National economies are increasingly facing the challenge of having to finance the prevention and treatment of human diseases, and of having to compensate for the resulting loss of economic production. Physical inactivity is demonstrably closely related to the risk of developing certain disease group. Physical inactivity results in direct and indirect burdens that the present study intends to quantify in Hungary for the period between 2005 and 2017. Based on the data of the Hungarian public finances, this study determines the direct and indirect costs incurred by Hungary due to illnesses, and, through the PAR method, it quantifies the financial burden of physical inactivity incurred by the Hungarian Treasury. The total financial burden of illnesses in Hungary showed a decreasing tendency from 2005 to 2017, even though the year 2017 saw an increase in costs compared to 2014. Similarly, while total public expenditure on illnesses associated with physical inactivity increased by 2017 when compared to 2009, the total amount attributable to medical conditions stemming from physical inactivity still showed a decrease of 2 billion HUF in the overall period. The biggest economic burden is posed by cardiovascular diseases, hypertension and type 2 diabetes. The increase in the economic burden associated with physical inactivity can be attributed to the combined effect of two factors: changes in total expenditure on specific disease groups (which showed an increase in the period under review) and changes in the physical activity levels of the Hungarian population (which showed an improvement over the period under review). Initiatives in Hungary aimed at encouraging an active lifestyle from childhood onwards should be continued since – beyond the initial impact that has already been felt to some extent in recent years - these initiatives will come to their full fruition in the coming decades.

Background: The so-called sports consumption models are looking for the factors that influence the sports spending of households. This paper aims to examine the Hungarian, Polish and German households’ sports expenditures which can be an important indicator of physical activity and sporty lifestyle. Methods: Surveying of households in three countries (Hungary, Poland and Germany) has been conducted with a self-designed questionnaire. We have used descriptive and bivariate non-parametric and parametric statistical methods: (1) χ² test, Mann-Whitney test and Kruskal-Wallis test for checking the relationship between sociodemographic and physical activity variables and (2) independent sample t-test and ANOVA for checking the differences in sports expenditures. Results: Our research concluded that men, especially previous athletes, exercise more than women and those who have no history as registered athletes. The choice of sports venues is obviously different between the countries in the sample. Members of the study population spend the most on sports services while they spend the least on sports equipment. German households have the highest spending rates compared to the other two countries. Conclusions: Results are in line with our previous research findings and with other literatures. The difference in preferences of sports venues could have the reason of different supply of sports clubs or the different living standards too. It needs further researches to clear it. Material wealth, income level and sport socialisation can be a determining factor regarding the level of sports spending.

(1) Background: Changes in daily life and academic training has led to uncertainty in the higher education student population during COVID-19. The goal of the study was to examine the impacts of the pandemic on Hungarian students. (2) Methods: A cross-sectional study was conducted by using self-report questionnaires collected in Google Forms. Eight-hundred-and-twenty-seven students (25.29 ± 8.09) took part anonymously. The respondents rate their overall physical and mental health on a 5-point Likert scale and validated scales were used: Well Being Index (WHO-5); Mindfulness Attention and Awareness Scale (MAAS); and Perceived Stress Scale (PSS-14). Statistical analyses were performed with IBM SPSS 24.0, results were considered at a significance level p ≤ 0.05. (3) Results: Positive correlation was found between MAAS and WHO-5 (r = 0.363, p < 0.001) negative correlation between MAAS and PSS-14 (r = −0.448, p < 0.001), and negative correlation between WHO-5 and PSS-14 (r = −0.671, p < 0.001). Females had higher PSS-14 mean score (32.51 ± 10.16) than males (27.71 ± 10.19; p < 0.001; Z = −5703), males (60.92 ± 12.10) had higher MAAS level than females (57.31 ± 12.51; p < 0.001; Z = −3589). No difference was found in gender regarding WHO-5 mean scores. Athletes (7.03 ± 3.27) differ significantly from non-athletes (6.00 ± 3.04) in WHO-5 (p < 0.001; Z = −4.349) and MAAS level (p = 0.012; Z = −2.498), but showed no difference in PSS-14 (p = 0.101; Z = −1.641). Students rated mental (3.01 ± 0.99) worse than physical health (3.49 ± 0.98; p < 0.001, r = 0.426) and the narrowing of social relationships worse (3.83 ± 1.26) than physical (p < 0.001, r = −0.212) and mental health (p < 0.001, r = −0.408). Females had worse mental health (2.96 ± 9.94) than males (3.20 ± 0.99; p = 0.003; Z = −2.924) and rated the narrowing of social relationships worse (3.90 ± 1.23) than males (3.59 ± 1.35; p = 0.006; Z = −2.730). (4) Conclusions: The pandemic has negatively impacted students, and it may have long-term consequences on their mental and physical health and education.

Inequalities in income, wealth, quality of life, health and education are an intensively researched field of economics. In this study, we examine the inequality in sports expenditures of Hungarian households. We hypothesize that the development of income inequalities will also correlate significantly to inequalities in sports consumption, and this trend has been intensifying over the past two decades. The research is based on the Household Budget Survey database of Hungarian households for the period 2005–2017. The net income conditions of the population and the sports expenditure items recorded on the basis of the COICOP nomenclature are examined by income decile. Data is analysed using descriptive statistics, inequality indicators and correlation calculations. Aggregate household expenditures on passive sports consumption show a stagnant trend, while aggregate expenditures on active sports consumption follow a slightly upward trend among the Hungarian population. Inequality indicators show growing inequalities in terms of income and sports expenditure over the reviewed period. Income inequality and sports spending inequality move together. The Hungarian population is becoming polarised in terms of both income and level of sports expenditure.