Visual and gamified approaches to understanding complex causal networks in human health

Student thesis: Doctoral ThesisDoctor of Philosophy (PhD)


Recent advances allow researchers to study the complex relationships between hundreds or thousands of health factors in a single study but interpreting their results is threatened by high levels of network complexity. Previous research demonstrates that network data complexities can be understood by developing technology to visualise, simulate and gamify it.
In this thesis I present research developing ways of understanding the complexity in the network of factors related to human health. First, I introduce and analyse the complex relationship between psychological and physical health factors, particularly wellbeing and insomnia. Using a technique called Mendelian Randomisation (MR) I test the causal pathways between sleep and wellbeing (chapter 2), and then use network MR to conceptualise the wide-ranging potential factors that influence the network of effects between sleep and wellbeing (chapter 3). The resulting network dataset provides the foundation of the second part of the thesis, where I explore and test different methods to help researchers better understand the complexity in network MR datasets. Chapter 4 explores the development and use of causal network visualisation and chapter 5 describes the creation of a data game that allows participants to interact with the data and see the predicted impact of intervening on different nodes of the network. In the final empirical chapter (chapter 6), I build on previous experiments, and investigate whether the inclusion of game features results in greater participant understanding of the complexity of the data compared to using an interactive visualisation control.
My findings indicate that physical and mental health factors exist as part of large and complex network structures and that researchers can better understand these to some degree with visualisations, and perhaps to a greater extent with interactive and game mediums.
Date of Award9 May 2023
Original languageEnglish
Awarding Institution
  • University of Bristol
SupervisorClaire M A Haworth (Supervisor) & Oliver S Davis (Supervisor)

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