Abstract
BackgroundDigital health social networks (DHSNs) are widespread, and the consensus is that they contribute to wellness by offering social support and knowledge sharing. The success of a DHSN is based on the number of participants and their consistent creation of externalities through the generation of new content. To promote network growth, it would be helpful to identify characteristics of superusers or actors who create value by generating positive network externalities.ObjectiveThe aim of the study was to investigate the feasibility of developing predictive models that identify potential superusers in real time. This study examined associations between posting behavior, 4 demographic variables, and 20 indication-specific variables.MethodsData were extracted from the custom structured query language (SQL) databases of 4 digital health behavior change interventions with DHSNs. Of these, 2 were designed to assist in the treatment of addictions (problem drinking and smoking cessation), and 2 for mental health (depressive disorder, panic disorder). To analyze posting behavior, 10 models were developed, and negative binomial regressions were conducted to examine associations between number of posts, and demographic and indication-specific variables.ResultsThe DHSNs varied in number of days active (3658-5210), number of registrants (5049-52,396), number of actors (1085-8452), and number of posts (16,231-521,997). In the sample, all 10 models had low R2 values (.013-.086) with limited statistically significant demographic and indication-specific variables.ConclusionsVery few variables were associated with social network engagement. Although some variables were statistically significant, they did not appear to be practically significant. Based on the large number of study participants, variation in DHSN theme, and extensive time-period, we did not find strong evidence that demographic characteristics or indication severity sufficiently explain the variability in number of posts per actor. Researchers should investigate alternative models that identify superusers or other individuals who create social network externalities.
Highlights
BackgroundDigital health social networks (DHSNs), otherwise known as discussion forums or peer-to-peer support groups, are in abundance [1,2,3,4,5,6,7,8]
Digital health social networks (DHSNs) are widespread, and the consensus is that they contribute to wellness by offering social support and knowledge sharing
Data were extracted from the custom structured query language (SQL) databases of 4 digital health behavior change interventions with DHSNs
Summary
Digital health social networks (DHSNs), otherwise known as discussion forums or peer-to-peer support groups, are in abundance [1,2,3,4,5,6,7,8]. The efficacy of these networks is still being evaluated, the consensus is that social support and knowledge sharing increase patient education, enhance self-management, and decrease burden on existing health services [9,10,11,12,13,14,15,16]. In the case of DHSNs, analysis is turning to mechanisms that drive growth, help attain sustainability, and generate positive network externalities. Digital health social networks (DHSNs) are widespread, and the consensus is that they contribute to wellness by offering social support and knowledge sharing. It would be helpful to identify characteristics of superusers or actors who create value by generating positive network externalities
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