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

社交网络对癌症生存试验的剂量和效果的影响

基本信息

批准号：
8743188
负责人：
Erin O'Carroll Bantum
金额：
$ 16.29万
依托单位：
PALO ALTO VETERANS INSTIT FOR RESEARCH
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-09-26 至 2017-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8743188
关键词：
Advanced Development Behavior Therapy Behavioral Benchmarking Beryllium Cancer Survivor Cancer Survivorship Chronic Communities Computers Data Distress Dose Effectiveness Elements Emotional Exercise Exposure to Feedback Goals Health Health Services Research Health behavior change Individual Internet Intervention Linguistics Literature Malignant Neoplasms Measures Mental Depression Methods Monitor Outcome Participant Pathway Analysis Patient Self-Report Pattern Phase Property Public Health Publishing Reporting Research Research Personnel Risk Social Interaction Social Network Social support Stress Subgroup Survivors Testing Text Time base behavior measurement common treatment eHealth effective intervention experience health care quality improved member novel public health relevance randomized trial skills training survivorship systematic review time use tool web site

项目摘要

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.

描述(由申请人提供)：开发从电子健康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，对于确定和评估这些干预措施的潜在行动机制至关重要。了解用户如何参与干预内容、个人参与所嵌入的社交网络以及用户之间发生的交互的质量将有助于改进现有的电子健康干预并使其更有效。只有几种基于互联网的干预措施在癌症幸存者中进行了测试，大多数随机试验都显示出积极结果的证据。在显示出积极效果的研究中，一个常见的治疗因素是使用社交网络功能，如专业促进、讨论板、私人消息和其他方式，让幸存者与类似的人建立个人联系。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学工具之间的几个巨大差距。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参与干预，包括使用干预的时间、进行技能培训练习的时间和字数。其次，这项研究将确定参与者之间互动质量的特定社交网络属性和标记，可用于预测干预参与度和随时间推移的结果。本研究的目的是：1)识别、表征和比较STC和HSN干预措施的社会网络属性，2)评估社会网络属性和社会互动质量对干预暴露(即治疗剂量)的纵向影响，3)评估社会网络属性和社会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：存活和兴旺癌症(STC，n=352)和Health-Space.net(HSN，n=231)。将对语言(即文本)数据进行自动文本分析以生成交互质量的标记。参与者之间的交互模式将被用来生成参与者-其他矩阵，该矩阵将接受社会网络分析。网站使用数据将被用来生成每次干预的个人参与度标记。统计分析将被用来评估社交网络属性和互动质量对参与度和结果的影响。这些结果可以用来快速识别低参与度干预的风险亚群，根据社交网络属性或交互质量定制干预内容，或对其他基于群体的电子健康干预的网络属性进行基准测试。鉴于基于互联网的干预措施覆盖范围很广，即使是相对温和的成果改进(例如，通过提高参与度)也有可能极大地改善这类干预措施对公共卫生的影响。