Impact of Social Networking on Dose and Effects of Cancer Survivorship Trials

描述（由申请人提供）：开发从电子卫生干预中提取和分析客观行为、语言和社会网络数据的实用方法，对于识别和评估这些干预的潜在行动机制至关重要。了解用户如何参与干预内容、嵌入个人参与的社交网络以及用户之间互动的质量，将有助于改进现有的电子卫生干预措施并使其更有效。只有少数基于互联网的干预措施对癌症幸存者进行了测试，大多数随机试验都显示出积极结果的证据。在显示积极影响的研究中，一个常见的治疗因素是使用社交网络功能，如专业便利、讨论板、私人信息，以及幸存者与相似的人建立个人联系的其他方式。这项研究将填补在基于互联网的干预措施分析中可用的方法学工具之间的几个巨大空白。首先，研究将使用客观的行为数据来识别个体的几个纵向标记

DESCRIPTION (provided by applicant): Developing practical methods for extracting and analyzing objective behavioral, linguistic, and social- networking data from e-health interventions is crucial for identifying and evaluating potential mechanisms of action for these interventions. Understanding how users engage with intervention content, the social networks in which individual engagement is embedded, and the quality of the interactions that occur between users will be instrumental in improving existing e-health interventions and making them more effective. Only a handful of Internet-based interventions have been tested for cancer survivors, and most randomized trials have shown evidence of positive outcomes. Among the studies showing positive effects, a common treatment element is the use of social-networking features, such as professional facilitation, discussion boards, private messages, and other ways for survivors to make personal connections with similar others. This study will plug several large gaps among methodological tools available for analysis of Internet-based interventions. First, the study will use objective behavioral data to identify several longitudinal markers of individual engagement with an intervention, including time spent using the intervention, time spent in skills-training exercises, and word count. Second, the study will identify specific social-networking attributes and markers of the quality of interactions between participants that can be used to predict intervention engagement and outcomes over time. The aims of the study are: 1) to identify, characterize, and compare social-networking attributes of the STC and HSN interventions, 2) to evaluate the longitudinal effects of social-networking attributes and social interaction quality on exposure to the intervention (i.e., dose of treatment), and 3) to evaluate the effects of social-networking attributes and social interaction quality on outcomes of the interventions. Behavioral, linguistic, and self-report data will be derived from two of the largest Internet-based survivorship trials: Surviving and Thriving Cancer (STC, n = 352) and Health-Space.net (HSN, n = 231). Linguistic (i.e., text) data will be subjected to automated text analysis to generate markers of interaction quality. Patterns of interactions between participants will be used to generate actor-other matrices, which will be subjected to social network analysis. Website use data will be used to generate markers of individual engagement with each intervention. Statistical analyses will be used to evaluate the effects of social-networking attributes and interaction quality on engagement and outcomes across time. These results could be used to quickly identify subgroups at risk for low-engagement with an intervention, to tailor intervention content based on social network attributes or interaction quality, or to benchmark the network properties of other group-based e-health interventions. Given the substantial reach available to Internet-based interventions, even relatively modest improvements to outcomes (e.g., by improving levels of engagement) would have the potential to greatly improve the public health impact of these kinds of interventions.