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Analyzing the Complexity of Behavioural Factors Influencing Weight in Adults

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Advanced Data Analytics in Health

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 93))

Abstract

Managing obesity is a difficult and pressing problem given its detrimental health effects and associated healthcare costs. This difficulty stems from obesity being the result of a complex system. This complexity is often ignored by generic interventions, and not fully utilized for clinical decision-making. We focused on heterogeneity and feedback loops as key parts of this complexity. We measured heterogeneity and found it high, in a demographically homogeneous sample as well as in a larger, more varied sample. We also demonstrated that taking a systems approach could hold value for clinical decision-making. Specifically, we showed that feedback loops had better associations with weight categories than individual factors or relationships, in addition to clear implications for weight dynamics. Clinical implications were discussed, in part through adapting techniques such as card decks in a computerized format. Further research was suggested on heterogeneity among population groups and categories of driver of weight.

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Notes

  1. 1.

    http://psychology-tools.com/binge-eating-scale/.

  2. 2.

    http://www.livingstrong.org/articles/ExerciseMotivations.pdf.

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Acknowledgements

The author is indebted to Prof. Diane T. Finegood, who pioneered research in systems thinking and obesity, and inspired this line of work. The data analysis presented here was made possible by the significant effort of many instructors at Simon Fraser University in recruiting participants: Anne-Kristina Arnold, Craig Asmundson, Diana Bedoya, Penelope Deck, Leah Esplen, Tony Leyland, Mike Walsh, and Matt White. Finally, the author thanks Amy Child at the University of Cambridge for extensive feedback on this manuscript, as well as Jean Adams and James Woodcock for suggestions.

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Authors and Affiliations

  1. Computer Science Department, Furman University, Greenville, SC, 29613, USA

    Philippe J. Giabbanelli

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  1. Philippe J. Giabbanelli
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Correspondence to Philippe J. Giabbanelli .

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Editors and Affiliations

  1. Computer Science Department, Furman University, Greenville, South Carolina, USA

    Philippe J. Giabbanelli

  2. Department of Computer Science, Lakehead University, Thunder Bay, Ontario, Canada

    Vijay K. Mago

  3. Department of Computer Engineering, Technological Educational Institute, Lamia, Greece

    Elpiniki I. Papageorgiou

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Giabbanelli, P.J. (2018). Analyzing the Complexity of Behavioural Factors Influencing Weight in Adults. In: Giabbanelli, P., Mago, V., Papageorgiou, E. (eds) Advanced Data Analytics in Health. Smart Innovation, Systems and Technologies, vol 93. Springer, Cham. https://doi.org/10.1007/978-3-319-77911-9_10

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  • DOI: https://doi.org/10.1007/978-3-319-77911-9_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77910-2

  • Online ISBN: 978-3-319-77911-9

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