Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

S.C.M.W. Tummers*, A.J. Hommersom*, E.H.S. Lechner*, Catharina Bolman, Roger Bemelmans

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Article in proceedingAcademicpeer-review

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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 gen-eral machine learning methods to this application domain, and Bayesian networks in particular. We investigate several suitable methods for deal-ing 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 determi-nants. 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 languageEnglish
Title of host publicationBNAIC/BeneLearn 2020
Subtitle of host publicationProceedings
EditorsLu Cao, Walter Kosters, Jefrey Lijffijt
Place of PublicationLeiden
PublisherLeiden University
Pages298- 312
Number of pages15
Publication statusPublished - 2020
EventBNAIC/BeneLearn 2020 - Online, Leiden University, Leiden, Netherlands
Duration: 19 Nov 202020 Nov 2020


ConferenceBNAIC/BeneLearn 2020
Internet address


  • Machine Learning
  • Bayesian Network
  • E-health Intervention
  • Structure Learning
  • Physical Activity


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