Abstract
This work contributes to an emerging literature focused on the role of physical activity on the subjective well-being of populations. Unlike the existing literature, it proposes an approach that uses algorithms to predict subjective well-being. The aims of this study were to determine the relative importance of sports participation and perceived value of elite sports on the subjective well-being of individuals. A total of 511 participants completed an online questionnaire. The statistical analysis used several machine learning techniques, including three algorithms, Decision Tree Classifier (DTC), Random Forest Classifier (RFC), and Gradient Boosting Classifier (GBC). In the three algorithms tested, sports participation, expressed as the weekly frequency and the time spent engaging in vigorous physical activity, showed a greater importance (between 47% and 53%) in determining subjective well-being. It also highlights the effect of perceived value of elite sport on the prediction of subjective well-being. This study provides evidence for public sport policy makers/authorities and for managers of physical activity and sport development programs. The surprising effect of the perceived value of elite sport on the prediction of subjective well-being.
Highlights
Increasing sport participation in order to promote citizens’ quality of life, health and well-being has been one of the main goals of the policies implemented by many European governments
Sports practice has been the subject of attention in recent years; studies on the effects of physical activity and sports on the subjective well-being of individuals, compared with the effects of other factors, remains oftentimes unexplored [5] and relatively unusual, e.g., [6,7,8,9,10,11,12,13,14], such that it has been reported that there seems to be a series of gaps, including the possible impact of non-observed variables [3], such as the frequency and weekly duration of physical activity sessions
We investigate and compare the performances of several machine learning techniques (DTC, Random Forest Classifier (RFC), and Gradient Boosting Classifier (GBC)) using data collected from a survey representing sports participation and perceived value of elite sport as predictors of subjective well-being
Summary
Increasing sport participation in order to promote citizens’ quality of life, health and well-being has been one of the main goals of the policies implemented by many European governments. Such as income, work situation, academic degree, gender, and race—influencing citizens’. The perceived value of elite sports has not received much attention and there is still no clear evidence about the effects of the international sporting success of a country on the subjective well-being of individuals [15]. Based on the aforementioned framework, to determine subjective well-being, this study evaluates the effects of (1) the weekly frequency and duration of sport sessions; and on the other hand, as an innovation in the literature, it uses the (2) perceived value of elite sports as a subjective and individual indicator of the international sporting success of teams and athletes from the country Sports practice has been the subject of attention in recent years; studies on the effects of physical activity and sports on the subjective well-being of individuals, compared with the effects of other factors, remains oftentimes unexplored [5] and relatively unusual, e.g., [6,7,8,9,10,11,12,13,14], such that it has been reported that there seems to be a series of gaps, including the possible impact of non-observed variables [3], such as the frequency and weekly duration of physical activity sessions.
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