Impact of the 1.5m society on health and lifestyle of different SES groups

Social distancing is one of the most prominent measures taken in the Netherlands and other countries to prevent the spread of the coronavirus (SARS-CoV-2) that causes the COVID-19 disease. The social distancing has a significant societal impact, leading to the “1.5 m society”. However, the current physical and social environment in the Netherlands is not built for this 1.5m society, therefore the social distancing measures affect physical activity levels, increase social isolation and cause mental health problems in the population. Because inactive lifestyle is the strongest predictor of early mortality (Lee et al., 2012; Ekelund et al., 2016; Healy et al., 2015; Hartman et al., 2017), the 1.5 m society not only suffers increased mortality due to COVID-19 but also can experience increases in cardiovascular diseases, diabetes, and mental health issues in the future. 
In a recent pilot study using data from the Nijmegen Exercise Study (NES), it was shown that during the “intelligent lockdown” in the spring of 2020, physical activity levels decreased by approximately 20% compared to 2019. Specifically, the activity levels decreased more for people living in small houses compared to those living in larger houses. The long term effects of this lifestyle change on weight, risk factors and health are unknown. Moreover, the impact likely differs between different socioeconomic status (SES) groups. Lifelines Corona Barometer also reports increases in depressive symptoms and anxiety during the periods of lockdown, suggesting that mental health is especially affected by the 1.5 m society. Considering that low SES individuals are already at a higher risk for various health problems, the 1.5m society is likely to exacerbate these issues. 
Within this project, our goal is to study the impact of the 1.5m society on the physical activity and health of different SES groups in relation to location characteristics, such as the neighborhood SES. This will allow to identify the groups and areas with the highest impact, where interventions might help to limit the health consequences.