Informatics-Based Digital Application to Promote Safe Exercise in Middle-Aged Adults with Type 1 Diabetes

基于信息学的数字应用促进中年 1 型糖尿病患者的安全运动

• 批准号: 10831257
• 负责人: Garrett Igo Ash
• 金额: $ 13.43万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10831257
• 关键词: Address; Adherence; Adult; Affect; Affective; American; Bayesian Modeling; Behavior; Behavioral; Bioinformatics; Blood Glucose; Cardiovascular Diseases; Cardiovascular system; Caring; Client; Clinical; Clinical Trials; Computer software; Consultations; Continuous Glucose Monitor; Data; Data Storage and Retrieval; Development; Diet; Dimensions; Early Intervention; Educational workshop; Electronics; Elements; Etiology; Evaluation; Evidence based intervention; Exercise; Fatigue; Feasibility Studies; Feedback; Foundations; Fright; Future; Generations; Goals; Grant; Health; Health behavior change; Human; Hypertension; Hypoglycemia; Informatics; Instruction; Insulin; Insulin-Dependent Diabetes Mellitus; Intervention; Laboratories; Libraries; Mathematics; Measures; Medical; Mentors; Modeling; Modification; Monitor; Moods; Motivation; Obesity; Outcome; Participant; Patients; Pattern; Persons; Provider; Publishing; Recommendation; Reporting; Research; Safety; Self Efficacy; Sleep; Technology; Testing; Text; Text Messaging; Time; Training; Translating; United States National Institutes of Health; Waiting Lists; Work; Writing; attributable mortality; biobehavior; blood glucose regulation; data repository; diabetes management; diabetes self-management; diaries; digital; evidence base; exercise intervention; exercise prescription; feasibility trial; glycemic control; hazard; health goals; heuristics; improved; innovation; insight; insulin sensitivity; mHealth; machine learning classification; middle age; mobile application; model development; mortality risk; motivational enhancement therapy; novel; pilot test; precision medicine; prevent; programs; psychosocial; response; satisfaction; sedentary; skills; symposium; therapy development; tool

ABSTRACT Type 1 diabetes (T1D) affects ~1 million American adults and increases the risk of mortality attributable to cardiovascular disease by 800%. Current evidence-based T1D self-management interventions target glycemic control but ignore other modifiable health concerns prevalent in T1D such as hypertension and obesity. Exercise interventions could provide a novel solution if they could innovatively address the diabetes management and psychosocial challenges around exercise posed by T1D. Continuous glucose monitoring (CGM) allows patients and providers to comprehensively track the short- and long-term outcomes of exercise. Evidence-based interventions to translate CGM technology into sustainable adherence to exercise-related behaviors are lacking. Our human-delivered pilot intervention provided previously sedentary adults with T1D access to exercise videos and monthly client-centered discussions of their CGM and exercise data with an exercise coach. Participants said these improved exercise management behavioral skills and motivation, but only transiently. They stated a need for more frequent and sustained contact, requiring automated mobile tools that this proposal will develop. These tools include just-in-time adaptive text messages to overcome exercise barriers at times of vulnerability, weekly personalized reviews of short-term exercise safety hazards with tips to avoid them, and monthly personalized evaluation of long-term impact of exercise on blood glucose levels via Bayesian modeling. The program represents stage 1 of the NIH intervention development model: intervention generation, refinement, modification, adaptation. These steps will be accomplished by a feasibility study evaluating user satisfaction and mathematical robustness of an alpha version, using these results to modify the alpha version into a beta version, and then testing the beta version in a nonrandomized crossover clinical trial. Lastly, the databank of biobehavioral metrics generated by this trial (exercise, CGM, mood and sleep diaries for ~ 7,000 person-days) will be subjected to dimensionality reduction to identify biobehavioral subtypes of baseline and early intervention data. We will test whether these subtypes help predict longer-term intervention response and/or flag specific biobehavioral feature combinations that drive intervention responsiveness. These findings will lay a foundation for Dr. Ash’s future work developing precision medicine approaches. Alongside this research Dr. Ash will complete training in the domains of 1) diabetes management and technology; 2) mobile health (mHealth) intervention development; and 3) dimensionality reduction analytics. The training plan includes a strategic combination of mentor-led trainings, coursework, grant writing, and attendance at relevant conferences and workshops. Dr. Ash has assembled a mentoring team in T1D self-management and technology, multiple health behavior change intervention development, mHealth development, and informatics. FitscriptLLC and PiLR Health will provide customized intervention tools and data capture software. Dr. Gerstein’s laboratory will support data storage, processing, and analytics.

摘要 1型糖尿病（T1 D）影响约100万美国成年人，并增加了归因于以下疾病的死亡风险： 心血管疾病800%目前基于证据的T1 D自我管理干预措施针对血糖 控制，但忽视了其他可改变的健康问题，如高血压和肥胖症普遍存在于T1 D。行使 干预措施可以提供一个新的解决方案，如果他们可以创新地解决糖尿病管理， T1 D带来的运动心理挑战。动态血糖监测（CGM）允许患者 和供应商全面跟踪运动的短期和长期结果。循证 缺乏将CGM技术转化为可持续坚持运动相关行为的干预措施。 我们的人工交付试点干预为以前久坐的T1 D成年人提供了锻炼视频 以及每月以客户为中心的CGM讨论和与运动教练的运动数据。参与者 他说，这些改善了锻炼管理的行为技能和动机，但只是暂时的。他们说， 需要更频繁和持续的联系，需要本提案将开发的自动化移动的工具。 这些工具包括及时的适应性短信，以克服脆弱时期的锻炼障碍， 每周对短期运动安全隐患进行个性化审查，并提供避免这些隐患的提示， 通过贝叶斯建模对运动对血糖水平的长期影响进行个性化评估。的 该计划代表了NIH干预开发模型的第一阶段：干预生成，细化， 修改，适应。这些步骤将通过评估用户满意度的可行性研究完成， alpha版本的数学鲁棒性，使用这些结果将alpha版本修改为beta版本， 然后在非随机交叉临床试验中测试测试版。最后， 本试验生成的生物行为指标（约7，000人日的运动、CGM、情绪和睡眠日记） 将进行降维，以确定基线和早期干预的生物行为亚型 数据我们将测试这些亚型是否有助于预测长期干预反应和/或标志特异性 生物行为特征组合，驱动干预反应。这些发现将为 阿什博士未来的精准医学研究除了这项研究，阿什博士还将 完成以下领域的培训：1）糖尿病管理和技术; 2）移动的健康（mHealth） 干预开发;和3）降维分析。培训计划包括一个战略 结合导师主导的培训，课程，赠款写作，并出席相关会议， 工作坊. Ash博士组建了一个T1 D自我管理和技术指导团队， 行为改变干预发展、移动健康发展和信息学。FitscriptLLC和PiLR Health 将提供定制的干预工具和数据采集软件。格斯坦博士的实验室将提供数据 存储、处理和分析。