Accepted for/Published in: JMIR Pediatrics and Parenting
Date Submitted: Dec 6, 2022
Date Accepted: Oct 4, 2023
Online communication data for social network health interventions: An agent-based modeling study
ABSTRACT
Background:
Social network interventions are an effective approach to promote physical activity among youth. These interventions are traditionally designed upon self-reported peer nomination network data for representing social connections. However, communication data from online social media might be a promising alternative, given the availability of these data and the shift of personal communication to the online sphere.
Objective:
To test the applicability of online communication data for social network intervention design, we compare the network structure, the selection of influential individuals and the effect of social network interventions of using online communication data with the traditional approach, that is, peer nomination data.
Methods:
Data on sociometric questionnaires, online communication and physical activity level (PAL) of 408 participants in 21 school classes were used. We apply social network analysis (SNA) to identify influential individuals, and agent-based modeling (ABM) to simulate social network interventions for promoting physical activity among adolescents in school classes. Influential individuals were selected, based on centrality measures (i.e., in-degree, closeness and betweenness), to spread the health intervention.
Results:
About 68-74% of the selected influential individuals differed between the two network representations. In general, the simulations showed that interventions could increase PAL by 5.2-6.9% within two months in both network representations. However, the predicted median impact on PAL was higher in networks based on peer nomination than online communication data for in-degree (5.2 vs 6.9% and closeness centrality (5.5 vs 6.8%). Also, a large variation in impact was observed between school classes (range 0.9% - 14.3%).
Conclusions:
Our findings showed that network interventions based on online communication data increase PAL. Online communication data can therefore be a useful alternative to peer nomination data for future designing of network interventions. Computational methods (SNA and ABM) can help to design these network interventions and provide insights on the role network characteristics on its effectiveness.
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