Demographic and Indication-Specific Characteristics Have Limited Association With Social Network Engagement: Evidence From 24,954 Members of Four Health Care Support Groups
Open accessAbstract
BACKGROUND: Digital 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. OBJECTIVE: The 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. METHODS: Data 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. RESULTS: values (.013-.086) with limited statistically significant demographic and indication-specific variables. CONCLUSIONS: Very 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.
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Demographics (user profiles) do not predict engagement.
“there is evidence that such demographics (user profiles) do not predict engagement [42].”
From Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data (2017) · cites this paper as [42]
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References 105 · Cited by 14
Research works printed but not linked: 3
- [52]Baye MR Advanced Topics in Business Strategy Managerial EconomicsBusiness Strategy. 7 ed 2010 New York, NY The McGraw-Hill Companies Inc
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- Authors
- van Mierlo, T., Li, X., Hyatt, D., & Ching, A. T.
- Affiliations
- University of Toronto (CA)
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2017-02-17
- Volume
- 19
- Issue
- 2
- Pages
- e40
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC5336601
- OpenAlex
- W2587979906
- Cited by
- 14
- References
- 105
- Citations by year
- 2025: 1 · 2024: 1 · 2022: 3 · 2020: 1 · 2019: 1 · 2018: 3 · 2017: 4
- FWCI
- 2.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- CentAUR (University of Reading) (acceptedVersion)
- Topics
- Social Sciences › Health › Social Media in Health Education
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- superusers, network effects, posting behavior, demographic predictors, addiction treatment, smoking cessation, problem drinking, depressive disorders, panic disorder, mental health, behaviour change interventions, predictive modeling
- MeSH terms
- Adult, Female, Humans, Male, Middle Aged, Self-Help Groups, Social Support, Internet, Social Networking
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