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间风险状态的新方法行为风险因素，如缺乏身体活动，压力管理不善，饮食不良和吸烟，是约80%的冠心病和脑血管疾病的原因（WHO, 2011），它们是其他负面健康结果的部分原因，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。它还可以帮助个人控制骨关节炎、更年期和其他影响健康和生活质量的身体问题的症状。本次BD2K K01提案的目的是建立自己在健康行为改变与维护大数据方法开发与应用方面的独立研究人员地位。我渴望通过在生物行为健康研究中发展、扩展和应用大数据方法来改善公众健康，帮助个人发展和保持积极的健康行为，特别是那些与体育活动、饮食和压力管理有关的行为。为了实现这一目标，我的培训计划侧重于培养以下方面的专业知识：(a)分析大型、复杂、高维数据的统计方法；(b)

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.