Impact of Social Network Structure on Stroke Recovery

 简介(申请人提供)：Amar Dhandd博士是一名神经学家和年轻的研究员，致力于以患者为导向的社会网络结构和缺血性中风康复的临床研究。K23奖项将使德汉德博士实现他的长期职业目标，即通过三个领域的培训成为一名独立的临床调查员：高级社会网络分析、生物统计学和干预研究。德汉德博士招募了一个由多学科导师组成的团队，并为实现这一目标设计了详细的课程。这项培训将在圣路易斯的华盛顿大学进行，这是一所在培训临床医生科学家方面有着悠久记录的机构。他的导师包括他的主要顾问李振武博士，他是一名在转化性中风研究方面有专长的神经科医生，道格·卢克博士，一位在社会网络分析方面有专长的公共卫生教授，以及凯瑟琳·朗博士，一位康复调查员，在纵向中风结果研究方面有专长。他的项目始于对中风康复中的社会机制的理解，即中风康复的社会机制具有影响力，但研究不足。社会孤立与较差的恢复有关，而社会支持和社区参与的增加与恢复的改善有关。尽管有这些知识，但社会因素影响康复的机制尚不清楚，这导致了多种社会支持干预措施，但未能改善中风的康复或减轻照顾者的负担。目前尚不清楚这一失败是否由于不适当的社会单位目标(例如，照顾者与家庭或友谊团体)、干预的时机(例如，中风后立即或延迟)或计划的持续时间和效力所致。为了解决这一差距，他提出了一项针对200名中风幸存者的前瞻性队列研究，使用一种新的方法-社会网络分析-来定量绘制患者周围的社会结构及其对康复的预测价值。社会网络分析的理论基础是，通过分析个人周围的社会关系结构来最充分地理解人的行为。随后，这些数据可能会为对其他疾病有效的网络干预提供信息，例如成瘾障碍和高血压。我们假设，中风幸存者的个人社交网络将变得更小和更密集，特别是在那些中风更严重的人，中风发病时的某些网络变量将通过特定的中介独立预测功能结果。这一假设将通过以下具体目标进行检验：目标1将确定不同严重程度的中风后社会网络结构的变化；目标2将评估中风发病时社会网络变量对中风结果的预测价值；目标3将确定社会网络和中风结果之间关系的中介因素。这项研究意义重大，因为它将展示中风康复过程中网络结构的自然历史，它们与中风预后的关系，以及网络和康复之间的中介作用。这个项目具有创新性，因为它引入了一个新的分析框架，挑战了现有的社会支持模式，并改进了社会支持干预的理论基础。

 DESCRIPTION (provided by applicant): Dr. Amar Dhand is a neurologist and young investigator who pursues patient-oriented clinical research on social networks structure and ischemic stroke recovery. A K23 award will allow Dr. Dhand to fulfill his long-term career goal of becoming an independent clinical investigator through training in three areas: advanced social network analysis, biostatistics, and intervention research. Dr. Dhand has recruited a multidisciplinary team of mentors and designed a detailed curriculum to accomplish this goal. This training will take place at Washington University in St. Louis, an institution with a long trak record of training clinician scientists. His mentors include Dr. Jin-Moo Lee, his primary advisor, who is a neurologist with expertise in translational stroke research, Dr. Doug Luke, a public health professor with expertise in social network analysis, and Dr. Catherine Lang, a rehabilitation investigator with expertise in longitudinal stroke outcomes research. His project begins with the understanding that social mechanisms in stroke recovery are influential and understudied. Social isolation is associated with poor recovery, while increased social support and community engagement is associated with improved recovery. Despite this knowledge, the mechanisms by which social factors influence recovery is unknown, and this has led to multiple social support interventions that have failed to improve stroke recovery or reduce caregiver burden. It is unknown whether this failure is due to an inappropriate social unit target (e.g., caregiver versus family versus friendship group), timing of intervention (e.g., immediately after stroke or delayed), or duration and potency of the program. To address this gap, he proposes a prospective cohort study of 200 stroke survivors using a novel methodology-social network analysis-to quantitatively map the social structure around a patient and its predictive value on recovery. Social network analysis is based on the theory that human behavior is most fully understood by analysis of the structure of social relations around an individual. These data may, subsequently, inform network interventions that have been efficacious in other diseases, such as addiction disorders and hypertension. We hypothesize that stroke survivors' personal social networks will become smaller and denser especially in those with more severe strokes, and certain network variables at stroke onset will independently predict functional outcomes through specific mediators. This hypothesis will be tested by the following specific aims: Aim 1 will determine changes in social network structure after stroke of varying severity; Aim 2 will assess the predictive value of social network variables at stroke onset on stroke outcomes; Aim 3 will determine the factors that mediate the relationship between social networks and stroke outcomes. This study is significant because it will show the natural history of network structures in stroke recovery, their relation to stroke outcomes, and the mediators between networks and recovery. This project is innovative because it introduces a novel analytical framework that challenges current social support models and improves the theoretical underpinnings of social support interventions.