Towards personalized digital experiences to promote diabetes self-management: A mixed-methods social computing approach
ABSTRACT
Background:
Type 2 Diabetes affects nearly 34.2 million adults and is the seventh leading cause of death in the United States. Online health communities have emerged as avenues to provide social support to individuals engaging in diabetes self-management (DSM). The analysis of online peer interactions and social connections can improve our understanding of the factors underlying behavior change which can inform the development of personalized DSM interventions.
Objective:
Our objective is to apply our methodology using mixed-methods approach to a) characterize the role of context-specific social influence patterns in DSM and b) derive interventional targets that enhance individual engagement in DSM.
Methods:
Using the peer messages from American Diabetes Association (ADA) support community for DSM (n = ~73k peer interactions from 2014-2021), a) labeled set of peer interactions was generated (n = 1,501 for ADA) through manual annotation; b) deep learning models were used to scale the qualitative codes to the entire datasets; c), the validated model was applied to perform a retrospective analysis; and d) social network analysis (SNA) techniques were used to portray large-scale patterns and relationships among the aforementioned communication dimensions embedded in peer interactions.
Results:
With additional in-domain pre-training, bidirectional encoder representations from Transformers (BERT) outperformed other deep learning models on all classification tasks. Our results suggest that the community users exposed to other users by expressing specific content using specific SAs tended to engage more in the community.
Conclusions:
In this study, we characterize the role of social influence in DSM as observed in large-scale social media datasets. Implications for multicomponent digital interventions is discussed.
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