ArticlePloS one2020
Evaluating social network-based weight loss interventions in Chinese population: An agent-based simulation.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Article
- Adaptive behaviour during epidemics: a social risk appraisal approach to modelling dynamics.Journal of the Royal Society, Interface · 2025Article
- Evolutionary Digital Twin-Oriented Complex Networked Systems driven by node features and the mutation of feature preferences.PloS one · 2024Article
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Authors and funding
3 authors.
Funding
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Abstract
objectiveThe aim of this study is to assess network-based weight loss interventions in the Chinese setting using agent-based simulation.
methodsAn agent-based model incorporating social, environmental and personal influence is developed to simulate the obesity epidemic through an interconnected social network among a population of 2197 individuals from the nationally representative survey. Model parameters are collected from literature and existing database. To ensure the robustness of our findings, the model is validated against empirical observations and sensitivity analyses are performed on calibrated parameters.
resultsWhen compared with the baseline model, significant weight difference is detected using paired samples t tests for network-based intervention strategies (p<0.05) but no difference is observed for the two conventional intervention strategies including choosing random or high-risk individuals (p>0.05). Targeting the most connected individuals minimizes the average population weight, average BMI, and generates a reduction of 2.70% and 1.38% in overweight and obesity prevalence.
conclusionsThe simulations shows that targeting individuals on the basis of their social network attributes outperforms conventional targeting strategies. Future work needs to focus on how to further leverage social networks to curb obesity prevalence and enhance interventions for other chronic conditions using agent-based simulation.
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