Operationalizing Behavioral Theory for mHealth: Dynamics, Context, and Personalization

移动医疗行为理论的实施：动态、情境和个性化

• 批准号: 9788305
• 负责人: Predrag Klasnja
• 金额: $ 51.87万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-19 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9788305
• 关键词: Address; Adult; Affect; Alcohols; Algorithms; Area; Bayesian Modeling; Bayesian Network; Behavior; Behavior Therapy; Behavioral; Behavioral Model; Behavioral Sciences; Biological Models; Calendar; Cardiovascular Diseases; Cardiovascular system; Cellular Phone; Cessation of life; Chronic; Chronic Disease; Cognition; Complex; Computer Simulation; Cues; Data; Development; Diabetes Mellitus; Diet; Effectiveness; Environment; Expectancy; Foundations; Funding; Goals; Health Technology; Health behavior; Health behavior change; Healthcare; Human; Individual; Intervention; Location; Malignant Neoplasms; Measurement; Measures; Modeling; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Outcome; Overweight; Patient Self-Report; Patients; Physical activity; Population; Process; Public Health; Randomized; Research; Sample Size; Self Efficacy; Series; Shapes; Source; Statistical Models; Stress; Structure; Technology; Testing; Time; Tobacco use; Uncertainty; United States National Institutes of Health; Walking; Weather; Work; adaptive intervention; adult obesity; base; behavior change; causal model; cohort; contextual factors; design; digital; dynamic system; effective intervention; falls; flexibility; improved; mHealth; mobile computing; novel; physical inactivity; randomized trial; research and development; response; sedentary; sedentary lifestyle; social cognitive theory; sound; theories; therapy design; tool; wearable device

Unhealthy behaviors contribute to the majority of chronic diseases, which account for 86% of all healthcare spending in the US. Despite a great deal of research, the development of behavior change interventions that are effective, scalable, and sustainable remains challenging. Recent advances in mobile sensing and smartphone-based technologies have led to a novel and promising form of intervention, called a “Just-in-time, adaptive intervention” (JITAI), which has the potential to continuously adapt to changing contexts and personalize to individual needs and opportunities for behavior change. Although interventions have been shown to be more effective when based on sound theory, current behavioral theories lack the temporal granularity and multiscale dynamic structure needed for developing effective JITAIs based on measurements of complex dynamic behaviors and contexts. Simultaneously, there is a lack of modeling frameworks that can express dynamic, temporally multiscale theories and represent dynamic, temporally multiscale data. This project will address the theory-development, measurement, and modeling challenges and opportunities presented by intensively collected longitudinal data, with a focus on physical activity and sedentary behavior, and broad implications for other behaviors. For efficiency, we build on the NIH-funded year-long micro- randomized trial (MRT) of HeartSteps (n=60), an adaptive mHealth intervention based on Social-Cognitive Theory (SCT) developed to increase walking and decrease sedentary behavior in patients with cardiovascular disease. The aims of this new proposal are: 1) Refine and develop dynamic measures of theoretical constructs that influence our target behaviors, 2) Enhance HeartSteps with the measures developed in Aim 1 and collect data from two additional year-long HeartSteps cohorts (sedentary overweight/obese adults (n=60) and type 2 diabetes patients (n=60), total n=180), 3) Develop a modeling framework to operationalize dynamic and contextualized theories of behavior in an intervention setting, and 4) Improve prediction of SCT outcomes using increasingly complex models. The work proposed here will provide new digital, data driven measures of key behavioral theory constructs at the momentary, daily, and weekly time scales, provide new tools tailored for the specification of complex models of behavioral dynamics, as well as new model estimation tools tailored specifically to the complex, longitudinal, multi-time scale behavioral and contextual data that are now accessible using mHealth technologies. Finally, we will leverage the collected data and the proposed modeling tools to develop and test enhanced, dynamic extensions of social cognitive theory operationalized as fully quantified, predictive dynamical models. Collectively, this work will provide the theoretical foundations and tools needed to significantly increase the effectiveness of physical activity-based mobile health interventions over multiple time scales, including their ability to effectively support behavior change over longer time scales. !

不健康的行为导致大多数慢性疾病，占所有医疗保健的86％ 在美国的支出。尽管进行了大量研究，但行为改变干预措施的发展 有效，可扩展和可持续性仍然受到挑战。移动传感器和 基于智能手机的技术导致了一种新颖而有希望的干预形式，称为“及时， 自适应干预”（Jitai），它有可能继续适应不断变化的环境和 个性化个人需求和行为改变的机会。尽管干预措施已经 根据声音理论，当前的行为理论缺乏暂时的，显示出更有效的 基于测量的粒度和多尺度动态结构需要开发有效的Jitais 复杂的动态行为和上下文。同时，缺乏建模框架可以 表达动态，暂时多构理论，并表示动态，暂时多尺度数据。这 项目将解决理论的发展，测量和建模挑战和机遇 由深入收集的纵向数据提出，重点是体育活动和久坐行为， 对其他行为的广泛影响。为了效率，我们以NIH资助的长达一年的微型 - 心脏的随机试验（MRT）（n = 60），这是一种基于社会认知的自适应MHealth干预措施 理论（SCT）为增加步行和减少心血管患者的久坐行为而发展 疾病。该新建议的目的是：1）完善并开发了理论构造的动态测量 这会影响我们的目标行为，2）通过在AIM 1中制定的措施增强心脏并收集 来自另外两个全年心变队列的数据（久坐的超重/肥胖成年人（n = 60）和2型 糖尿病患者（n = 60），总n = 180），3）开发一个建模框架，以操作动态和 在干预设置中的情境化行为理论，以及4）改善SCT结果的预测 使用日益复杂的模型。这里提出的工作将提供新的数字，数据驱动的措施 关键行为理论在瞬间，每日和每周的时间尺度上构建，提供了量身定制的新工具 为了规范复杂的行为动力学模型以及量身定制的新模型估计工具 专门针对复杂的，纵向的，多时间的规模行为和上下文数据 使用MHealth技术可访问。最后，我们将利用收集的数据和提议的建模 开发和测试增强的，动态的社会认知理论的动态扩展 量化的预测动态模型。总的来说，这项工作将提供理论基础，并 需要显着提高基于体育活动的移动健康干预措施的有效性所需的工具 在多个时间尺度上，包括它们有效地支持行为在较长时间尺度上改变行为的能力。 呢