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: 10887767
• 负责人: Garrett Igo Ash
• 金额: $ 7.55万
• 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/10887767
• 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.

抽象的 1 型糖尿病 (T1D) 影响约 100 万美国成年人，并增加死亡风险 目前基于证据的 T1D 自我管理干预措施以血糖为目标。 控制但忽视 T1D 中常见的其他可改变的健康问题，例如高血压和肥胖。 如果干预措施能够创新地解决糖尿病管理问题，那么它们可以提供一种新颖的解决方案 1 型糖尿病 (T1D) 带来的运动带来的心理社会挑战使患者能够进行连续血糖监测 (CGM)。 和提供者全面跟踪基于证据的短期和长期结果。 目前缺乏将 CGM 技术转化为可持续坚持运动相关行为的干预措施。 我们的人工试点干预为以前久坐的 T1D 成年人提供了锻炼视频 每月与运动教练以客户为中心讨论 CGM 和运动数据。 他们表示，这些措施改善了运动管理行为技能和动机，但只是暂时的。 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工具。 这些工具包括及时自适应短信，以克服脆弱时的锻炼障碍， 每周对短期运动安全危险进行个性化审查，并提供避免这些危险的提示，每月 通过贝叶斯模型个性化评估运动对血糖水平的长期影响。 计划代表 NIH 干预开发模型的第一阶段：干预生成、完善、 这些步骤将通过评估用户满意度的可行性研究来完成。 alpha 版本的数学稳健性，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本最后，数据库。 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 我们将测试这些亚型是否有助于预测长期干预反应和/或标记特定的数据。 这些发现将为驱动干预反应性的生物行为特征组合奠定基础。 除了这项研究之外，阿什博士还将致力于开发精准医学方法。 完成 1) 糖尿病管理和技术领域的培训；2) 移动医疗 (mHealth) 干预开发；3) 降维分析。 结合导师指导的培训、课程作业、资助写作以及参加相关会议和 Ash 博士组建了一个 T1D 自我管理和技术、多重健康方面的指导团队。 行为改变干预开发、移动医疗开发和信息学。 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支持数据。 存储、处理和分析。

项目成果

Editorial: Motivation states and hedonic motivation for physical activity, exercise, and sport vs. sedentary behaviors.

• DOI: 10.3389/fspor.2023.1282118
• 发表时间: 2023
• 期刊: FRONTIERS IN SPORTS AND ACTIVE LIVING
• 影响因子: 2.7
• 作者: Stults-Kolehmainen, Matthew A.;Dunton, Genevieve;Boullosa, Daniel;Ash, Garrett I.;Filgueiras, Alberto
• 通讯作者: Filgueiras, Alberto

Role of the exercise professional in metabolic and bariatric surgery.

运动专业人员在代谢和减肥手术中的作用。

• DOI: 10.1101/2023.04.20.23288698
• 发表时间: 2023
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Stults-Kolehmainen,MatthewA;Bond,DaleS;Richardson,LauraA;Herring,LouisaY;Mulone,Bethany;Garber,CarolEwing;Morton,John;Ghiassi,Saber;Duffy,AndrewJ;Balk,Ethan;Abolt,CharlesJ;Howard,MattC;Ash,GarrettI;Williamson,Susannah

Health policy considerations for combining exercise prescription into noncommunicable diseases treatment: a narrative literature review.

• DOI: 10.3389/fpubh.2023.1219676
• 发表时间: 2023
• 期刊: FRONTIERS IN PUBLIC HEALTH
• 影响因子: 5.2
• 作者: Tao, Dan;Awan-Scully, Roger;Ash, Garrett I.;Gu, Yaodong;Pei, Zhong;Gao, Yang;Cole, Alistair;Supriya, Rashmi;Sun, Yan;Xu, Rui;Baker, Julien S.
• 通讯作者: Baker, Julien S.