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: 10449662
• 负责人: Garrett Igo Ash
• 金额: $ 14.89万
• 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/10449662
• 关键词: 3-Dimensional; 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; Custom; Data; Data Storage and Retrieval; Development; Diet; Dimensions; Early Intervention; Educational workshop; 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; Logistics; Machine Learning; Mathematics; Measures; Medical; Mentors; 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; base; biobehavior; blood glucose regulation; data repository; diabetes management; diabetes self-management; diaries; digital; evidence base; exercise intervention; exercise prescription; feasibility trial; glucose monitor; glycemic control; hazard; health goals; heuristics; improved; innovation; insight; insulin sensitivity; mHealth; 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.

抽象的 1型糖尿病（T1D）影响约100万美国成年人，并增加了可归因于死亡率的风险 心血管疾病增加了800％。当前基于证据的T1D自我管理干预措施目标血糖 控制但忽略了T1D中普遍存在的其他可修改的健康问题，例如高血压和肥胖症。锻炼 如果干预措施可以创新地解决糖尿病管理和 T1D提出的锻炼的社会心理挑战。连续葡萄糖监测（CGM）允许患者 提供者可以全面跟踪锻炼的短期和长期结果。循证 缺乏将CGM技术转化为可持续遵守与运动相关行为的干预措施。 我们提供的人提供的飞行员干预为先前久坐的成年人提供了T1D访问练习视频 以及每月以客户为中心的CGM讨论，并通过运动教练进行锻炼数据。参与者 这些提高了运动管理的行为技巧和动力，但仅是暂时的。他们说一个 需要更频繁，持续的联系，需要该建议将开发自动移动工具。 这些工具包括及时的自适应短信，以克服脆弱性的锻炼障碍， 每周对短期运动安全危害的个性化评论，并避免了技巧，每月一次 通过贝叶斯建模对运动对血糖水平的长期影响的个性化评估。这 计划代表NIH干预开发模型的第1阶段：干预，改进， 修改，适应。这些步骤将通过可行性研究来评估用户满意度和 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， 然后在非随机跨界临床试验中测试Beta版本。最后，数据库 该试验产生的生物行为指标（锻炼，CGM，情绪和睡眠日记约7,000人日） 将降低维度，以识别基线和早期干预的生物行为亚型 数据。我们将测试这些子类型是否有助于预测长期干预响应和/或特定的标志 生物行为特征组合，可推动干预响应。这些发现将奠定基础 对于Ash博士的未来工作，开发了精密医学方法。以及这项研究Ash博士威尔 在1）糖尿病管理和技术的领域完成培训； 2）移动健康（MHealth） 干预发展； 3）减少维度分析。培训计划包括策略 由心理主导的培训，课程工作，赠款写作和参加相关会议的出席的结合 讲习班。 Ash博士已经组建了一支在T1D自我管理和技术的心理团队，多重健康 行为改变干预开发，MHealth的发展和信息。 Fitscriptllc和Pilr Health 将提供自定义的干预工具和数据捕获软件。格斯坦博士的实验室将支持数据 存储，处理和分析。