Factors influencing sedentary behaviour: A system based analysis using Bayesian networks within DEDIPAC

Christoph Buck (1) , Anne Loyen (2) , Ronja Foraita (1) , Jelle van Cauwenberg (3) , Marieke de Craemer (3, 4) , Ciaran Mac Donncha (5) , Jean-Michel Oppert (6, 7, 8) , Johannes Brug (9) , Nanna Lien (10) , Greet Cardon (4, 3) , Iris Pigeot (1, 11) , Sebastien Chastin (4, 3, 12)

1 Leibniz Institute for Prevention Research and Epidemiology - BIPS
2 VU - Vrije Universiteit Amsterdam [Amsterdam]
3 UGENT - Universiteit Gent = Ghent University
4 Department of Movement and Sports Sciences
5 UL - University of Limerick
6 ICAN - Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Research Unit on Cardiovascular and Metabolic Diseases
7 SU - Sorbonne Université
8 CHU Pitié-Salpêtrière [AP-HP]
9 UvA - University of Amsterdam [Amsterdam] = Universiteit van Amsterdam
10 UiO - University of Oslo
11 University of Bremen
12 GCU - Glasgow Caledonian University

BACKGROUND: Decreasing sedentary behaviour (SB) has emerged as a public health priority since prolonged sitting increases the risk of non-communicable diseases. Mostly, the independent association of factors with SB has been investigated, although lifestyle behaviours are conditioned by interdependent factors. Within the DEDIPAC Knowledge Hub, a system of sedentary behaviours (SOS)-framework was created to take interdependency among multiple factors into account. The SOS framework is based on a system approach and was developed by combining evidence synthesis and expert consensus. The present study conducted a Bayesian network analysis to investigate and map the interdependencies between factors associated with SB through the life-course from large scale empirical data. METHODS: Data from the Eurobarometer survey (80.2, 2013) that included the International physical activity questionnaire (IPAQ) short as well as socio-demographic information and questions on perceived environment, health, and psychosocial information were enriched with macro-level data from the Eurostat database. Overall, 33 factors were identified aligned to the SOS-framework to represent six clusters on the individual or regional level: 1) physical health and wellbeing, 2) social and cultural context, 3) built and natural environment, 4) psychology and behaviour, 5) institutional and home settings, 6) policy and economics. A Bayesian network analysis was conducted to investigate conditional associations among all factors and to determine their importance within these networks. Bayesian networks were estimated for the complete (23,865 EU-citizens with complete data) sample and for sex- and four age-specific subgroups. Distance and centrality were calculated to determine importance of factors within each network around SB. RESULTS: In the young (15-25), adult (26-44), and middle-aged (45-64) groups occupational level was directly associated with SB for both, men and women. Consistently, social class and educational level were indirectly associated within male adult groups, while in women factors of the family context were indirectly associated with SB. Only in older adults, factors of the built environment were relevant with regard to SB, while factors of the home and institutional settings were less important compared to younger age groups. CONCLUSION: Factors of the home and institutional settings as well as the social and cultural context were found to be important in the network of associations around SB supporting the priority for future research in these clusters. Particularly, occupational status was found to be the main driver of SB through the life-course. Investigating conditional associations by Bayesian networks gave a better understanding of the complex interplay of factors being associated with SB. This may provide detailed insights in the mechanisms behind the burden of SB to effectively inform policy makers for detailed intervention planning. However, considering the complexity of the issue, there is need for a more comprehensive system of data collection including objective measures of sedentary time.

Domaines: Santé publique et épidémiologie

Christoph Buck, Anne Loyen, Ronja Foraita, Jelle van Cauwenberg, Marieke de Craemer, et al.. Factors influencing sedentary behaviour: A system based analysis using Bayesian networks within DEDIPAC. PLoS ONE, 2019, 14, pp.e0211546. ⟨10.1371/journal.pone.0211546⟩. ⟨hal-02024737⟩