Explore the vast range of titles available.

Cardiovascular diseases (CVD) mortality has been shown to follow a seasonal pattern. Several studies suggested several possible determinants of this pattern, including misclassification of causes of deaths. We aimed at assessing seasonality in overall, CVD, cancer and non-CVD/non-cancer mortality using data from 19 countries from different latitudes. Monthly mortality data were compiled from 19 countries, amounting to over 54 million deaths. We calculated ratios of the observed to the expected numbers of deaths in the absence of a seasonal pattern. Seasonal variation (peak to nadir difference) for overall and cause-specific (CVD, cancer or non-CVD/non-cancer) mortality was analyzed using the cosinor function model. Mortality from overall, CVD and non-CVD/non-cancer showed a consistent seasonal pattern. In both hemispheres, the number of deaths was higher than expected in winter. In countries close to the Equator the seasonal pattern was considerably lower for mortality from any cause. For CVD mortality, the peak to nadir differences ranged from 0.185 to 0.466 in the Northern Hemisphere, from 0.087 to 0.108 near the Equator, and from 0.219 to 0.409 in the Southern Hemisphere. For cancer mortality, the seasonal variation was nonexistent in most countries. In countries with seasonal variation, mortality from overall, CVD and non-CVD/non-cancer show a seasonal pattern with mortality being higher in winter than in summer. Conversely, cancer mortality shows no substantial seasonality.

BackgroundObesity is a known risk factor for hypertension, but the extent to which physical activity mediates this relationship remains unclear.MethodsCross-sectional data from medical exams of Swiss Armed Forces conscripts between 2007 and 2022 (N=382 583). Physical fitness was assessed via the Conscription Physical Test (CPT), which included five components, one of which was an endurance test (ET). Both CPT and ET results were categorised as ‘fit’ or ‘unfit’. Weight status was classified based on body mass index (BMI) into normal weight, overweight (BMI 25–29.99) and obesity (BMI≥30 kg/m²). Hypertension was defined as ≥140/90 mm Hg. The study explored CPT and ET as mediators between BMI and hypertension.Results20.6% of the conscripts had hypertension, 20.4% overweight and 4.5% obese. Conscripts with overweight or obesity had a higher risk of hypertension compared with normal weight (OR and (bias-corrected 95% CI) of natural direct effect: 1.803 (1.766 to 1.845) and 2.727 (2.570 to 2.865)), with a protective effect of being fit (natural indirect effect for CPT: 0.976 (0.971 to 0.982) and 0.917 (0.881 to 0.953)). When ET was assessed, similar findings were obtained: 1.765 (1.731 to 1.804) and 2.680 (2.482 to 2.887) for overweight and obesity, with a protective effect of being fit (0.991 (0.983 to 0.999) and 0.925 (0.861 to 0.991)).ConclusionsMale Swiss conscripts with overweight and obesity face an increasing risk of hypertension, with the protective benefit of physical fitness showing an increasing trend as BMI increases.

Several raw-data processing software for accelerometer-measured physical activity (PA) exist, but whether results agree has not been assessed. We examined the agreement between three different software for raw accelerometer data, and associated their results with cardiovascular risk. A cross-sectional analysis conducted between 2014 and 2017 in 2693 adults (53.4% female, 45–86 years) living in Lausanne, Switzerland was used. Participants wore the wrist-worn GENEActive accelerometer for 14 days. Data was processed with the GENEActiv manufacturer software, the Pampro package in Python and the GGIR package in R. For the latter, two sets of thresholds “White” and “MRC” defining levels of PA and two versions (1.5–9 and 1.11–1) for the “MRC” threshold were used. Cardiovascular risk was assessed using the SCORE risk score. Time spent (mins/day) in stationary, light, moderate and vigorous PA ranged from 633 (GGIR-MRC) to 1147 (Pampro); 93 (GGIR-White) to 196 (GGIR-MRC); 19 (GGIR-White) to 161 (GENEActiv) and 1 (GENEActiv) to 26 (Pampro), respectively. Spearman correlations between results ranged between 0.317 and 0.995, while concordance coefficients ranged between 0.035 and 0.968. With some exceptions, the line of perfect agreement was not in the 95% confidence interval of the Bland–Altman plots. Compliance to PA guidelines varied considerably: 99.8%, 98.7%, 76.3%, 72.6% and 50.2% for Pampro, GENEActiv, GGIR-MRC v.1.11–1, GGIR-MRC v.1.4–9 and GGIR-White, respectively. Cardiovascular risk decreased with increasing time spent in PA across most software packages. We found large differences in PA estimation between software and thresholds used, which makes comparability between studies challenging.

Evidence suggests that the built environment can influence the intensity of physical activity. However, despite the importance of the geographic context, most of the studies do not consider the spatial framework of this association. We aimed to assess individual spatial dependence of objectively measured moderate and vigorous physical activity (MVPA) and describe the characteristics of the built environment among spatial clusters of MVPA. Cross-sectional data from the second follow-up (2014-2017) of CoLaus|PsyCoLaus, a longitudinal population-based study of the Lausanne area (Switzerland), was used to objectively measure MVPA using accelerometers. Local Moran's I was used to assess the spatial dependence of MVPA and detect geographic clusters of low and high MVPA. Additionally, the characteristics of the built environment observed in the clusters based on raw MVPA and MVPA adjusted for socioeconomic and demographic factors were compared. Data from 1,889 participants (median age 63, 55% women) were used. The geographic distribution of MVPA and the characteristics of the built environment among clusters were similar for raw and adjusted MVPA. In the adjusted model, we found a low concentration of individuals within spatial clusters of high MVPA (median: 38.5mins; 3% of the studied population) and low MVPA (median: 10.9 mins; 2% of the studied population). Yet, clear differences were found in both models between clusters regarding the built environment; high MVPA clusters were located in areas where specific compositions of the built environment favor physical activity. Our results suggest the built environment may influence local spatial patterns of MVPA independently of socioeconomic and demographic factors. Interventions in the built environment should be considered to promote physically active behaviors in urban areas.

To evaluate the association of objective physical activity (PA) and sedentary behavior (SB) with sleep duration and quality. Cross-sectional study including 2649 adults (53.5% women, 45-86 years) from the general population. Proportions of time spent in PA and SB were measured using 14 day accelerometry. Low PA and high SB statuses were defined as the lowest and highest tertile of each behavior. \"Inactive,\" \"Weekend warrior,\" and \"Regularly active\" weekly patterns were also defined. Sleep parameters were derived from the accelerometer and validated questionnaires. High PA, relative to low PA, was associated with higher sleep efficiency (76.6 vs. 73.8%, p < 0.01) and lower likelihood of evening chronotype [relative-risk ratio (RR) and 95% CI: 0.71 (0.52; 0.97)]. Similar associations were found for low SB relative to high SB. \"Weekend warriors\" relative to \"Inactives,\" had higher sleep efficiency [76.4 vs. 73.9%, p < 0.01] and lower likelihood of evening chronotype [RR: 0.63 (0.43; 0.93)]. \"Regularly actives,\" relative to \"Inactives,\" had higher sleep efficiency [76.7 vs. 73.9%, p < 0.01] and tended to have less frequently an evening chronotype [RR: 0.75 (0.54; 1.04), p = 0.09]. No associations were found for PA and SB with sleep duration, daytime sleepiness, insomnia, and risk of sleep apnea (after adjustment for body mass index). High PA and low SB individuals, even if they do not sleep longer, have higher sleep efficiency and have less frequently an evening chronotype.

PurposePhysical activity (PA) has been shown to influence salivary cortisol concentrations in small studies conducted among athletes. We assessed the association of activity status and patterns with salivary cortisol in the general population.MethodsCross-sectional study including 1948 adults (54.9% women, 45–86 years). PA and sedentary behaviour (SB) were measured for 14 days by accelerometry. Low PA and high SB status were defined, respectively, as the lowest and highest tertile of each behaviour. ‘Inactive’, ‘Weekend warrior’, and ‘Regularly active’ patterns were also defined. Four salivary cortisol samples were collected over a single day and the following parameters were calculated: area under the curve to ground (AUCg), awakening response (CAR) and diurnal slope.ResultsAfter multivariable adjustment, low SB remained associated to steeper slopes relative to high SB (− 1.54 ± 0.03 vs. − 1.44 ± 0.04 nmol/l per hour). Non-significant trends were found for high PA relative to low PA with steeper slopes (− 1.54 ± 0.03 vs. − 1.45 ± 0.04) and lower AUCg (208.7 ± 2.0 vs. 215.9 ± 2.9 nmol.h/l). Relative to ‘Inactives’, ‘Regularly actives’ had lower AUCg (205.4 ± 2.4 vs. 215.5 ± 2.9) and ‘Weekend warriors’ had steeper slopes (− 1.61 ± 0.05 vs. − 1.44 ± 0.04). No associations were found for CAR.ConclusionLow SB and high PA are related to lower cortisol secretion as measured by different parameters of salivary cortisol, but the effects were only modest.

Introduction Evidence suggests that the built environment can influence the intensity of physical activity. However, despite the importance of the geographic context, most of the studies do not consider the spatial framework of this association. We aimed to assess individual spatial dependence of objectively measured moderate and vigorous physical activity (MVPA) and describe the characteristics of the built environment among spatial clusters of MVPA. Methods Cross-sectional data from the second follow-up (2014–2017) of CoLaus|PsyCoLaus, a longitudinal population-based study of the Lausanne area (Switzerland), was used to objectively measure MVPA using accelerometers. Local Moran’s I was used to assess the spatial dependence of MVPA and detect geographic clusters of low and high MVPA. Additionally, the characteristics of the built environment observed in the clusters based on raw MVPA and MVPA adjusted for socioeconomic and demographic factors were compared. Results Data from 1,889 participants (median age 63, 55% women) were used. The geographic distribution of MVPA and the characteristics of the built environment among clusters were similar for raw and adjusted MVPA. In the adjusted model, we found a low concentration of individuals within spatial clusters of high MVPA (median: 38.5mins; 3% of the studied population) and low MVPA (median: 10.9 mins; 2% of the studied population). Yet, clear differences were found in both models between clusters regarding the built environment; high MVPA clusters were located in areas where specific compositions of the built environment favor physical activity. Conclusions Our results suggest the built environment may influence local spatial patterns of MVPA independently of socioeconomic and demographic factors. Interventions in the built environment should be considered to promote physically active behaviors in urban areas.

ObjectiveTo assess the seasonality of cardiovascular risk factors (CVRF) in a large set of population-based studies.MethodsCross-sectional data from 24 population-based studies from 15 countries, with a total sample size of 237979 subjects. CVRFs included Body Mass Index (BMI) and waist circumference; systolic (SBP) and diastolic (DBP) blood pressure; total, high (HDL) and low (LDL) density lipoprotein cholesterol; triglycerides and glucose levels. Within each study, all data were adjusted for age, gender and current smoking. For blood pressure, lipids and glucose levels, further adjustments on BMI and drug treatment were performed.ResultsIn the Northern and Southern Hemispheres, CVRFs levels tended to be higher in winter and lower in summer months. These patterns were observed for most studies. In the Northern Hemisphere, the estimated seasonal variations were 0.26kg/m2 for BMI, 0.6cm for waist circumference, 2.9mmHg for SBP, 1.4mmHg for DBP, 0.02mmol/L for triglycerides, 0.10mmol/L for total cholesterol, 0.01mmol/L for HDL cholesterol, 0.11mmol/L for LDL cholesterol, and 0.07mmol/L for glycaemia. Similar results were obtained when the analysis was restricted to studies collecting fasting blood samples. Similar seasonal variations were found for most CVRFs in the Southern Hemisphere, with the exception of waist circumference, HDL, and LDL cholesterol.ConclusionsCVRFs show a seasonal pattern characterised by higher levels in winter, and lower levels in summer. This pattern could contribute to the seasonality of CV mortality.