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

描述（由申请人提供）：开发用于从电子卫生干预措施中提取和分析客观行为、语言和社交网络数据的实用方法对于识别和评估这些干预措施的潜在作用机制至关重要。了解用户如何参与干预内容，个人参与嵌入其中的社交网络，以及用户之间发生的互动质量，将有助于改善现有的电子保健干预措施，使其更加有效。只有少数基于互联网的干预措施已经在癌症幸存者中进行了测试，大多数随机试验都显示了积极的结果。在显示积极效果的研究中，一个常见的治疗元素是使用社交网络功能，如专业促进，讨论板，私人信息，以及幸存者与类似他人建立个人联系的其他方式。这项研究将填补可用于分析基于互联网的干预措施的方法工具之间的几个巨大空白。首先，本研究将使用客观的行为数据来识别个体的几个纵向标记， 参与干预，包括使用干预所花费的时间、技能培训练习所花费的时间和字数。其次，该研究将确定特定的社交网络属性和参与者之间互动质量的标记，这些标记可用于预测干预参与和随时间推移的结果。这项研究的目的是：1）识别、表征和比较STC和HSN干预的社交网络属性，2）评估社交网络属性和社交互动质量对暴露于干预的纵向影响（即，治疗剂量），以及3）评估社交网络属性和社交互动质量对干预结果的影响。行为，语言和自我报告数据将来自两个最大的基于互联网的生存试验：生存和繁荣的癌症（STC，n = 352）和Health-Space.net（HSN，n = 231）。语言（即，文本）数据将进行自动文本分析 以生成交互质量的标记。参与者之间的互动模式将用于生成行为者-其他人矩阵，并将对其进行社会网络分析。网站使用数据将用于生成每个干预措施的个人参与标记。统计分析将用于评估社交网络属性和互动质量对参与度和结果的影响。这些结果可用于快速识别低参与干预风险的亚组，根据社交网络属性或交互质量定制干预内容，或对其他基于组的电子健康干预的网络属性进行基准测试。鉴于互联网干预措施的广泛覆盖面，即使对成果的改善相对有限（例如，通过提高参与水平）将有可能大大改善这类干预措施对公共卫生的影响。

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.