van Mierlo Trevor, Li Xinlong, Hyatt Douglas, Ching Andrew T
Research Associate, Henley Business School, University of Reading, Henley-on-Thames, United Kingdom.
Evolution Health Systems Inc, Toronto, ON, Canada.
J Med Internet Res. 2017 Feb 17;19(2):e40. doi: 10.2196/jmir.6330.
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.
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.
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.
The 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 R values (.013-.086) with limited statistically significant demographic and indication-specific variables.
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.
数字健康社交网络（DHSN）广泛存在，人们普遍认为它们通过提供社会支持和知识共享来促进健康。DHSN的成功基于参与者的数量以及他们通过生成新内容持续创造外部效应。为了促进网络增长，识别通过产生积极网络外部效应创造价值的超级用户或参与者的特征会有所帮助。
本研究的目的是调查开发实时识别潜在超级用户的预测模型的可行性。本研究考察了发帖行为、4个人口统计学变量和20个特定适应症变量之间的关联。
数据从4项使用DHSN的数字健康行为改变干预措施的自定义结构化查询语言（SQL）数据库中提取。其中，2项旨在协助治疗成瘾（问题饮酒和戒烟），2项用于心理健康（抑郁症、恐慌症）。为了分析发帖行为，开发了10个模型，并进行负二项回归以检验发帖数量与人口统计学和特定适应症变量之间的关联。
DHSN在活跃天数（3658 - 5210天）、注册人数（5049 - 52396人）、参与者数量（1085 - 8452人）和发帖数量（16231 - 521997条）方面存在差异。在样本中，所有10个模型的R值都很低（0.013 - 0.086），具有统计学意义的人口统计学和特定适应症变量有限。
与社交网络参与相关的变量极少。虽然一些变量具有统计学意义，但它们似乎没有实际意义。基于大量的研究参与者、DHSN主题的差异以及较长的时间段，我们没有找到有力证据表明人口统计学特征或适应症严重程度足以解释每个参与者发帖数量的变异性。研究人员应研究识别超级用户或其他创造社交网络外部效应的个体的替代模型。
