Novel Methods to Identify Momentary Risk States for Stress & Physical Inactivity

描述(由申请人提供)：识别压力和缺乏身体活动的瞬时风险状态的新方法行为风险因素，如缺乏体育锻炼、不良的压力管理、不良饮食和吸烟，是导致约80%的冠心病和脑血管疾病的原因(世卫组织，2011年)，它们也是其他负面健康后果的部分原因，如高血脂、高血压、癌症、糖尿病和肥胖。因此，帮助个人改变不健康的行为并保持健康的行为可以降低心血管疾病、糖尿病和癌症的发病率和死亡率。它还可以帮助个人管理骨关节炎、更年期和其他影响健康和生活质量的身体问题的症状。这份BD2K K01提案的目的是将自己确立为健康行为改变和维护大数据方法开发和应用的独立研究员。我渴望通过在生物行为健康研究中开发、扩展和应用大数据方法来改善公共健康，以帮助个人发展和保持积极的健康行为，特别是与体育活动、饮食和压力管理相关的行为。为实现这一目标，我的培训计划侧重于发展以下方面的专门知识：(A)分析大型、复杂、高维数据的统计方法；(B) 计算机科学和信息学，以及用于访问、管理和处理大数据的高级高性能计算主题；以及(C)健康行为变化的理论和衡量，特别是关于密集评估体力活动和压力管理的理论和衡量。我的培训和研究活动将在李润泽博士(首席导师)、Vasant Honavar博士(联合导师)和Joshua Smyth博士(联合导师)的指导下进行。为了检查个人在体力活动、压力管理和其他健康行为中的参与度，我将利用这笔资金开发和应用大数据方法，这些方法可以整合多个时间尺度和研究的数据，更好地推断因果关系，并考虑数据中的依赖关系(例如，时间结构、二元组)。我将在技术和健康行为杂志上发表关于这些方法的手稿，并向临床研究人员分发软件，以便他们能够在工作中使用这些方法。在完成这笔拨款后，我将准备作为开发健康行为干预措施的跨学科团队的数据科学家做出重要贡献。这项工作将对公共卫生产生广泛影响，特别是对在实时、真实世界环境中提供的适应性、个体化、健康行为干预措施的发展具有广泛影响。

DESCRIPTION (provided by applicant): Novel Methods to Identify Momentary Risk States for Stress & Physical Inactivity Behavioral risk factors, such as physical inactivity, poor stress management, poor diet, and smoking, are responsible for about 80% of coronary heart disease and cerebrovascular disease (WHO, 2011), and they are partly responsible for other negative health outcomes, such as high lipids, high blood pressure, cancer, diabetes, and obesity. Thus, helping individuals to change unhealthy behaviors and maintain healthy ones can decrease morbidity and mortality from cardiovascular disease, diabetes, and cancer. It can also help individuals manage symptoms of osteoarthritis, menopause, and other physical issues that impact health and quality of life. This BD2K K01 proposal is for the purpose of establishing myself as an independent researcher in the development and application of big data methods for health-behavior change and maintenance. I aspire to improve public health by developing, extending, and applying big data methods in biobehavioral health research to help individuals develop and maintain positive health behaviors, specifically those related to physical activity, diet, and stress management. To achieve this goal, my training plan focuses on developing expertise in (a) statistical methods for the analysis of large, complex, high-dimensional data; (b) computer science and informatics, along with advanced high-performance computing topics for accessing, managing, and processing big data; and (c) the theories and measurement of health behavior change, specifically with regards to intensive assessment of physical activity and stress management. My training and research activities will take place under the mentorship of Drs. Runze Li (primary mentor), Vasant Honavar (co-mentor), and Joshua Smyth (co-mentor). In order to examine individuals' engagement in physical activity, stress management, and other health behaviors, I will use this funding to develop and apply big data methods that can integrate data across multiple time scales and studies, better infer causality, and account for dependencies (e.g., time-structure, dyads) in the data. I will publish manuscripts on these methods in both technical and health-behavior journals, and I will disseminate software to clinical researchers so that they can use these methods in their work. At the completion of this grant, I will be prepared to make important contributions as the data scientist on interdisciplinar teams that develop health behavior interventions. This work will have broad implications for public health, in particular for the development of adaptive, individualized, health-behavior interventions delivered in real-time, real-world contexts.