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Mentoring and research in applying digital technologies to prevent chronic Illnesses

应用数字技术预防慢性病的指导和研究

基本信息

批准号：
9732635
负责人：
YOSHIMI FUKUOKA
金额：
$ 18.34万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-01 至 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9732635
关键词：
Accelerometer Address Adherence Adult Algorithms Area Award Back Behavior Behavioral California Car Phone Cardiovascular Diseases Cardiovascular system Cellular Phone Chronic Disease Clip Complex Data Data Analyses Data Collection Development Diabetes Mellitus Diabetes prevention Discipline Electronic Mail Emerging Technologies Environment Epidemic Evaluation Failure Feedback Frequencies Funding Future Goals Guidelines Individual International Intervention Intervention Trial Leadership Link Machine Learning Measures Mentors Mentorship Methods Mid-Career Clinical Scientist Award (K24)Midcareer Investigator Award in Patient-Oriented Research Modeling Monitor Non-Insulin-Dependent Diabetes Mellitus Obesity Participant Patient Self-Report Physical activity Primary Prevention Principal Investigator Randomized Controlled Clinical Trials Recommendation Research Research Design Research Personnel Role Running Sample Size San Francisco School Nursing Secure Specific qualifier value Technology Testing Text Messaging Thinness Time To specify Training Treatment Failure Treatment Protocols United States United States National Institutes of Health Universities Visit Wireless Technology base career cost cost effective design diaries digital exercise intervention improved individualized medicine innovation lifestyle intervention mobile application mobile computing patient oriented research pedometer physical model prediction algorithm predictive modeling prevent programs public health relevance skills social media tool training opportunity

项目摘要

DESCRIPTION (provided by applicant): For this K24 midcareer investigator award in patient-oriented research (POR) award, my goals are: 1) to become an international leader in the primary prevention of type 2 diabetes and cardiovascular diseases by applying emerging digital technologies and social media; 2) to conduct high-impact research in testing innovative, cost-effective lifestyle interventions (physical activity) using machine leaning; and 3) to provide a hih quality mentorship and training environment that supports the conduct of high POR impact research in cardiovascular and diabetes prevention. This K24 award will allow me to take on a broader and more formal mentorship role not only in the School of Nursing but also throughout the University of California, San Francisco campus. The overall goals of the new research in this application are to develop decision rule algorithms and quantitative models to optimize real-time predictions of future physical activity intervention adherences and to pilot test these algorithms and models in an intervention trial. The specific aims are: 1) to determine the extent to which baseline factors can predict adherence in self-monitoring (using mobile apps and wearing advanced accelerometers); 2) to determine the extent to which early app-based self-monitoring and evaluation (e.g., advanced accelerometers and diary use, and step counts in the first few weeks) can be optimized to make real-time predictions of future treatment failures; 3) to assess the feasibility of the "Physical Activity Plus" intervention using developed real-time prediction models (developed in Aim 2), compared to a "regular physical activity" intervention, for changes in total daily steps and duration of moderate and intensive physical activity over a three-month period. To tailor intervention intensity, we will evaluate the frequency of study visits and real-time feedback to minimize treatment failures and maximize adherence. Junior investigators in the POR have adapted digital mobile technologies as part of their research tools and have generated large volumes of continuous data. However, only a few training opportunities are available in this research area; in particular, for methods to rigorously and fully utilize the richness of the data collected. Therefore, the proposed K24 application will also meet these mentees' urgent needs across disciplines.

描述（由申请人提供）：对于这个K24职业中期研究者奖在以患者为导向的研究（POR）奖，我的目标是：1）通过应用新兴的数字技术和社交媒体，成为2型糖尿病和心血管疾病一级预防的国际领导者; 2）在测试创新的、具有成本效益的生活方式干预措施方面进行高影响力的研究（身体活动）使用机器学习;和3）提供高质量的指导和培训环境，以支持在心血管和糖尿病预防方面进行高POR影响研究。这个K24奖将使我能够承担更广泛，更正式的指导作用，不仅在护理学院，而且在整个加州大学弗朗西斯科分校。这项新研究的总体目标是开发决策规则算法和定量模型，以优化对未来体力活动干预依从性的实时预测，并在干预试验中对这些算法和模型进行初步测试。具体目标是：1）确定基线因素可以在多大程度上预测自我监测的依从性（使用移动的应用程序和佩戴高级加速度计）; 2）确定早期基于应用程序的自我监测和评估（例如，先进的加速计和日记使用，以及最初几周的步数）可以优化，以实时预测未来的治疗失败; 3）使用开发的实时预测模型评估“体力活动+”干预的可行性（在目标2中开发），与“定期身体活动”干预相比，在三个月的时间里，每天的总步数和中等强度体力活动的持续时间的变化。为了调整干预强度，我们将评估研究访视和实时反馈的频率，以最大限度地减少治疗失败并最大限度地提高依从性。POR的初级研究人员采用数字移动的技术作为其研究工具的一部分，并生成了大量连续数据。然而，在这一研究领域只有少数培训机会，特别是严格和充分利用收集到的丰富数据的方法。因此，拟议的K24应用程序也将满足这些学员跨学科的迫切需求。