Turn Your Online Engagement in Chronic Disease Management from Zero to Hero: A Multi-Dimensional Continuous-Time Evaluation
38 Pages Posted: 24 Jan 2019 Last revised: 28 Mar 2019
Date Written: January 21, 2019
Individuals’ engagement in online healthcare communities (OHCs) has attracted a large body of research. However, prior research has mainly studied a single behavioral dimension of online engagement. Given that individuals’ online engagement is a complex process, overlooking certain aspects of engagement may lead to underestimating the effectiveness of OHCs. In addition, due to the progressive nature of chronic disease, chronic disease management needs to be evaluated continuously. These concerns motivate us to develop a framework that accounts for both engagement dimensions and engagement timing for studying chronic disease management in OHCs. Considering that the engagement level of disease management cannot be directly observed, in this study, we propose a multi-dimensional Continuous-Time Hidden Markov Model (CTHMM) that captures individuals’ engagement level as a latent state. We root our research in the context of weight management. Our main findings include: 1) The timing of engagement can affect an individual’s engagement level; for instance, individuals with higher participation frequency are more likely to shift to different engagement levels. 2) Participating in support-exchange activities can shift individuals’ focus from weigh-ins to journals, which are two distinct behavioral dimensions of self-monitoring. Thus, an incomplete characterization of engagement dimensions can underestimate individuals’ activeness in OHCs, which will further lead to underrating the role of OHCs in chronic disease control. 3) Different forms of social support can have statistically different effects on engagement, and these effects are mediated by individuals’ own engagement levels. Individuals need to “smartly” adopt social support tools to improve their health management.
Keywords: behavioral dimensions of engagement, timing information, chronic disease management, online healthcare communities (OHCs), multi-dimensional Continuous-Time Hidden Markov Model (CTHMM)
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