Changes of Self-Rated Health Status, Overweight and Physical Activity During Childhood and Adolescence-The Ratchet Effect of High Parental Socioeconomic Status

Primary school children's health and its association with physical fitness development and health-related factors

The health status (HS) of children is influenced by a variety of factors, including physical fitness (PF) or social and environmental characteristics. We present a 4-year longitudinal study carried out with 263 primary school children. PF was assessed yearly using the German Motor Performance Test 6-18. Demographic data, leisure time behavior and socioeconomic factors were collected using questionnaires for children and parents. Based on parents' ratings in year 4, children were categorized as either "very good health status" (VGHS) or "good health status or below" (GHSB). Children with VGHS (73%) showed a larger improvement of global PF (p < 0.001), a significantly higher proportion of being/playing outside (p < 0.001), significantly lower proportions of overweight (p < 0.001), of media availability in the bedroom (p = 0.011) and of daily media consumption > 2 h (p = 0.033) compared to children with GHSB. Regarding socio-economic factors, children with VGHS revealed significantly fewer parents with lower education (p = 0.002), lower physical activity levels (p = 0.030) and lower migration background (p < 0.001). Physical fitness (p = 0.019) and outdoors exercising (p = 0.050) were the only variables to provide significantly higher chances of perceiving one's own health as very good when tested within a complex model including all the variables studied in this work. Considering the little focus on PF in the current Austrian physical education curriculum and the favorable environmental features of the Tyrolean region, more emphasis should be given to promoting didactical and pedagogical approaches that allow schoolers to be active in the nature.

Study of the Economic, Environmental, and Social Factors Affecting Chinese Residents' Health Based on Machine Learning

The Healthy China Strategy puts realistic demands for residents' health levels, but the reality is that various factors can affect health. In order to clarify which factors have a great impact on residents' health, based on China's provincial panel data from 2011 to 2018, this paper selects 17 characteristic variables from the three levels of economy, environment, and society and uses the XG boost algorithm and Random forest algorithm based on recursive feature elimination to determine the influencing variables. The results show that at the economic level, the number of industrial enterprises above designated size, industrial added value, population density, and per capita GDP have a greater impact on the health of residents. At the environmental level, coal consumption, energy consumption, total wastewater discharge, and solid waste discharge have a greater impact on the health level of residents. Therefore, the Chinese government should formulate targeted measures at both economic and environmental levels, which is of great significance to realizing the Healthy China strategy.