Analyzing and Predicting User Participations in Online Health Communities: A Social Support Perspective.

BACKGROUND: Online health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with similar peers to seek, receive, and provide different types of social support, such as informational support, emotional support, and companionship. As active participations in an OHC are beneficial to both the OHC and its users, it is important to understand factors related to users' participations and predict user churn for user retention efforts. OBJECTIVE: This study aimed to analyze OHC users' Web-based interactions, reveal which types of social support activities are related to users' participation, and predict whether and when a user will churn from the OHC. METHODS: We collected a large-scale dataset from a popular OHC for cancer survivors. We used text mining techniques to decide what kinds of social support each post contained. We illustrated how we built text classifiers for 5 different social support categories: seeking informational support (SIS), providing informational support (PIS), seeking emotional support (SES), providing emotional support (PES), and companionship (COM). We conducted survival analysis to identify types of social support related to users' continued participation. Using supervised machine learning methods, we developed a predictive model for user churn. RESULTS: Users' behaviors to PIS, SES, and COM had hazard ratios significantly lower than 1 (0.948, 0.972, and 0.919, respectively) and were indicative of continued participations in the OHC. The churn prediction model based on social support activities offers accurate predictions on whether and when a user will leave the OHC. CONCLUSIONS: Detecting different types of social support activities via text mining contributes to better understanding and prediction of users' participations in an OHC. The outcome of this study can help the management and design of a sustainable OHC via more proactive and effective user retention strategies.

Phase II open label, multi-center clinical trial of modulation of intermediate endpoint biomarkers by 1α-hydroxyvitamin D2 in patients with clinically localized prostate cancer and high grade pin.

BACKGROUND: Prostate cancer is the most common malignancy and second leading cause of cancer related deaths in American men supporting the study of prostate cancer chemoprevention. Major risk factors for this disease have been associated with low serum levels of vitamin D. Here, we evaluate the biologic activity of a less calcemic vitamin D analog 1α-hydroxyvitamin D2 [1α-OH-D2] (Bone Care International, Inc.) in patients with prostate cancer and high grade prostatic intraepithelial neoplasia (HG PIN). METHODS: Patients with clinically organ-confined prostate cancer and HG PIN were randomized to 1α-OH-D2 versus placebo for 28 days prior to radical prostatectomy. Intermediate endpoint biomarkers included serum vitamin D metabolites, TGFß 1/2, free/total PSA, IGF-1, IGFBP-3, bFGF, and VEGF. Tissue endpoints included histology, MIB-1 and TUNEL staining, microvessel density and factor VIII staining, androgen receptor and PSA, vitamin D receptor expression and nuclear morphometry. RESULTS: The 1α-OH-D2 vitamin D analog was well tolerated and could be safely administered with good compliance and no evidence of hypercalcemia over 28 days. While serum vitamin D metabolite levels only slightly increased, evidence of biologic activity was observed with significant reductions in serum PTH levels. TGF-ß2 was the only biomarker significantly altered by vitamin D supplementation. Whether reduced TGF-ß2 levels in our study is an early indicator of response to vitamin D remains unclear. CONCLUSIONS: While further investigation of vitamin D may be warranted based on preclinical studies, results of the present trial do not appear to justify evaluation of 1α-OH-D2 in larger clinical prostate cancer prevention studies.