Health assets and active lifestyles during preadolescence and adolescence: highlights from the HBSC/WHO health survey and implications for health promotion

The present chapter discusses the assets model as a theoretical approach to the study of health behavior and health promotion. The model emphasizes people’s talents, competences, and resources. In this chapter, a health asset is defined as any factor or resource that maximizes the opportunities for individuals, local communities, and populations to attain and maintain health and well-being. This perspective expands and complements the current medical model as it focuses on the development of a sense of empowerment in community members to prevent and manage their own health. Therefore, in this chapter we address the concepts of salutogenesis, social support, resilience, coping, self-regulation, social capital, and personal and social competence, which are central to the development of individuals’ potential to manage and savor their own health, creating the conditions for self-fulfillment. Additionally, we demonstrate how the assets model guides the study of children’s and adolescents’ health in the Portuguese Health Behaviour in School-aged Children study (www.hbsc.org), concentrating on areas such as active lifestyles and quality-of-life perception. Finally, we present a roadmap for action that emphasizes the need to identify the factors that make children and adolescents happy and healthy individuals, while minimizing risks and problems they naturally encounter throughout their development. We also argue for the need to involve young people in discussions concerning their health and health promotion practices, focusing on the development of talents, capabilities, and positive expectations for the future.

Big data: appearance and disappearance

The mainstay of Big Data is prediction in that it allows practitioners, researchers, and policy analysts to predict trends based upon the analysis of large and varied sources of data. These can range from changing social and political opinions, patterns in crimes, and consumer behaviour. Big Data has therefore shifted the criterion of success in science from causal explanations to predictive modelling and simulation. The 19th-century science sought to capture phenomena and seek to show the appearance of it through causal mechanisms while 20th-century science attempted to save the appearance and relinquish causal explanations. Now 21st-century science in the form of Big Data is concerned with the prediction of appearances and nothing more. However, this pulls social science back in the direction of a more rule- or law-governed reality model of science and away from a consideration of the internal nature of rules in relation to various practices. In effect Big Data offers us no more than a world of surface appearance and in doing so it makes disappear any context-specific conceptual sensitivity.