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

Christoph Buck, Anne Loyen, Ronja Foraita, Jelle van Cauwenberg, Marieke de Craemer, Ciaran Mac Donncha, Jean-Michel Oppert, Johannes Brug, Nanna Lien, Greet Cardon, Iris Pigeot, Sebastien Chastin, on behalf of the DEDIPAC consortium

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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.
Original languageEnglish
Article numbere0211546
JournalPLoS ONE
Volume14
Issue number1
DOIs
Publication statusPublished - 1 Jan 2019

Cite this

Buck, C., Loyen, A., Foraita, R., van Cauwenberg, J., de Craemer, M., Donncha, C. M., ... on behalf of the DEDIPAC consortium (2019). Factors influencing sedentary behaviour: A system based analysis using Bayesian networks within DEDIPAC. PLoS ONE, 14(1), [e0211546]. https://doi.org/10.1371/journal.pone.0211546
Buck, Christoph ; Loyen, Anne ; Foraita, Ronja ; van Cauwenberg, Jelle ; de Craemer, Marieke ; Donncha, Ciaran Mac ; Oppert, Jean-Michel ; Brug, Johannes ; Lien, Nanna ; Cardon, Greet ; Pigeot, Iris ; Chastin, Sebastien ; on behalf of the DEDIPAC consortium. / Factors influencing sedentary behaviour: A system based analysis using Bayesian networks within DEDIPAC. In: PLoS ONE. 2019 ; Vol. 14, No. 1.
@article{69a5254667984c04a97af45e57c1a5cd,
title = "Factors influencing sedentary behaviour: A system based analysis using Bayesian networks within DEDIPAC",
abstract = "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.",
author = "Christoph Buck and Anne Loyen and Ronja Foraita and {van Cauwenberg}, Jelle and {de Craemer}, Marieke and Donncha, {Ciaran Mac} and Jean-Michel Oppert and Johannes Brug and Nanna Lien and Greet Cardon and Iris Pigeot and Sebastien Chastin and {on behalf of the DEDIPAC consortium}",
year = "2019",
month = "1",
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doi = "10.1371/journal.pone.0211546",
language = "English",
volume = "14",
journal = "PLoS ONE",
issn = "1932-6203",
publisher = "Public Library of Science",
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Buck, C, Loyen, A, Foraita, R, van Cauwenberg, J, de Craemer, M, Donncha, CM, Oppert, J-M, Brug, J, Lien, N, Cardon, G, Pigeot, I, Chastin, S & on behalf of the DEDIPAC consortium 2019, 'Factors influencing sedentary behaviour: A system based analysis using Bayesian networks within DEDIPAC' PLoS ONE, vol. 14, no. 1, e0211546. https://doi.org/10.1371/journal.pone.0211546

Factors influencing sedentary behaviour: A system based analysis using Bayesian networks within DEDIPAC. / Buck, Christoph; Loyen, Anne; Foraita, Ronja; van Cauwenberg, Jelle; de Craemer, Marieke; Donncha, Ciaran Mac; Oppert, Jean-Michel; Brug, Johannes; Lien, Nanna; Cardon, Greet; Pigeot, Iris; Chastin, Sebastien; on behalf of the DEDIPAC consortium.

In: PLoS ONE, Vol. 14, No. 1, e0211546, 01.01.2019.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Factors influencing sedentary behaviour: A system based analysis using Bayesian networks within DEDIPAC

AU - Buck, Christoph

AU - Loyen, Anne

AU - Foraita, Ronja

AU - van Cauwenberg, Jelle

AU - de Craemer, Marieke

AU - Donncha, Ciaran Mac

AU - Oppert, Jean-Michel

AU - Brug, Johannes

AU - Lien, Nanna

AU - Cardon, Greet

AU - Pigeot, Iris

AU - Chastin, Sebastien

AU - on behalf of the DEDIPAC consortium

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

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