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: The SAQ was translated from English to Turkish using the back-translation method. It contains 19 questions scored from 1 to either 5 or 6 in 5 domains (physical limitation, angina stability, angina frequency, disease perception, and treatment satisfaction). Cronbach's alpha coefficient was used to evaluate internal consistency. Spearman's rank correlation coefficient was calculated to assess the construct validity. Convergent validity was examined using correlations between the SAQ and the MacNew Heart Disease Health-related Quality of Life Questionnaire (MacNew) and the Nottingham Health Profile. Divergent validity was evaluated using correlations between the SAQ and age, body mass index (BMI), gender, and the marital status of patients. A value of p<0.05 was considered statistically significant.
RESULTS: Sixty-seven patients were enrolled in the study. The mean age of the study patients was 58.7 years (SD: 10.2). Cronbach's alpha scores of the SAQ, ranging in value from 0.715 to 0.910, demonstrated that this scale is reliable. All of the SAQ scales had a significant correlation with all of the MacNew scales, which indicated that the scale has convergent validity. Insignificant correlations with age, BMI, gender, and marital status illustrated the good divergent validity of the scale.
CONCLUSION: The Turkish version of the SAQ is a valid and reliable instrument. It is a useful and practical tool to evaluate patients with angina and CHD.
METHODS: A 17-item questionnaire was developed to assess nutrition practices and administered to dialysis managers of 150 HD centers, identified through the National Renal Registry. Nutritional outcomes of 4362 patients enabled crosscutting comparisons as per dietitian accessibility and center sector.
RESULTS: Dedicated dietitian (18%) and visiting/shared dietitian (14.7%) service availability was limited, with greatest accessibility at government centers (82.4%) > non-governmental organization (NGO) centers (26.7%) > private centers (15.1%). Nutritional monitoring varied across HD centers as per albumin (100%) > normalized protein catabolic rate (32.7%) > body mass index (BMI, 30.7%) > dietary intake (6.0%). Both sector and dietitian accessibility was not associated with achieving albumin ≥40 g/L. However, NGO centers were 36% more likely (p = 0.030) to achieve pre-dialysis serum creatinine ≥884 μmol/L compared to government centers, whilst centers with dedicated dietitian service were 29% less likely (p = 0.017) to achieve pre-dialysis serum creatinine ≥884 μmol/L. In terms of BMI, private centers were 32% more likely (p = 0.022) to achieve BMI ≥ 25.0 kg/m2 compared to government centers. Private centers were 62% less likely (p
METHODS: In this cross-sectional, hospital-based study, anthropometric measurements [weight, length/height, mid-upper arm circumference (MUAC), triceps skinfold thickness were performed in 285 children aged from 3 months to 15 years who were admitted to University Malaya Medical Centre, Kuala Lumpur in November 2013. Acute (wasting) and chronic (stunting) undernutrition were defined as weight-for-height (WFH) and height-for-age (HFA) < -2 standard deviation (S.D.), respectively. Underweight was defined as weight-for-age < -2 S.D. For children aged between 1 and 5 years of age, World Health Organization definition for acute undernutrition (HFA mass index < -2 S.D., and 7% (n = 21) had triceps skinfold thickness < -2 S.D., while 17% (n = 47) were underweight. Using the World Health Organization definition of acute undernutrition, an additional eight patients were noted to have acute undernutrition (n = 40, 14%). No significant risk factors associated with undernutrition were identified.
CONCLUSION: The prevalence of undernutrition among children admitted to a tertiary hospital in Malaysia was 14%. Strategies for systematic screening and provision of nutritional support in children at risk of undernutrition as well as treatment of undernutrition in children requiring hospitalization are needed.