Objective To explore the interaction of sleep quality and depression among patients with type 2 diabetes. Methods With multistage cluster sampling, all the participants were interviewed with self-designed questionnaire, diabetes-specific quality of life scale and self-rating depression scale, and Pittsburgh sleep quality index scale. Chi-square test was used for qualitative data. Risk factors were analyzed by means of multiple linear regression or logistic regression model. The indicator of interaction was calculated according to the delta method. Results There were 944 residents involved in the final analysis, including 365 males and 579 females. The average age was (64. 0 ± 10. 2) years. Compared with patients with type 2 diabetes mellitus( T2DM) who had good sleep quality and no depression symptoms, the risk of quality of life in those with good sleep quality but depression was 2. 75 (95% CI 1. 59-4. 77); and the risk of quality of life in those with poor sleep quality and no depression was 1. 55(95%CI 1. 03-2. 33). The risk of quality of life in those with poor sleep quality and depression was 4. 97(95% CI 2. 34-9. 63). Due to poor sleep quality and depression in patients with T2DM the combined interaction index was 2. 48 (95% CI 1. 44-4. 29), the relative excess risk was 3. 42(95% CI 2. 16-4. 67), and the attributable proportion was 0. 51(95% CI 0. 32-0. 70). Conclusion A combined interaction of poor sleep quality and depression in affecting the quality of life was found in type 2 diabetic patients. When both factors existed at the same time, the interaction effect of these 2 factors was greater than the single one.

Objective: To analyze the relationship between family behaviors and overweight/obesity in primary and junior school students aged 6-14 years in Xuzhou, and to provide a reference for a targeted measure to prevent and control overweight and obesity. Methods: Using multistage stratified cluster random sampling, a total of 6 220 students aged 6-14 years old from 10 primary schools and 10 junior schools were investigated by a self-designed questionnaire. Chi-square and multivariate Logistic regression models were used to explore the relationship between family behaviors and overweight/obesity in primary and junior school students. Results: The rate of overweight/obesity in primary and junior boys was higher than that in primary and junior girls. The rate of overweight/obesity in urban students was higher than that of rural students(P<0.05). The Chi-square analysis showed that overweight of parents, irregular breakfast, eating fast food, eating sweets, drinking sweetened beverage, long screen time and short sleep duration were risk family behavior factors of overweight/obesity in primary and junior boy students(P<0.05). The risk family behavior factors of overweight/obesity in primary and junior girl students were overweight of parents, irregular breakfast, eating fast food and eating sweets(P<0.05). The risk family behavior factors of overweight/obesity, such as drinking sweetened beverage and short sleep duration, were also related to primary girls(P<0.05), and long screen time was related to junior girls(P<0.05). The multivariate Logistic regression showed that such family behavior factors as irregular breakfast(OR-boy=1.58, OR-girl=1.74), eating fast food(OR-boy=1.37, OR-girl=1.11), eating sweets(OR-boy=1.85, OR-girl=1.52), drinking sweetened beverage(OR-boy=1.64, OR-girl=1.33) and short sleep duration(OR-boy=1.56, OR-girl=1.69) were positively correlated with the prevalence of overweight/obesity in primary students. Long screen time was also correlated to overweight/obesity primary boy students(OR=1.18). Family behavior factors for child overweight and obesity induded overweight of parents(OR-boy=1.29, OR-girl=1.23) and eating sweets(OR-boy=1.44, OR-girl=1.51). Irregular breakfast(OR=1.51), eating fast food(OR=1.22), drinking sweetened beverage (OR=1.75) and long visual screen time (OR=1.15) were also positively correlated with the prevalence of overweight/obesity in junior boy students. Conclusion Family behavior factors were positively correlated with the prevalence of overweight/obesity in primary and junior students. The influence of family behavior factors were different between primary and junior students. Behavioral interventions based on family should be adopted to prevent and control the overweight/obesity of children.

Objective To analyze the relationship between sleep duration, screen viewing time, and the prevalence of overweight/obesity among primary school students in Xuzhou. Methods Using a cluster sampling method, a total of 3 228 students (including 1 679 boys and 1 549 girls with an average age of 10.78±0.69 years) from grade one to six from 10 primary schools in Xuzhou underwent interview using a self?designed questionnaire containing basic characteristics, sleep duration, and screen viewing time. Data on height and weight were also collected. The relationship between sleep duration, screen viewing time, and overweight/obesity was analyzed using a logistic regression analysis. Results The prevalence rates of overweight among boys and girls were 16.56% and 11.94%, respectively (χ2=13.59, P<0.05). The prevalence rates of obesity among boys and girls were 14.47% and 10.07%, respectively (χ2=14.01, P<0.05). In total, 74.41% students reported a lack of sleep; the average sleeping time was (9.24±1.07) h. The average sleeping time among boys was (9.35 ± 1.12) h and among girls was (9.13 ± 1.03) h. The difference in sleep duration between boys and girls was significant (t=5.79, P<0.05). The differences in sleep duration and overweight/obesity were significant between both boys (χ2=18.62, P<0.05) and girls (χ2=21.14, P<0.05). Regarding screen viewing time, 17.29% of students spent more than 2 hours per day viewing a screen. The difference in screen viewing time between boys and girls was significant (Z=3.02, P=0.014). The proportion of children with screen viewing time of more than 2 h/d among overweight/obese and healthy weight male students was 29.50% (82/278) and 22.56% (316/1401), respectively, which was significantly different (χ2=6.18, P=0.01). However, there was no significant difference when examining the same groups among girls (12.98% (24/185;obese/overweight) vs . 9.97% (136/1364; healthy weight); χ2=1.59, P=0.21). After adjusting for parental obesity, eating sweets, and physical activity, logistic regression analysis showed that students who had a sleep duration less than 10 h/d had an odds ratio of 1.4 (95% CI: 1.15-1.71), the odds ratio for boys and girls was 1.56 (95% CI: 1.13-2.14) and 1.69 (95% CI: 1.15-2.46). The students who had a screen viewing time of more than 2 h/d had an odds ratio of 1.14 (95% CI: 1.05-1.80); the odds ratio for boys in this group was 1.18 (95% CI: 1.03-1.67). Conclusion Short sleep duration is a risk factor for being overweight/obese in both boys and girls. However, long screen viewing times were associated with being overweight/obese only in boys.

OBJECTIVETo explore the effects related to quality and duration of sleep and their interactions on the prevalence of type-2 diabetes (T2DM).

METHODS9 622 people aged 18 years and over were recruited for our cross-sectional study during March 2013 to May 2013. Unconditional logistic regression was used to analyze the relationship between quality and duration of sleep on T2DM. Bootstrap was used to calculate the relative excess risk of interaction (RERI), the attributable proportion (AP) of interaction and the synergy index (SI). 95% confidence intervals (CI) of RERI, AP and SI were estimated.

RESULTSConcerning the comparison between cases and controls on both individual and total scores, other scores were all significantly different (P < 0.01), except for two items (time for falling asleep and drugs for hypnosis). The prevalence of T2DM in volunteers with poor sleeping quality was higher than that in volunteers with good sleeping quality (P < 0.01). Individuals with sleep duration <6 hours had a higher prevalence of T2DM, when compared with individuals with sleep duration of 6-8 hours (P < 0.01). After adjusting for age, gender, level of education, occupation, family history of diabetes, status on cigarette smoking, alcohol intake, physical activities and body mass index (BMI), the prevalence of T2DM appeared the highest in those with poor sleeping quality and short duration (OR = 4.78, 95% CI:3.32-6.99; P < 0.01), when compared with those who had good sleep quality and 6-8 h sleep duration. The risk of T2DM still increased in people who had poor sleep or long duration (OR = 1.92, 95% CI:1.18-3.31; P < 0.01). Values of RERI, AP and SI (with 95% CI) were 2.33 (1.23-8.79), 0.67(0.21-0.83) and 6.87 (2.33-10.75), respectively, for the interaction between poor sleep quality and short sleep duration, while 0.33 (-0.12-1.13), 0.17 (-0.03-0.51), 1.56 (0.76-2.74) for the interaction between good sleep quality and long sleep duration.

CONCLUSIONOur results suggested that there were additive interactions between poor quality and shorter duration of sleep.