significant decrease in the slow wave sleep duration for AS compared to NoNi, and interestingly not between FS and NoNi. Conclusion: This study suggests that current smokers suffer from alterations in both sleep architecture and ventilatory parameters, the later appears to persist even after smoking cessation.

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BACKGROUND: Sleep disturbances are associated with adverse perinatal outcomes. Thus, it is necessary to understand the continuous patterns of sleep during pregnancy and how moderators such as maternal age and pre-pregnancy body mass index impact sleep. OBJECTIVE: This study aimed to examine the continuous changes in sleep parameters objectively (i.e. sleep stages, total sleep time, and awake time) in pregnant women and to describe the impact of maternal age and/or pre-pregnancy body mass index as moderators of these objective sleep parameters. DESIGN: This was a longitudinal observational design. METHODS: Seventeen women with a singleton pregnancy participated in this study. Mixed model repeated measures were used to describe weekly patterns, while aggregated changes describe these three pregnancy periods (10-19, 20-29, and 30-39 gestational weeks). RESULTS: For the weekly patterns, we found significantly decreased deep (1.26 ± 0.18 min/week, p < 0.001), light (0.72 ± 0.37 min/week, p = 0.05), and total sleep time (1.56 ± 0.47 min/week, p < 0.001) as well as increased awake time (1.32 ± 0.34 min/week, p < 0.001). For the aggregated changes, we found similar patterns to weekly changes. Women (⩾30 years) had an even greater decrease in deep sleep (1.50 ± 0.22 min/week, p < 0.001) than those younger (0.84 ± 0.29 min/week, p = 0.04). Women who were both overweight/obese and ⩾30 years experienced an increase in rapid eye movement sleep (0.84 ± 0.31 min/week, p = 0.008), but those of normal weight (<30 years) did not. CONCLUSION: This study appears to be the first to describe continuous changes in sleep parameters during pregnancy at home. Our study provides preliminary evidence that sleep parameters could be potential non-invasive physiological markers predicting perinatal outcomes.

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BACKGROUND: The lateral pharyngeal wall (LPW) is a critical anatomic structure in patients with obstructive sleep apnea (OSA). Resolving the retropalatal circumferential (RC) narrowing caused by combination of both LPW collapse and antero-posterior (AP) narrowing holds promise for surgical treatment of OSA. We sought to determine the clinical characteristics and distinctive alterations in sleep parameters of patients with OSA who have RC narrowing and LPW collapse. METHODS: Drug-induced sleep endoscopy (DISE), polysomnography findings, and sleep questionnaires were reviewed retrospectively in patients with OSA. RESULTS: Of the 106 OSA patients examined, 48% showed RC narrowing and 44% showed AP narrowing at the oropharynx level during sleep while 8% of the patients showed only LPW collapse. Patients with RC narrowing with LPW collapse exhibited a higher BMI than those with AP narrowing only. In addition, patients with RC narrowing showed more aggravated sleep parameters including apneic events than patients with AP narrowing alone. The degree of RC narrowing correlated significantly with the severity of OSA as shown by a higher apnea index and lower oxygen desaturations. CONCLUSIONS: Our clinical findings suggest that the presence of RC narrowing with LPW collapse in OSA is closely related to increased apneic and oxygen desaturation events. RC narrowing with LPW collapse may be targets for surgical correction in patients with OSA to improve therapeutic outcomes.

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INTRODUCTION: Tobacco use may be associated with incident insomnia. The aim of the study was to investigate the association between tobacco use and incident sleep parameters in a longitudinal study in South Africa. METHODS: Longitudinal data from two consecutive waves of middle-aged and older adults in 2014-2015 (n=5059) and 2018-2019 (n=4176) in rural South Africa were analyzed. Tobacco use and sleep parameters were assessed by self-report. The associations between tobacco use and incident sleep parameters were estimated with multivariable logistic regression. RESULTS: The prevalence of baseline sleep parameters was poor sleep quality 6.5%, sleep disturbance 13.6%, restless sleep 32.9%, and breathing stops 7.0%. In the fully adjusted model for people without poor sleep quality at baseline, daily tobacco smoking, smoking ≥10 units of tobacco products, current tobacco use and current smokeless tobacco use did not increase the odds of incident poor sleep quality. Smoking ≥10 units of tobacco products in a day (AOR=3.83; 95% CI: 1.77-8.28), current tobacco use (AOR=1.65; 95% CI: 1.09-2.51), and daily tobacco smoking (AOR=2.16; 95% CI: 1.15-4.07), were significantly positively associated with incident sleep disturbance. Furthermore, incident restless sleep was significantly positively associated with smoking ≥10 units of tobacco products in a day (AOR=3.97; 95% CI: 1.18-13.37), current smokeless tobacco use (AOR=2.78; 95% CI: 1.17-6.62) and current tobacco use (AOR=2.00; 95% CI: 1.00-4.00). Incident breathing stops were significantly positively associated with daily smoking tobacco (AOR=2.08; 95% CI: 1.11-3.34), smoking 1-9 units of tobacco products in a day (AOR=2.17; 95% CI: 1.20-3.94), and current tobacco use (AOR=1.77; 95% CI: 1.16-2.72). CONCLUSIONS: Higher tobacco use was independently associated with incident sleep disturbance, incident restless sleep, and incident breathing stops, but not with incident poor sleep quality.

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Actigraphic parameters can provide indication of people's sleep quality during their daily lives. However, there is a need for clear guidelines on the understanding of the different actigraphic parameters. The present study aims to propose a conceptual and theoretical framework for known actigraphic-derived parameters, which is able to describe the alternation between rest and wake phases during the nocturnal sleep, explaining their main characteristics and interrelations that can be replicated in future studies. Forty Sport Sciences students at the University of Milan (20 males; mean age ± SD, 22 ± 3 y) completed the validated Italian version of Morningness-Eveningness Questionnaire (MEQ) and wore an actigraph (Motion Watch 8®, Cambridge Neurotechnology, Cambridge, UK) for seven days. A framework was developed to depict the interactions between the actigraphic parameters and how they objectively describe sleep, according to which the parameters are organized into three different functional blocks related to different aspects of sleep. Correlations analyses were conducted to explore the relationships among the primary actigraphic parameters within and across the functional blocks. The proposed framework is a purely theoretical construct that provides a simple interpretation of known actigraphic parameters guiding researchers and practitioners in the use of these parameters either for research or clinical purposes.

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STUDY OBJECTIVES: In-laboratory polysomnography is the current gold standard for objective sleep measurements in clinical trials, but this does not capture night-to-night variability in sleep parameters. This study analyzed variability in sleep parameters recorded over multiple nights of sleep in an ecological setting using a portable sleep monitor and then estimated the minimum sample sizes required to reliably account for inter- and intra-individual variability in sleep parameters. METHODS: Participants were males who self-reported the absence of sleep disorders, and used a sleep monitoring device (Dreem Headband, Dreem, France) over multiple nights of sleep. Night-to-night variability of sleep parameters was determined over five consecutive weeknights using coefficients of variation (CV), and the minimal number of individuals and nights needed to reliably determine each sleep parameter was assessed. RESULTS: Night-to-night variability for the whole group (n = 94; 470 nights) was high (CV 0.44-0.58) for N2, N3, sleep onset and persistent sleep latencies, and wake after sleep onset (WASO), medium (CV 0.22-0.28) for N1 and N3 percentage, awakenings and REM latency, and low (CV 0.04-0.19) for sleep efficiency, N2 and REM percentages, total sleep time (TST) and micro-arousal index. Minimum sample sizes for reliable assessment of TST and WASO were 2 nights with 10 participants and 4 nights with 50 participants, respectively. CONCLUSIONS: Night-to-night variability of sleep parameters is underestimated and under-recognized. These data on variability in commonly used sleep parameters will facilitate better estimation of sample sizes and number of nights required in clinical trials based on the outcomes of interest.

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Background: There is a need to better understand the association between sleep and chronic diseases. In this study we developed a natural language processing (NLP) algorithm to mine polysomnography (PSG) free-text notes from electronic medical records (EMR) and evaluated the performance. Methods: Using the Veterans Health Administration EMR, we identified 46,093 PSG studies using CPT code 95,810 from 1 October 2000−30 September 2019. We randomly selected 200 notes to compare the accuracy of the NLP algorithm in mining sleep parameters including total sleep time (TST), sleep efficiency (SE) and sleep onset latency (SOL), wake after sleep onset (WASO), and apnea-hypopnea index (AHI) compared to visual inspection by raters masked to the NLP output. Results: The NLP performance on the training phase was >0.90 for precision, recall, and F-1 score for TST, SOL, SE, WASO, and AHI. The NLP performance on the test phase was >0.90 for precision, recall, and F-1 score for TST, SOL, SE, WASO, and AHI. Conclusions: This study showed that NLP is an accurate technique to extract sleep parameters from PSG reports in the EMR. Thus, NLP can serve as an effective tool in large health care systems to evaluate and improve patient care.

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PURPOSE: This systematic review aims to assess the night-to-night variability (NtNV) in respiratory sleep parameters in children and the accuracy of diagnosing obstructive sleep apnea (OSA) in children based on a single-night sleep study. METHODS: The PubMed, EMBASE, and Cochrane Library databases were searched until March 8, 2021. This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (CRD42021239838). RESULTS: Our study included 395 patients from 5 articles. The mean (SD) age of all included patients was 11.78 (4.05) years. AHI was reported for 325 participants in 4 studies, and the mean change between two consecutive nights was -0.13 [95% CI: -0.40, 0.14] events per hour. The mean change in OAI was -0.07 [95% CI: -0.27, 0.12] events per hour in 187 participants across 3 studies. Based on the diagnostic criteria used, three studies reported that the diagnostic rates of OSA patients in a single-night sleep study were 83%, 84.6%, and 91%. The NtNV in AHI in children with severe and moderate OSA was greater than that in children with mild OSA (3.35 [95% CI: 0.07, 6.62] events per hour vs -0.15 [95% CI: -0.42, 0.12] events per hour), and these children with more severe OSA may have shown a higher AHI on the first night. CONCLUSIONS: The NtNV in AHI was not statistically significant in the group sample of children. However, there were significant differences in NtNV in AHI between children with mild and moderate-to-severe OSA. Individual NtNV in respiratory sleep parameters may cause children to be misdiagnosed by single-night diagnostic sleep studies.

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Background: Sleep state misperception (SSM) is seen among patients with obstructive sleep apnea (OSA) as well as those having insomnia. Moreover, OSA and insomnia can also be comorbid. This study aims at finding the proportion of SSM and "Comorbid Insomnia with OSA" (COMISA) among patients of OSA and chronic insomnia. Macroachitecture of sleep was also compared across groups. Methods: This study utilized the retrospective laboratory and medical records of two groups of patients: chronic insomnia and OSA. Sleep disorders were diagnosed according to standard criteria. Daytime sleepiness was examined using the Epworth Sleepiness Scale. Diagnosis of SSM was based on the difference between subjective and objective sleep onset latency (Subjective SOL > 1.5 × Objective SOL). Results: Sixteen adult subjects were included in each group. Based on the difference between subjective and objective sleep onset latency, SSM was reported by 62.5% subjects of chronic insomnia and 56.25% subjects having OSA (OR = 1.29; 95% CI = 0.31-5.33; P = 0.79). The proportion of COMISA in subjects with chronic insomnia was 18% and among subjects with OSA, it was 43%. Effect size for the proportion was calculated as odds ratio (33.96; 95% CI = 7.48-154.01; P < 0.0002). Thus, the odds for COMISA were higher among subjects with OSA than those with chronic Insomnia. The three groups (OSA, COMISA and Chronic Insomnia) were comparable with regard to the macro-architecture of sleep. Conclusion: SSM is common among subjects with OSA and chronic insomnia. COMISA was commoner among patients with OSA compared to those with chronic insomnia. Macro-architecture of sleep is comparable among groups.

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BACKGROUND: This study examined the effect of vitamin B12 supplementation on sleep parameters of latency, total sleep time, wake-after-sleep onset, total time in bed and sleep efficiency in healthy adults. METHODS: This quasi-experimental pre-test, post-test design recruited adults 25-50 years old, with normal vitamin B12 levels. Sleep parameters were assessed using Phillips Respironics Actiwatch-2® on non-dominant wrist from Monday to Thursday for four weeks. Pre-supplementation data was collected for the first two weeks; then participants started 3mg pre-packaged cyanocobalamin supplements daily for 14 days. Post-supplementation data was collected for weeks three and four. Serum was collected by venipuncture at the beginning and end of the study for vitamin B12 assay. Descriptive statistics involved median and interquartile range [IQR]. A comparison of the sleep parameters before and after cyanocobalamin supplementation was done using non-parametric inferential analysis. RESULTS: Fourteen healthy adult participants completed the study; nine females and five males with median age of 37[17] years and a normal range of serum vitamin B12 level (169-695 pmol/L). Median serum vitamin B12 level was significantly elevated following supplementation (355[217] to 961[679]) pmol/L; p = 0.020); but there was no change in any of the sleep parameters measured. Spearman's rho correlation analysis showed no correlation between serum vitamin B12 levels and the sleep parameters for pre-supplementation and post-supplementation weeks. CONCLUSION: Two weeks of cyanocobalamin supplementation (3mg/day) resulted in the expected increase in serum vitamin B12 levels in healthy adults but did not influence their sleep wake activity.

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Acute and chronic insomnia have different causes and may require different treatments. They are investigated with multi-night nocturnal actigraphy data from two sleep studies. Two different wrist-worn actigraphy devices were used to measure physical activities. This required data pre-processing and transformations to smooth the differences between devices. Statistical, power spectrum, fractal and entropy analyses were used to derive features from the actigraphy data. Sleep parameters were also extracted from the signals. The features were then submitted to four machine learning algorithms. The best performing model was able to distinguish acute from chronic insomnia with an accuracy of 81%. The algorithms were then used to evaluate the acute and chronic groups compared to healthy sleepers. The differences between acute insomnia and healthy sleep were more prominent than between chronic insomnia and healthy sleep. This may be associated with the adaptation of the physiology to prolonged periods of disturbed sleep for individuals with chronic insomnia. The new model is a powerful addition to our suite of machine learning models aiming to pre-screen insomnia at home with wearable devices.

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PURPOSE OF REVIEW: To summarise the association between obstructive sleep apnoea and diabetic retinopathy and diabetic macular oedema. To examine the effects of other sleep parameters on diabetic retinopathy and diabetic macular oedema. To discuss the pathophysiology of diabetic eye changes and how it is related to obstructive sleep apnoea. RECENT FINDINGS: Conflicting data exists in terms of the association of diabetic eye changes with sleep apnoea and/or other sleep parameters. Various cross-sectional studies show PDR to be associated with the prevalence of OSA. Patients who underwent continuous positive airway pressure (CPAP) treatment were significantly less likely to develop pre/proliferative DR. Secondary sleep parameters generally are not associated with DR except for long duration of sleep. Differences in reporting could be due to the different thresholds set to define OSA/ODI and severity of DR/DME, in addition to factors used in multivariate analysis. There is a need for further studies with long-term follow-up and to assess the impact of CPAP on the development and progression of diabetic eye change(s).

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PURPOSE: : To screen for obstructive sleep apnea (OSA) in patients presenting to diabetic retinopathy (DR) clinic and to correlate its presence with the severity of DR. METHODS: : A prospective, cross-sectional study of diabetes mellitus patients in retina clinic of a tertiary care referral center, North India (January 2019-March 2020). All were subjected to STOP-Bang Questionnaire and Epworth Sleepiness Scale (ESS) score. Patients at high OSA risk (STOP-Bang score ≥5 and ESS score ≥10) were referred to Department of Otorhinolaryngology (sleep clinic) for polysomnography. Based on Apnea Hypopnea Index (AHI), OSA was graded as mild (AHI = 5-14/h), moderate (AHI = 15-30/h), and severe (AHI >30/h). Statistical analysis was done using three models of outcome measures: (1) "No DR" versus "any DR," (2) "Less severe DR" versus "More severe DR," and (3) "No diabetic macular edema (DME)" versus "DME." RESULTS: : Of 362 patients screened, 18 (4.97%) had OSA (11 mild, 5 moderate, and 2 severe). Though OSA did not show a significant association with various outcome measures, patients with moderate-severe OSA had higher odds in developing "any DR" (OR = 7.408; 95% CI = 0.533-102.898), "more severe DR" (OR = 1.961; 95% CI = 0.153-25.215), and "DME" (OR = 2.263; 95% CI = 0.357-14.355), on multiple logistic regression. CONCLUSION: : Ours is the first screening study of OSA in DR patients in India, the diabetes capital of the world. We detected OSA in 4.97% of patients in a DR clinic, with an increased risk of "any DR," "more severe DR," and "DME" in the presence of moderate-severe OSA.

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BACKGROUND: Several behaviors have been reported to interfere with sleep in otherwise healthy adults, including low physical activity (PA) levels. However, few studies have compared low PA with the other behavioral risk factors of objective sleep impairment, despite the behavior tending to cooccur in highly stressed and affectively distressed individuals. Thus, the authors compared objective and subjective measures of PA and other potential sleep disrupting behaviors as predictors of objective sleep (sleep onset latency, actual sleep time, total sleep duration, awake time, and sleep efficacy) at baseline (T1) and 3 months later (T2). METHODS: A community-derived sample of 161 people aged 18-65 years were asked about PA, other behavior (ie, night eating, electronic device use, watching television, caffeine and alcohol use), stress, affective distress (ie, anxiety, depression), and demographics including shift work and parenting young children in an online questionnaire at T1 and T2. PA and sleep were also monitored for 24 hours each at T1 and T2 using actigraphy. RESULTS: Multiple regression analyses indicated that sleep at T1 was associated with PA (ie, total number of steps, metabolic equivalents/time, time spent travelling) after controlling mean ambient temperature and relevant demographics. At T2, longer sleep onset latency was predicted by parenting young children and night time television viewing; shorter sleep duration was predicted by female gender; and awake time and sleep efficacy were predicted by alcohol intake after controlling T1 sleep measures, demographics, and mean ambient temperature. CONCLUSION: The risk factors for objective sleep impairment included parenting young children and watching television at night, whereas better sleep outcomes were associated with greater engagement with PA.

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