Modelling the Joint Effect of Social Determinants and Peers on Obesity Among Canadian Adults

Giabbanelli, Philippe J.; Jackson, Piper; Finegood, Diane T.

doi:10.1007/978-3-642-39149-1_10

Cited by 16 publications

(16 citation statements)

References 36 publications

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“…As expected, due to the altered propagation of the weight loss effect, the total network weight loss decreases as the network density declines. These findings are consistent with that of Giabbanelli et al [15], since the total network Time (months) p = 0 p = 0.2 p = 0.4 p = 0.8…”

Section: Effects Of Changing Network Densitysupporting

confidence: 92%

“…The literature suggests that among young adults, having obese friends trying to lose weight has a positive impact on an individual's willingness to lose weight [14]. How people are connected to each other is also found to impact the efficacy of health interventions, and that the structure of social networks impacts how health messages are transferred among individuals and how people can influence certain aspects of their peers' behavior [15]. Yet the relationship between acquiring knowledge of health issues and practicing the positive behaviour is unclear [15].…”

Section: Introductionmentioning

confidence: 99%

“…How people are connected to each other is also found to impact the efficacy of health interventions, and that the structure of social networks impacts how health messages are transferred among individuals and how people can influence certain aspects of their peers' behavior [15]. Yet the relationship between acquiring knowledge of health issues and practicing the positive behaviour is unclear [15]. Although some recent work [16] has taken advantage of social network concepts to investigate the influence of people on each other's healthrelated behavior and preferences, there remains a need to assess techniques in social network analysis that can enhance the efficacy of network-based interventions.…”

Section: Introductionmentioning

confidence: 99%

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Modeling the Spread of an Obesity Intervention through a Social Network

Sangachin

Samadi

Cavuoto

2014

Journal of Healthcare Engineering

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With the growing prevalence of obesity and the public health implications, it is critical to develop and evaluate potential interventions. One approach is to investigate the spread of positive health outcomes through a social network. We employ the ground concepts of spread maximization problem and adapt it to best reflect the dynamics of a weight loss intervention. A diffusion model is then employed for the propagation of weight loss effect throughout the network of obese and overweight individuals. This diffusion model integrates both personal attributes and network-related attributes of an individual while engaged in a weight loss program. Simulation findings suggest that choosing initial agents based on individual attributes, including ability to lose weight, body mass index, and threshold, produced the highest total weight loss in the network. Greedy algorithm was also applied to choose the most effective subset of initial seeds.

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Section: Effects Of Changing Network Densitysupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling the Spread of an Obesity Intervention through a Social Network

Sangachin

Samadi

Cavuoto

2014

Journal of Healthcare Engineering

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“…Supporting “what-if” scenarios is one of the main applications of modelling and simulation [68] and the design of such platforms has been well illustrated [9, 14, 69, 70] including in its application to policy portfolios for healthy food choices [14, 71]. Figure 1 provides as example a virtual platform simulating how individuals' behaviour depends on social and environmental influences, which could be changed to see the resulting impact in behaviours.…”

Section: Methodsmentioning

confidence: 99%

Using Agent-Based Models to Develop Public Policy about Food Behaviours: Future Directions and Recommendations

Giabbanelli

Crutzen

2017

Computational and Mathematical Methods in Medicine

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Most adults are overweight or obese in many western countries. Several population-level interventions on the physical, economical, political, or sociocultural environment have thus attempted to achieve a healthier weight. These interventions have involved different weight-related behaviours, such as food behaviours. Agent-based models (ABMs) have the potential to help policymakers evaluate food behaviour interventions from a systems perspective. However, fully realizing this potential involves a complex procedure starting with obtaining and analyzing data to populate the model and eventually identifying more efficient cross-sectoral policies. Current procedures for ABMs of food behaviours are mostly rooted in one technique, often ignore the food environment beyond home and work, and underutilize rich datasets. In this paper, we address some of these limitations to better support policymakers through two contributions. First, via a scoping review, we highlight readily available datasets and techniques to deal with these limitations independently. Second, we propose a three steps' process to tackle all limitations together and discuss its use to develop future models for food behaviours. We acknowledge that this integrated process is a leap forward in ABMs. However, this long-term objective is well-worth addressing as it can generate robust findings to effectively inform the design of food behaviour interventions.

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“…Analyzing the relationships in this paper was deemed necessary for the development of the simulation model for two reasons. First, it could provide an insight into the dynamics of obesity [5,6]. Second, understanding it would strengthen the foundations to gradually develop more comprehensive models of obesity.…”

Section: Using Visual Analytics At An Early Stagementioning

confidence: 99%

Using Visual Analytics to Support the Integration of Expert Knowledge in the Design of Medical Models and Simulations

Giabbanelli

Jackson

2015

Procedia Computer Science

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Visual analytics (VA) provides an interactive way to explore vast amounts of data and find interesting patterns. This has already benefited the development of computational models, as the patterns found using VA can then become essential elements of the model. Similarly, recent advances in the use of VA for the data cleaning stage are relevant to computational modelling given the importance of having reliable data to populate and check models. In this paper, we demonstrate via case studies of medical models that VA can be very valuable at the conceptual stage, to both examine the fit of a conceptual model with the underlying data and assess possible gaps in the model. The case studies were realized using different modelling tools (e.g., system dynamics or network modelling), which emphasizes that the relevance of VA to medical modelling cuts across techniques. Finally, we discuss how the interdisciplinary nature of modelling for medical applications requires an increased support for collaboration, and we suggest several areas of research to improve the intake and experience of VA for collaborative modelling in medicine.

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Modelling the Joint Effect of Social Determinants and Peers on Obesity Among Canadian Adults

Cited by 16 publications

References 36 publications

Modeling the Spread of an Obesity Intervention through a Social Network

Modeling the Spread of an Obesity Intervention through a Social Network

Using Agent-Based Models to Develop Public Policy about Food Behaviours: Future Directions and Recommendations

Using Visual Analytics to Support the Integration of Expert Knowledge in the Design of Medical Models and Simulations

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