![]() ![]() However, in the latter study, this effect was particularly observable in participants with positive psychosocial attributes.ĭe la Haye and colleagues were able to show that close friends in adolescent friendship networks have a significant influence on each other regarding a number of health related behaviours, especially the physical activity behaviour was “ found to be similar”. confirm the impact of social networks and social support on the degree that people are physically active. Mc Neill, Kreuter, and Subramanian identify five modifiable dimensions of the social environment that have an influence on the activity status of a person: firstly, social support and social networks secondly, socioeconomic position and income inequality thirdly, racial discrimination fourthly, social cohesion and social capital and fifthly, neighbourhood factors. note that physical activity is associated with individual social participation and neighbourhood interpersonal trust. The studies in unison report that a high socioeconomic position (including occupational position, income and educational level) positively correlates with a higher degree of physical activity in leisure time.įurther studies also analysed the effects of social networks on the willingness of an individual to be physically active. Most studies in this regard have analysed socioeconomic correlates of physical activity, particularly the socioeconomic status. In the last ten years, public health research has paid increased attention to determinants of physical activity in order to fight the pandemic development of non–communicable diseases. In order to promote physical activity, future health initiatives should target these factors of a person’s network. Our study shows that several social factors determine the physical activity of very active and very inactive groups. Obese people were particularly inactive when they were members of frequently communicating, age-heterogeneous groups. Such interacting factors were for example the degree of communication within the group, the gender- and age-related composition of the group, but also the equipment that had been brought to the beach/pool. The identification of six contrasting clusters highlights that besides the body shape several factors interact regarding a group’s physical level. ![]() Yet, classification tree analysis reveals that obesity itself does not necessarily determine physical inactivity levels. ![]() While obese groups had the lowest average activity level, groups mainly consisting of people with an athletic body shape were the most physically active. General statistical analysis shows that, overall, the most differentiating factor regarding physical activity was the body shape of the group members. For this, we conducted a classification tree analysis. To better understand activity promoting and hindering mechanisms, special attention is given to the identification of contrasting factors that characterise groups which are very active or very inactive. For the general statistics, we accessed the significance of differences regarding the degree of physical activity dependent on the target variables. In total, we observed 907 groups with the groups’ size varying between 2 and 8 members. Total observation period was eight and a half months. We recorded the physical activity of face-to-face interacting groups and analysed categories such as group size, estimated age of the group members, and verbal communication patterns. This study presents the quantitative data of a systematic (covert) participant observation. In order to consider the context in which physical activity occurs and to investigate whether cultural settings may influence physical activity, we conducted the study at pools in different cultural environments - Hawai’i and Germany. This field study aims to investigate the determinants of physical activity of particularly active and inactive groups in their leisure environments. ![]()
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