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Social jetlag is a discordance between the social and biological rhythm and is associated with higher HbA1c, higher BMI, and higher odds of obesity. The pathways that could explain these associations are still debated. This study aims to assess the mediating role of several lifestyle factors in the cross-sectional association between social jetlag and BMI. We used cross-sectional data from 1784 adults from urban areas in the Netherlands, collected in 2019. Social jetlag (difference in midpoint of sleep between week and weekend nights) was categorized as low(<1 h), moderate(1−2 h), and high(>2 h). BMI(kg/m2) was calculated from self-reported height and weight. The association between social jetlag and BMI was assessed using linear regression, adjusted for sex, age, education, and sleep duration and stratified for the effect modifier stress (high vs. low). Mediation analysis was performed for self-reported smoking, physical activity, alcohol consumption, and adherence to a healthy diet. High social jetlag was associated with higher BMI (0.69 kg/m2,95%CI 0.05;1.33). This association was stronger in people with high stress (0.93 kg/m2,95%CI 0.09;1.76). Social jetlag was also associated with higher odds of smoking, lower physical activity, higher alcohol consumption, and lower healthy diet adherence. In people with high stress, these factors mediated 10–15% of the association between social jetlag and BMI. Social jetlag is associated with higher BMI and this association is stronger in people with high stress. In people with high stress, healthy diet adherence mediated 12% of this association. Other pathways involved in this association should be further investigated. • Social jetlag is associated with higher BMI in adults. • Social jetlag is associated with several unhealthy lifestyle factors. • Diet significantly mediates 12% of the association between social jetlag and BMI. • Other pathways involved in this association should be further investigated. [ABSTRACT FROM AUTHOR]

This study aimed to determine the longitudinal associations between self-reported sleep duration and cardiometabolic disease (CMD) risk in corporate executives. Self-reported sleep duration and lifestyle, occupational, psychological, and anthropometrical, blood pressure and blood marker variables were obtained from 1512 employees at annual health risk assessments in South Africa between 2016 and 2019. Gender-stratified linear mixed models, adjusting for age, lifestyle, occupational and psychological covariates were used to explore these longitudinal associations. Among women, shorter sleep duration was associated with higher body mass index (BMI) covarying for age only (ß with 95% confidence intervals: −0.19 [−0.36, −0.03]), age and occupational factors (−0.20 [−0.36, −0.03]) and age and psychological factors (−0.20 [−0.37, −0.03]). Among men, shorter sleep was associated with both BMI and waist circumference (WC) covarying for age only (BMI: −0.15 [−0.22; −0.08]; WC: −0.62 [−0.88; −0.37]); age and lifestyle factors (BMI: −0.12 [−0.21; −0.04]); WC: −0.016 [−0.92; −0.29], age and occupational factors (BMI: −0.20 [−0.22; 0.08]; WC: −0.62 [−0.88; −0.36]), and age and psychological factors (BMI: −0.15 [−0.22; −0.07]; WC: −0.59 [−0.86; −0.33]). Among men, shorter sleep was also longitudinally associated with higher CMD risk scores in models adjusted for age and lifestyle factors (CMD: −0.12 [−0.20; −0.04]) and age and psychological factors (CMD: −0.08 [−0.15; −0.01]). Corporate executives who report shorter sleep durations may present with poorer CMD risk profiles, independent of age, lifestyle, occupational and psychological factors. Addressing sleep health in workplace health programmes may help mitigate the development of CMD in such employees. • Sleep duration and cardiometabolic disease risk were assessed. • Short sleep duration was associated with increased cardiometabolic disease risk. • Potential confounders were included in longitudinal mixed models. • Workplaces could include sleep health to manage cardiometabolic disease. [ABSTRACT FROM AUTHOR]

Abstract: Objective. : The aim of the study was to investigate the impact of a playground redesign intervention across time on children''s recess physical activity levels using combined physical activity measures and to evaluate the potential influence of covariates on the intervention effect. Method. : Fifteen schools located in areas of high deprivation in one large city in England each received £20,000 through a national £10 million Sporting Playgrounds Initiative to redesign the playground environment based on a multicolored zonal design. Eleven schools served as matched socioeconomic controls. Physical activity levels during recess were quantified using heart rate telemetry and accelerometry at baseline, 6 weeks and 6 months following the playground redesign intervention. Data were collected between July 2003 and January 2005 and analyzed using multilevel modeling. Results. : Statistically significant intervention effects were found across time for moderate-to-vigorous and vigorous physical activity assessed using both heart rate and accelerometry. Conclusions. : The results suggest that a playground redesign, which utilizes multicolor playground markings and physical structures, is a suitable stimulus for increasing children''s school recess physical activity levels. [Copyright &y& Elsevier]

Background. From a preventive point of view, it is especially important to investigate the life style risk factors connected to cardiovascular disease (CVD) biological risk factors clustering. The purposes of this study were (I) to determine in pediatric population the coexistence of biological cardiovascular disease risk factors (high blood pressure (HBP), percentage of high fat mass (%HFM), and high total cholesterol (HTC)) and (II) to study the relationship between low physical activity, a lifestyle risk factor, with those biological risk factors.Methods. This study comprised 1461 children (699 males and 762 females) aged between 8 and 15 years old. The following measurements were carried out: blood pressure, percentage of fat mass, total cholesterol (TC), and physical activity. The quartiles, adjusted for age and gender, were the criterion used to classify the subjects at risk. Odds ratios and confidence intervals at 95% were used to study HBP, HTC, and %HFM clustering in both genders. Multinomial logistic regression was used to investigate the relationship between clustering and physical activity index (PAI).Results. The findings of this study indicated that there is clustering for the biological risk factors, specially between HBP and %HFM for females. It can be observed that about half of the subjects had at least one biological risk factor.Conclusions. The results of the present study suggest that children and adolescents with higher PAI have a lower number of biological risk factors for CVD. [Copyright &y& Elsevier]

Targeted screening for childhood high blood pressure may be more feasible than routine blood pressure measurement in all children to avoid unnecessary harms, overdiagnosis or costs. Targeting maybe based e.g. on being overweight, but information on other predictors may also be useful. Therefore, we aimed to develop a multivariable diagnostic prediction model to select children aged 9-10 years for blood pressure measurement. Data from 5359 children in a population-based prospective cohort study were used. High blood pressure was defined as systolic or diastolic blood pressure ≥ 95th percentile for gender, age, and height. Logistic regression with backward selection was used to identify the strongest predictors related to pregnancy, child, and parent characteristics. Internal validation was performed using bootstrapping. 227 children (4.2%) had high blood pressure. The diagnostic model included maternal hypertensive disease during pregnancy, maternal BMI, maternal educational level, parental hypertension, parental smoking, child birth weight standard deviation score (SDS), child BMI SDS, and child ethnicity. The area under the ROC curve was 0.73, compared to 0.65 when using only child overweight. Using the model and a cut-off of 5% for predicted risk, sensitivity and specificity were 59% and 76%; using child overweight only, sensitivity and specificity were 47% and 84%. In conclusion, our diagnostic prediction model uses easily obtainable information to identify children at increased risk of high blood pressure, offering an opportunity for targeted screening. This model enables to detect a higher proportion of children with high blood pressure than a strategy based on child overweight only. [ABSTRACT FROM AUTHOR]