Factors influencing sedentary behaviour: A system based analysis using Bayesian networks within DEDIPAC.
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 interdependen...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0211546 |
| _version_ | 1818678196408680448 |
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| author | 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 DEDIPAC consortium |
| author_facet | 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 DEDIPAC consortium |
| author_sort | Christoph Buck |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T09:11:25Z |
| format | Article |
| id | doaj.art-2105b93f524749039ac813c69e963832 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-17T09:11:25Z |
| publishDate | 2019-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-2105b93f524749039ac813c69e9638322022-12-21T21:55:11ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01141e021154610.1371/journal.pone.0211546Factors influencing sedentary behaviour: A system based analysis using Bayesian networks within DEDIPAC.Christoph BuckAnne LoyenRonja ForaitaJelle Van CauwenbergMarieke De CraemerCiaran Mac DonnchaJean-Michel OppertJohannes BrugNanna LienGreet CardonIris PigeotSebastien ChastinDEDIPAC consortiumBACKGROUND: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.https://doi.org/10.1371/journal.pone.0211546 |
| spellingShingle | 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 DEDIPAC consortium Factors influencing sedentary behaviour: A system based analysis using Bayesian networks within DEDIPAC. PLoS ONE |
| title | Factors influencing sedentary behaviour: A system based analysis using Bayesian networks within DEDIPAC. |
| title_full | Factors influencing sedentary behaviour: A system based analysis using Bayesian networks within DEDIPAC. |
| title_fullStr | Factors influencing sedentary behaviour: A system based analysis using Bayesian networks within DEDIPAC. |
| title_full_unstemmed | Factors influencing sedentary behaviour: A system based analysis using Bayesian networks within DEDIPAC. |
| title_short | Factors influencing sedentary behaviour: A system based analysis using Bayesian networks within DEDIPAC. |
| title_sort | factors influencing sedentary behaviour a system based analysis using bayesian networks within dedipac |
| url | https://doi.org/10.1371/journal.pone.0211546 |
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