Impact of Social Network Structure on Stroke Recovery

 描述（由申请人提供）：Amar Dhand博士是一名神经学家和年轻的研究者，他从事以患者为导向的社交网络结构和缺血性中风恢复的临床研究。K23奖将使Dhand博士能够通过三个领域的培训实现他成为独立临床研究者的长期职业目标：高级社交网络分析，生物统计学和干预研究。Dhand博士招募了一个多学科的导师团队，并设计了一个详细的课程来实现这一目标。这项培训将在圣路易斯的华盛顿大学进行，该大学在培训临床科学家方面有着悠久的历史。他的导师包括他的主要顾问Jin-Moo Lee博士，他是一位具有转化中风研究专业知识的神经学家，Doug Luke博士，一位具有社交网络分析专业知识的公共卫生教授，以及Catherine Lang博士，一位具有纵向中风结果研究专业知识的康复调查员。他的项目始于了解中风康复中的社会机制具有影响力且未得到充分研究。社会孤立与恢复不佳有关，而增加社会支持和社区参与与恢复改善有关。尽管有这些知识，但社会因素影响恢复的机制尚不清楚，这导致多种社会支持干预措施未能改善卒中恢复或减轻护理人员负担。尚不清楚这种失败是否是由于不适当的社会单位目标（例如，照顾者对家庭对友谊组），干预的时机（例如，中风后立即或延迟），或程序的持续时间和效力。为了解决这一差距，他提出了一个前瞻性队列研究200中风幸存者使用一种新的方法-社会网络分析-定量映射的社会结构周围的病人和其预测价值的恢复。社会网络分析是基于这样的理论，即通过分析个人周围的社会关系结构，可以最充分地理解人类行为。这些数据随后可能为网络干预提供信息，这些干预在其他疾病中有效，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，特别是在那些更严重的中风患者中，中风发作时的某些网络变量将通过特定的介质独立预测功能结果。该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卒中后社交网络结构的变化;目标2将评估卒中发作时社交网络变量对卒中结局的预测价值;目标3将确定介导社交网络与卒中结局之间关系的因素。这项研究意义重大，因为它将显示中风恢复中网络结构的自然历史，它们与中风结局的关系，以及网络和恢复之间的介质。这个项目是创新的，因为它引入了一个新的分析框架，挑战目前的社会支持模型，并提高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.