Objective: To investigate the nature of the associations between the severity of OSA and the number and anatomical sites of upper airway operations with operative complications.
Design, Setting, and Participants: This retrospective study included adult patients diagnosed with OSA (apnea-hypopnea index [AHI], >5) who underwent upper airway surgery at a single tertiary referral hospital between October 1, 2008, and October 1, 2015.
Interventions: All patients underwent single or combination surgery on the nose, palatopharyngeal (tonsils, adenoids, and soft palate), and tongue base as a treatment of OSA.
Main Outcomes and Measures: Pulmonary, surgical, and cardiovascular complications within the first 30 postoperative days were analyzed according to OSA severity and types of upper airway surgery. Logistic regression was used to assess the multivariable association of OSA, age, sex, body mass index, medical comorbidities, and types of upper airway surgery with short-term operative complications.
Results: The study included 95 patients (87 males [91.6%]; 83 were Malay [87.4%]; mean [SD] age, 37.7 [1.6] years) with complete data and follow-up who underwent upper airway surgery to treat OSA. Patients with more severe OSA had greater body mass index (Cohen d, 0.27; 95% CI, -0.28 to 0.82), longer surgical time (Cohen d, 1.57; 95% CI, 0.95-2.15), and older age (Cohen d, 3.06; 95% CI, 2.29-3.77). At least 1 operative complication occurred in 48 of 95 patients (51%). In a multivariable model, the overall complication rate was increased with age and body mass index. Complication rates were not associated with AHI severity, type of procedure performed, and whether the surgery was single or combination surgery. Lowest oxygen desaturation (odds ratio, 1.03; 95% CI, 0.96-1.45; P = .04) and longest apnea duration (odds ratio, 1.03; 95% CI, 0.99-1.08; P = .02) were polysomnographic variables that predict the short-term operative complications.
Conclusions and Relevance: In patients with OSA undergoing upper airway surgery, the severity of OSA as assessed by AHI, and the sites and numbers of concurrent operations performed were not associated with the rate of short-term operative complications.
Methods: One hundred participants (50 good sleepers; 50 poor sleepers) were asked to choose between 2 written scenarios to answer 1 of 2 questions: "Which describes a better (or worse) night of sleep?". Each scenario described a self-reported experience of sleep, stringing together 17 possible determinants of sleep quality that occur at different times of the day (day before, pre-sleep, during sleep, upon waking, day after). Each participant answered 48 questions. Logistic regression models were fit to their choice data.
Results: Eleven of the 17 sleep quality parameters had a significant impact on the participants' choices. The top 3 determinants of sleep quality were: Total sleep time, feeling refreshed (upon waking), and mood (day after). Sleep quality judgments were most influenced by factors that occur during sleep, followed by feelings and activities upon waking and the day after. There was a significant interaction between wake after sleep onset and feeling refreshed (upon waking) and between feeling refreshed (upon waking) and question type (better or worse night of sleep). Type of sleeper (good vs poor sleepers) did not significantly influence the judgments.
Conclusions: Sleep quality judgments appear to be determined by not only what happened during sleep, but also what happened after the sleep period. Interventions that improve mood and functioning during the day may inadvertently also improve people's self-reported evaluation of sleep quality.
METHODS: Parents of children aged 3-5 years, from 14 countries (8 low- and middle-income countries, LMICs) completed surveys to assess changes in movement behaviours and how these changes were associated with the COVID-19 pandemic. Surveys were completed in the 12 months up to March 2020 and again between May and June 2020 (at the height of restrictions). Physical activity (PA), sedentary screen time (SST) and sleep were assessed via parent survey. At Time 2, COVID-19 factors including level of restriction, environmental conditions, and parental stress were measured. Compliance with the World Health Organizations (WHO) Global guidelines for PA (180 min/day [≥60 min moderate- vigorous PA]), SST (≤1 h/day) and sleep (10-13 h/day) for children under 5 years of age, was determined.
RESULTS: Nine hundred- forty-eight parents completed the survey at both time points. Children from LMICs were more likely to meet the PA (Adjusted Odds Ratio [AdjOR] = 2.0, 95%Confidence Interval [CI] 1.0,3.8) and SST (AdjOR = 2.2, 95%CI 1.2,3.9) guidelines than their high-income country (HIC) counterparts. Children who could go outside during COVID-19 were more likely to meet all WHO Global guidelines (AdjOR = 3.3, 95%CI 1.1,9.8) than those who were not. Children of parents with higher compared to lower stress were less likely to meet all three guidelines (AdjOR = 0.5, 95%CI 0.3,0.9).
CONCLUSION: PA and SST levels of children from LMICs have been less impacted by COVID-19 than in HICs. Ensuring children can access an outdoor space, and supporting parents' mental health are important prerequisites for enabling pre-schoolers to practice healthy movement behaviours and meet the Global guidelines.
METHODS: This is a cross-sectional study involving 27 patients with symptoms of OSAS seen at a tertiary institutional center and 25 normal controls performed between June 2015 and June 2016. All patients and controls underwent a polysomnography (PSG) test and were diagnosed with OSAS based on the apnea-hypopnea index (AHI). Patients are those with OSAS symptoms and had AHI > 5, whereas controls are staffs from the ophthalmology clinic without clinical criteria for OSAS and had PSG result of AHI
Objective: To assess whether sleep timing and napping behavior are associated with increased obesity, independent of nocturnal sleep length.
Design, Setting, and Participants: This large, multinational, population-based cross-sectional study used data of participants from 60 study centers in 26 countries with varying income levels as part of the Prospective Urban Rural Epidemiology study. Participants were aged 35 to 70 years and were mainly recruited during 2005 and 2009. Data analysis occurred from October 2020 through March 2021.
Exposures: Sleep timing (ie, bedtime and wake-up time), nocturnal sleep duration, daytime napping.
Main Outcomes and Measures: The primary outcomes were prevalence of obesity, specified as general obesity, defined as body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) of 30 or greater, and abdominal obesity, defined as waist circumference greater than 102 cm for men or greater than 88 cm for women. Multilevel logistic regression models with random effects for study centers were performed to calculate adjusted odds ratios (AORs) and 95% CIs.
Results: Overall, 136 652 participants (81 652 [59.8%] women; mean [SD] age, 51.0 [9.8] years) were included in analysis. A total of 27 195 participants (19.9%) had general obesity, and 37 024 participants (27.1%) had abdominal obesity. The mean (SD) nocturnal sleep duration was 7.8 (1.4) hours, and the median (interquartile range) midsleep time was 2:15 am (1:30 am-3:00 am). A total of 19 660 participants (14.4%) had late bedtime behavior (ie, midnight or later). Compared with bedtime between 8 pm and 10 pm, late bedtime was associated with general obesity (AOR, 1.20; 95% CI, 1.12-1.29) and abdominal obesity (AOR, 1.20; 95% CI, 1.12-1.28), particularly among participants who went to bed between 2 am and 6 am (general obesity: AOR, 1.35; 95% CI, 1.18-1.54; abdominal obesity: AOR, 1.38; 95% CI, 1.21-1.58). Short nocturnal sleep of less than 6 hours was associated with general obesity (eg, <5 hours: AOR, 1.27; 95% CI, 1.13-1.43), but longer napping was associated with higher abdominal obesity prevalence (eg, ≥1 hours: AOR, 1.39; 95% CI, 1.31-1.47). Neither going to bed during the day (ie, before 8pm) nor wake-up time was associated with obesity.
Conclusions and Relevance: This cross-sectional study found that late nocturnal bedtime and short nocturnal sleep were associated with increased risk of obesity prevalence, while longer daytime napping did not reduce the risk but was associated with higher risk of abdominal obesity. Strategic weight control programs should also encourage earlier bedtime and avoid short nocturnal sleep to mitigate obesity epidemic.
METHODS: A comprehensive search was conducted in CENTRAL, MEDLINE, SCOPUS, Google Scholars, World Health Organization Trials Portal, ClinicalTrials.gov, Clinical Trial Registry of India, and AYUSH Research Portal for all appropriate trials. Randomized controlled trials that examined the effect of Ashwagandha extract versus placebo on sleep in human participants 18 years old and above were considered. Two authors independently read all trials and independently extracted all relevant data. The primary outcomes were sleep quantity and sleep quality. The secondary outcomes were mental alertness on rising, anxiety level, and quality of life.
RESULTS: A total of five randomized controlled trials containing 400 participants were analyzed. Ashwagandha extract exhibited a small but significant effect on overall sleep (Standardized Mean Difference -0.59; 95% Confidence Interval -0.75 to -0.42; I2 = 62%). The effects on sleep were more prominent in the subgroup of adults diagnosed with insomnia, treatment dosage ≥600 mg/day, and treatment duration ≥8 weeks. Ashwagandha extract was also found to improve mental alertness on rising and anxiety level, but no significant effect on quality of life. No serious side effects were reported.
CONCLUSION: Ashwagandha extract appears to has a beneficial effect in improving sleep in adults. However, data on the serious adverse effects of Ashwagandha extract are limited, and more safety data would be needed to assess whether it would be safe for long-term use.
METHODOLOGY: This research was conducted on 1210 noninstitutionalized elderly Malaysian individuals with dementia. The effects of age, ethnicity, educational level, marital status, sex differences, social support, and having a partner on sleep quality were evaluated in the respondents. The multiple logistic regression analysis was used to predict the risk of sleep disturbances among the participants.
RESULTS: Approximately, 41% of the participants experienced sleep disruption. Further findings showed that ethnicity (odds ratio [OR] = 0.62), social support (OR = 1.35), marital status (OR = 2.21), educational level (OR = 0.65), and having a partner (OR = 0.45) significantly affected sleep quality (P < .05). Sex differences and age were unrelated predictors of sleep disturbances (P > .05).
CONCLUSION: It was concluded that social isolation and being single increased sleep disruption among respondents, but having a partner and ethnic non-Malay decreased the rate of sleep problems.
Methods: This was a prospective cross-sectional study. A total of 3303 subjects aged 40 years and above from two large population-based cohorts, the Singapore Malay Eye Study-2 (n = 1191, 2011-2013) and the Singapore Indian Eye Study-2 (n = 2112, 2013-2015), were included. The presence of symptoms of dry eye was defined as having at least one of six symptoms often or all the time. Sleep questionnaires included the Epworth Sleepiness Scale, Berlin Questionnaire, STOP-bang questionnaire, and Insomnia Severity Index. Poor sleep quality was defined as meeting the respective questionnaire thresholds. General health questionnaires (including sleep duration) and standardized ocular and systemic tests were also used.
Results: Of 3303 participants, 6.4% had excessive sleepiness, 20.5% had high risk for sleep apnea, 2.7% had clinical insomnia, and 7.8% had <5 hours of sleep. These sleep factors were associated with symptoms of dry eye. After adjusting for relevant demographic, medical, and social factors, the following were associated with higher odds of symptoms of dry eye: excessive sleepiness (Epworth Sleepiness Scale: odds ratio [OR] = 1.77 [1.15-2.71]), high risk of sleep apnea (Berlin Questionnaire: OR = 1.55 [1.17-2.07], STOP-Bang Questionnaire: OR = 2.66 [1.53-4.61]), clinical insomnia (Insomnia Severity Index: OR = 3.68 [2.17-6.26]) and <5 hours of sleep (OR = 1.73 [1.17-2.57], reference sleep duration 5-9 hours). Sleep apnea, insomnia, and sleep duration were each shown to be independently associated with symptoms of dry eye.
Conclusion: Short sleep duration and poor quality are both significantly and independently associated with symptoms of dry eye.
AIMS: To develop a model to detect the respiratory phases present in the pulmonary acoustic signals and to evaluate the performance of the model in detecting the respiratory phases.
METHODS: Normalised averaged power spectral density for each frame and change in normalised averaged power spectral density between the adjacent frames were fuzzified and fuzzy rules were formulated. The fuzzy inference system (FIS) was developed with both Mamdani and Sugeno methods. To evaluate the performance of both Mamdani and Sugeno methods, correlation coefficient and root mean square error (RMSE) were calculated.
RESULTS: In the correlation coefficient analysis in evaluating the fuzzy model using Mamdani and Sugeno method, the strength of the correlation was found to be r = 0.9892 and r = 0.9964, respectively. The RMSE for Mamdani and Sugeno methods are RMSE = 0.0853 and RMSE = 0.0817, respectively.
CONCLUSION: The correlation coefficient and the RMSE of the proposed fuzzy models in detecting the respiratory phases reveals that Sugeno method performs better compared with the Mamdani method.
METHODS: Parents of 872 infants and toddlers in Japan (48.6% boys), and parents of 20 455 infants and toddlers in 11 other Asian countries/regions (48.1% boys; China, Hong Kong, India, Indonesia, Korea, Malaysia, Philippines, Singapore, Taiwan, Thailand, and Vietnam) completed an Internet-based expanded version of the Brief Infant Sleep Questionnaire.
RESULTS: Young children in Japan exhibited significantly fewer nocturnal wakings and shorter daytime sleep in comparison with other Asian countries/regions. Although the former finding was apparent in all age groups, the reduced duration of daytime sleep in Japan was not present until after 3 months of age. Interestingly, sleep problems were reported by significantly fewer parents in Japan compared with those in other Asian countries/regions, although parents in Japan reported significantly more difficulty at bedtime.
CONCLUSIONS: The short sleep duration of young children in Japan is largely due to a relatively short duration of daytime sleep. Significant differences in sleep characteristics in Japan relative to other Asian regions were found primarily after 3 months of age. Future studies should further explore the underlying causes and the potential impacts of these sleep differences.