Impact of Social Network Structure on Stroke Recovery

社交网络结构对中风康复的影响

• 批准号: 9150302
• 负责人: Amar Dhand
• 金额: $ 15.67万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-12-01 至 2020-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9150302
• 关键词: Address; Adult; Age; Area; Automobile Driving; Behavior; Behavior Therapy; Biometry; Caregiver Burden; Caregivers; Clinical Investigator; Clinical Research; Communities; Data; Development; Disease; Educational Curriculum; Elements; Emotional; Failure; Family; Friendships; Future; Goals; Home environment; Human; Hypertension; Impairment; Individual; Influentials; Institution; Intervention; Intervention Studies; Interview; Ischemic Stroke; Knowledge; Lead; Maps; Measures; Mediating; Mediation; Mediator of activation protein; Mental Depression; Mentored Patient-Oriented Research Career Development Award; Mentors; Methodological Studies; Methodology; Methods; Modeling; Morbidity - disease rate; Natural History; Nature; Network-based; Neurologic; Neurologist; Outcome; Outcomes Research; Pathway Analysis; Patients; Persons; Physical Function; Play; Predictive Value; Prospective cohort study; Public Health; Recovery; Recruitment Activity; Regression Analysis; Rehabilitation therapy; Research; Research Personnel; Science; Scientist; Severities; Social Environment; Social Network; Social isolation; Social support; Statistical Data Interpretation; Stroke; Structure; Surveys; Testing; Time; Training; United States National Institutes of Health; Universities; Washington; Work; addiction; base; career; cohort; density; design; executive function; functional outcomes; health care service utilization; improved; innovation; member; multidisciplinary; novel; patient oriented; primary outcome; professor; programs; public health relevance; secondary outcome; social; stroke recovery; stroke survivor; theories

 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.

 描述（由申请人提供）：Amar Dhand 博士是一位神经科医生和年轻研究者，致力于以患者为中心的社交网络结构和缺血性中风恢复的临床研究。 K23 奖项将使 Dhand 博士能够通过高级社交网络分析、生物统计学和干预研究三个领域的培训来实现其成为独立临床研究者的长期职业目标。 Dhand 博士招募了一支多学科导师团队，并设计了详细的课程来实现这一目标。此次培训将在圣路易斯华盛顿大学进行，该机构在培训临床科学家方面拥有悠久的历史。他的导师包括他的主要顾问 Jin-Moo Lee 博士（一位擅长转化中风研究的神经科医生）、Doug Luke 博士（一位擅长社交网络分析的公共卫生教授）和 Catherine Lang 博士（一位擅长纵向中风结果研究的康复研究员）。他的项目首先认识到中风康复中的社会机制具有影响力且研究不足。社会隔离与康复不良相关，而增加社会支持和社区参与则与康复改善相关。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未能改善中风康复或减轻护理人员负担。目前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，照顾者与家庭与友谊团体）、干预时间（例如，中风后立即或延迟）或计划的持续时间和效力造成的。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，使用一种新颖的方法——社交网络分析——来定量绘制患者周围的社会结构及其对康复的预测价值。社交网络分析基于这样的理论：通过分析个体周围的社会关系结构可以最充分地理解人类行为。随后，这些数据可能会为网络干预措施提供信息，这些干预措施对其他疾病（例如成瘾症和高血压）有效。我们假设中风幸存者的个人社交网络将变得更小、更密集，尤其是那些患有更严重中风的人，并且中风发作时的某些网络变量将通过特定的中介因素独立预测功能结果。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的中风后社交网络结构的变化；目标 2 将评估中风发作时社交网络变量对中风结果的预测价值；目标 3 将确定调节社交网络与中风结果之间关系的因素。这项研究意义重大，因为它将展示中风恢复中网络结构的自然历史、它们与中风结果的关系以及网络和恢复之间的中介因素。该项目具有创新性，因为它引入了一种新颖的分析框架，挑战了当前的社会支持模型，并改善了社会支持干预措施的理论基础。