TY - JOUR
T1 - Revealing Intention In Health-related Peer Interactions
T2 - Implications For Optimizing Patient Engagement In Self-health Management
AU - Singh, Tavleen
AU - Wang, Jing
AU - Myneni, Sahiti
N1 - Publisher Copyright:
©2020 AMIA - All rights reserved.
PY - 2020
Y1 - 2020
N2 - Risky health behaviors such as poor diet, physical inactivity are the main contributors to the development of diabetes, one of the major causes of death and disability in the United States. Online health communities provide new avenues for individuals to efficiently manage their health conditions and adopt a positive lifestyle. So far, analysis of health-related online social exchanges has focused solely on communication content and structure of social ties, ignoring implicit user intentions underlying communication exchanges. In this paper, we propose an analytical framework to characterize communication intent, content, and social ties in online peer interactions. We integrate models from socio-behavioral sciences and linguistics with network analytics and apply it to understand Diabetes Self-Management. Results indicate the informational needs of users expressed in forms of speech acts can vary across different user engagement and disease management profiles. Implications for the design of interventions for better self-management of diabetes are discussed.
AB - Risky health behaviors such as poor diet, physical inactivity are the main contributors to the development of diabetes, one of the major causes of death and disability in the United States. Online health communities provide new avenues for individuals to efficiently manage their health conditions and adopt a positive lifestyle. So far, analysis of health-related online social exchanges has focused solely on communication content and structure of social ties, ignoring implicit user intentions underlying communication exchanges. In this paper, we propose an analytical framework to characterize communication intent, content, and social ties in online peer interactions. We integrate models from socio-behavioral sciences and linguistics with network analytics and apply it to understand Diabetes Self-Management. Results indicate the informational needs of users expressed in forms of speech acts can vary across different user engagement and disease management profiles. Implications for the design of interventions for better self-management of diabetes are discussed.
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M3 - Article
C2 - 33936488
AN - SCOPUS:85100322157
VL - 2020
SP - 1120
EP - 1129
JO - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
JF - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
SN - 1559-4076
ER -