title: Employing the Gini coefficient to measure participation inequality in treatment-focused Digital Health Social Networks.
abstract: Digital Health Social Networks (DHSNs) are common; however, there are few metrics that can be used to identify participation inequality. The objective of this study was to investigate whether the Gini coefficient, an economic measure of statistical dispersion traditionally used to measure income inequality, could be employed to measure DHSN inequality. Quarterly Gini coefficients were derived from four long-standing DHSNs. The combined data set included 625,736 posts that were generated from 15,181 actors over 18,671 days. The range of actors (8-2323), posts (29-28,684), and Gini coefficients (0.15-0.37) varied. Pearson correlations indicated statistically significant associations between number of actors and number of posts (0.527-0.835,p< .001), and Gini coefficients and number of posts (0.342-0.725,p< .001). However, the association between Gini coefficient and number of actors was only statistically significant for the addiction networks (0.619 and 0.276,p< .036). Linear regression models had positive but mixedR2results (0.333-0.527). In all four regression models, the association between Gini coefficient and posts was statistically significant (t= 3.346-7.381,p< .002). However, unlike the Pearson correlations, the association between Gini coefficient and number of actors was only statistically significant in the two mental health networks (t= -4.305 and -5.934,p< .000). The Gini coefficient is helpful in measuring shifts in DHSN inequality. However, as a standalone metric, the Gini coefficient does not indicate optimal numbers or ratios of actors to posts, or effective network engagement. Further, mixed-methods research investigating quantitative performance metrics is required.
authors.affiliation: Henley Business School, University of Reading, Greenlands, Henley-on-Thames, RG9 3AU UK ; Evolution Health Systems Inc., 900 King Streeet West, Suite 401, Toronto, M5V 3H5 Canada.
authors.forename: Trevor
authors.identifier: 0000-0002-5675-0084
authors.initials: T
authors.lastname: van Mierlo
authors.affiliation: Rotman School of Management, University of Toronto, 105 St George Street, Toronto, M5S 3E6 Canada.
authors.forename: Douglas
authors.initials: D
authors.lastname: Hyatt
authors.affiliation: Rotman School of Management, University of Toronto, 105 St George Street, Toronto, M5S 3E6 Canada.
authors.forename: Andrew T
authors.initials: AT
authors.lastname: Ching
keywords: Econometrics; Gini coefficient; Online support groups; Social networks; Superusers