Mental disorder recovery correlated with centralities and interactions on an online social network
health-care; self-help; patientslikeme; illness; support; access; media
Recent research has established both a theoretical basis and strong empirical evidence that effective social behavior plays a beneficial role in the maintenance of physical and psychological well-being of people. To test whether social behavior and well-being are also associated in online communities, we studied the correlations between the recovery of patients with mental disorders and their behaviors in online social media. As the source of the data related to the social behavior and progress of mental recovery, we used PatientsLikeMe (PLM), the world's first open-participation research platformfor the development of patient-centered health outcome measures. We first constructed an online social network structure based on patient-to-patient ties among 200 patients obtained from PLM. We then characterized patients' online social activities by measuring the numbers of "posts and views" and " helpful marks" each patient obtained. The patients' recovery data were obtained from their self-reported status information that was also available on PLM. We found that some node properties (in-degree, eigenvector centrality and PageRank) and the two online social activity measures were significantly correlated with patients' recovery. Furthermore, we re-collected the patients' recovery data two months after the first data collection. We found significant correlations between the patients' social behaviors and the second recovery data, which were collected two months apart. Our results indicated that social interactions in online communities such as PLM were significantly associated with the current and future recoveries of patients with mental disorders.
Ma, X., & Sayama, H. (2015). Mental disorder recovery correlated with centralities and interactions on an online social network. PeerJ, 3, e1163.
Ma, Xinpei P. and Sayama, Hiroki, "Mental disorder recovery correlated with centralities and interactions on an online social network" (2015). Systems Science and Industrial Engineering Faculty Scholarship. 6.