Sedentary lifestyle and low physical activity are associated with health issues, including both physical and mental health, non-communicable diseases, overweight, obesity and reduced quality of life. This study investigated differences in physical activity and other individual factors among different occupational groups, highlighting the impact of sedentary behaviour on perceived stress by occupation. Cross-sectional study included 571 full-time workers of Kaunas city, Lithuania. The outcome of this study was assessment of perceived stress. Time spent sedentary per day, occupation and other individual characteristics were self-reported using questionnaires. Two main occupational groups were analysed: white-collar and blue-collar workers. Multivariate logistic regression was used to assess the impact of sedentary behaviour on perceived stress among different occupational groups. The prevalence of high sedentary behaviour was 21.7 and 16.8 % among white-collar and blue-collar workers, respectively. Blue-collar workers had a higher risk of high perceived stress (OR 1.55, 95% CI 1.05–2.29) compared to white-collar workers; however, sedentary time did not have any impact on high perceived stress level. Meanwhile, white-collar male (OR 4.34, 95% CI 1.46–12.95) and white-collar female (OR 3.26, 95% CI 1.23–8.65) workers who spend more than three hours per day sedentary had a greater risk of high levels of perceived stress. These findings indicate sedentary behaviour effect on perceived stress among two occupational groups—white-collar and blue-collar workers—and other important factors associated with perceived stress.
Background
Physical activity (PA) has been declining dramatically over time in many countries worldwide. The decrease of PA levels affects a person’s health and quality of life as it is a significant risk factor for many noncommunicable diseases. Understanding the factors that determine PA is particularly important in promoting greater PA in adults and reducing the risk of diseases associated with physical inactivity. This study investigated associations of seasonal PA levels with socioeconomic and health factors among adults.
Methods
A cross-sectional study included 1111 participants of Kaunas city, Lithuania who completed a questionnaire about PA and mobility behaviour, socioeconomic, health and demographic factors. Commuting PA and sufficient PA (sPA) on weekdays and weekends in the summer and winter seasons was investigated in this study. Data on daily commuting duration and forms of transportation were collected using a questionnaire survey. Daily commuting was categorized into two categories: 1) using motorized transportation or walking or cycling 0 to 29 min, 2) and walking or cycling for 30 min or more.
Results
Our findings showed significant seasonal impact on PA levels. The results revealed that employment status was significantly associated with PA. Unemployed individuals were 2 times more likely to engage in sPA in winter and almost 3 times in summer compared to workers.
Conclusions
Our findings suggest the importance of considering environmental, socioeconomic and health factors when assessing PA. Promoting PA through active commuting is an important part of a healthy lifestyle and strategies to support the implementation of health-promoting policies and practices are needed.
In many countries, road traffic is one of the main sources of air pollution associated with adverse effects on human health and environment. Nitrogen dioxide (NO2) is considered to be a measure of traffic-related air pollution, with concentrations tending to be higher near highways, along busy roads, and in the city centers, and the exceedances are mainly observed at measurement stations located close to traffic. In order to assess the air quality in the city and the air pollution impact on public health, air quality models are used. However, firstly, before the model can be used for these purposes, it is important to evaluate the accuracy of the dispersion modelling as one of the most widely used method. The monitoring and dispersion modelling are two components of air quality monitoring system (AQMS), in which statistical comparison was made in this research. The evaluation of the Atmospheric Dispersion Modelling System (ADMS-Urban) was made by comparing monthly modelled NO2 concentrations with the data of continuous air quality monitoring stations in Kaunas city. The statistical measures of model performance were calculated for annual and monthly concentrations of NO2 for each monitoring station site. The spatial analysis was made using geographic information systems (GIS). The calculation of statistical parameters indicated a good ADMS-Urban model performance for the prediction of NO2. The results of this study showed that the agreement of modelled values and observations was better for traffic monitoring stations compared to the background and residential stations.
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