Gaining Insight into Determinants of Physical Activity Using Bayesian Network Learning

Simone C.M.W. Tummers; Arjen Hommersom; Lilian Lechner; Catherine Bolman; Roger Bemelmans

doi:10.1007/978-3-030-76640-5_11

Gaining Insight into Determinants of Physical Activity Using Bayesian Network Learning

Simone C.M.W. Tummers^*, Arjen Hommersom, Lilian Lechner, Catherine Bolman, Roger Bemelmans

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

Abstract

Bayesian network modelling is applied to health psychology data in order to obtain more insight into the determinants of physical activity. This preliminary study discusses some challenges to apply general machine learning methods to this application domain, and Bayesian networks in particular. We investigate several suitable methods for dealing with missing data, and determine which method obtains good results in terms of fitting the data. Furthermore, we present the learnt Bayesian network model for this e-health intervention case study, and conclusions are drawn about determinants of physical activity behaviour change and how the intervention affects physical activity behaviour and its determinants. We also evaluate the contributions of Bayesian network analysis compared to traditional statistical analyses in this field. Finally, possible extensions on the performed analyses are proposed.

Original language	English
Title of host publication	Artificial Intelligence and Machine Learning
Subtitle of host publication	32nd Benelux Conference, BNAIC/Benelearn 2020, Leiden, The Netherlands, November 19–20, 2020, Revised Selected Papers
Editors	Mitra Baratchi, Lu Cao, Walter A. Kosters, Jefrey Lijffijt, Jan N. van Rijn, Frank W. Takes
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	172-187
Number of pages	16
ISBN (Print)	9783030766399
DOIs	https://doi.org/10.1007/978-3-030-76640-5_11
Publication status	Published - 20 May 2021
Event	32nd Benelux Conference on Artiﬁcial Intelligence and Belgian-Dutch Conference on Machine Learning, BNAIC/Benelearn 2020 - Virtual, Online Duration: 19 Nov 2020 → 20 Nov 2020 https://bnaic.liacs.leidenuniv.nl/

Publication series

Series	Communications in Computer and Information Science
Volume	1398 CCIS
ISSN	1865-0929

Conference

Conference	32nd Benelux Conference on Artiﬁcial Intelligence and Belgian-Dutch Conference on Machine Learning, BNAIC/Benelearn 2020
Period	19/11/20 → 20/11/20
Internet address	https://bnaic.liacs.leidenuniv.nl/

Keywords

Bayesian network
E-health intervention
Machine learning
Physical activity
Structure learning

Access to Document

10.1007/978-3-030-76640-5_11Licence: CC BY

Cite this

Tummers, S. C. M. W., Hommersom, A., Lechner, L., Bolman, C., & Bemelmans, R. (2021). Gaining Insight into Determinants of Physical Activity Using Bayesian Network Learning. In M. Baratchi, L. Cao, W. A. Kosters, J. Lijffijt, J. N. van Rijn, & F. W. Takes (Eds.), Artificial Intelligence and Machine Learning: 32nd Benelux Conference, BNAIC/Benelearn 2020, Leiden, The Netherlands, November 19–20, 2020, Revised Selected Papers (pp. 172-187). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-76640-5_11

Tummers, Simone C.M.W. ; Hommersom, Arjen ; Lechner, Lilian et al. / Gaining Insight into Determinants of Physical Activity Using Bayesian Network Learning. Artificial Intelligence and Machine Learning: 32nd Benelux Conference, BNAIC/Benelearn 2020, Leiden, The Netherlands, November 19–20, 2020, Revised Selected Papers. editor / Mitra Baratchi ; Lu Cao ; Walter A. Kosters ; Jefrey Lijffijt ; Jan N. van Rijn ; Frank W. Takes. Springer Science and Business Media Deutschland GmbH, 2021. pp. 172-187 (Communications in Computer and Information Science, Vol. 1398 CCIS).

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title = "Gaining Insight into Determinants of Physical Activity Using Bayesian Network Learning",

abstract = "Bayesian network modelling is applied to health psychology data in order to obtain more insight into the determinants of physical activity. This preliminary study discusses some challenges to apply general machine learning methods to this application domain, and Bayesian networks in particular. We investigate several suitable methods for dealing with missing data, and determine which method obtains good results in terms of fitting the data. Furthermore, we present the learnt Bayesian network model for this e-health intervention case study, and conclusions are drawn about determinants of physical activity behaviour change and how the intervention affects physical activity behaviour and its determinants. We also evaluate the contributions of Bayesian network analysis compared to traditional statistical analyses in this field. Finally, possible extensions on the performed analyses are proposed.",

keywords = "Bayesian network, E-health intervention, Machine learning, Physical activity, Structure learning",

author = "Tummers, {Simone C.M.W.} and Arjen Hommersom and Lilian Lechner and Catherine Bolman and Roger Bemelmans",

note = "Funding Information: Acknowledgements. This work is part of the research programme Active4Life with project number 546003005, which is financed by ZonMw. Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 32nd Benelux Conference on Artiﬁcial Intelligence and Belgian-Dutch Conference on Machine Learning, BNAIC/Benelearn 2020 ; Conference date: 19-11-2020 Through 20-11-2020",

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Tummers, SCMW, Hommersom, A , Lechner, L , Bolman, C & Bemelmans, R 2021, Gaining Insight into Determinants of Physical Activity Using Bayesian Network Learning. in M Baratchi, L Cao, WA Kosters, J Lijffijt, JN van Rijn & FW Takes (eds), Artificial Intelligence and Machine Learning: 32nd Benelux Conference, BNAIC/Benelearn 2020, Leiden, The Netherlands, November 19–20, 2020, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH, Communications in Computer and Information Science, vol. 1398 CCIS, pp. 172-187, 32nd Benelux Conference on Artiﬁcial Intelligence and Belgian-Dutch Conference on Machine Learning, BNAIC/Benelearn 2020, 19/11/20. https://doi.org/10.1007/978-3-030-76640-5_11

Gaining Insight into Determinants of Physical Activity Using Bayesian Network Learning. / Tummers, Simone C.M.W.; Hommersom, Arjen ; Lechner, Lilian et al.
Artificial Intelligence and Machine Learning: 32nd Benelux Conference, BNAIC/Benelearn 2020, Leiden, The Netherlands, November 19–20, 2020, Revised Selected Papers. ed. / Mitra Baratchi; Lu Cao; Walter A. Kosters; Jefrey Lijffijt; Jan N. van Rijn; Frank W. Takes. Springer Science and Business Media Deutschland GmbH, 2021. p. 172-187 (Communications in Computer and Information Science, Vol. 1398 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

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AU - Tummers, Simone C.M.W.

AU - Hommersom, Arjen

AU - Lechner, Lilian

AU - Bolman, Catherine

AU - Bemelmans, Roger

N1 - Funding Information: Acknowledgements. This work is part of the research programme Active4Life with project number 546003005, which is financed by ZonMw. Publisher Copyright: © 2021, Springer Nature Switzerland AG.

PY - 2021/5/20

Y1 - 2021/5/20

N2 - Bayesian network modelling is applied to health psychology data in order to obtain more insight into the determinants of physical activity. This preliminary study discusses some challenges to apply general machine learning methods to this application domain, and Bayesian networks in particular. We investigate several suitable methods for dealing with missing data, and determine which method obtains good results in terms of fitting the data. Furthermore, we present the learnt Bayesian network model for this e-health intervention case study, and conclusions are drawn about determinants of physical activity behaviour change and how the intervention affects physical activity behaviour and its determinants. We also evaluate the contributions of Bayesian network analysis compared to traditional statistical analyses in this field. Finally, possible extensions on the performed analyses are proposed.

AB - Bayesian network modelling is applied to health psychology data in order to obtain more insight into the determinants of physical activity. This preliminary study discusses some challenges to apply general machine learning methods to this application domain, and Bayesian networks in particular. We investigate several suitable methods for dealing with missing data, and determine which method obtains good results in terms of fitting the data. Furthermore, we present the learnt Bayesian network model for this e-health intervention case study, and conclusions are drawn about determinants of physical activity behaviour change and how the intervention affects physical activity behaviour and its determinants. We also evaluate the contributions of Bayesian network analysis compared to traditional statistical analyses in this field. Finally, possible extensions on the performed analyses are proposed.

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KW - Structure learning

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DO - 10.1007/978-3-030-76640-5_11

M3 - Conference Article in proceeding

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SN - 9783030766399

T3 - Communications in Computer and Information Science

SP - 172

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BT - Artificial Intelligence and Machine Learning

A2 - Baratchi, Mitra

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A2 - van Rijn, Jan N.

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PB - Springer Science and Business Media Deutschland GmbH

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Tummers SCMW, Hommersom A , Lechner L , Bolman C, Bemelmans R. Gaining Insight into Determinants of Physical Activity Using Bayesian Network Learning. In Baratchi M, Cao L, Kosters WA, Lijffijt J, van Rijn JN, Takes FW, editors, Artificial Intelligence and Machine Learning: 32nd Benelux Conference, BNAIC/Benelearn 2020, Leiden, The Netherlands, November 19–20, 2020, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2021. p. 172-187. (Communications in Computer and Information Science, Vol. 1398 CCIS). doi: 10.1007/978-3-030-76640-5_11