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: 10005898
• 负责人: Predrag Klasnja
• 金额: $ 49.39万
• 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/10005898
• 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 Models; 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; multiscale data; novel; physical inactivity; randomized trial; research and development; response; sedentary; sedentary lifestyle; social cognitive theory; sound; theories; therapy design; tool; wearable sensor technology

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. !

不健康的行为导致了大多数慢性病，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 是否有效、可扩展和可持续仍然具有挑战性。移动传感技术的最新进展 基于智能手机的技术导致了一种新颖而有前途的干预形式，称为“即时干预”， 适应性干预“(JITAI)，它有可能不断适应不断变化的环境和 根据个人需求和改变行为的机会进行个性化。尽管干预措施一直是 基于健全理论的行为理论被证明是更有效的，目前的行为理论缺乏时间性 基于测量的有效JITAI开发所需的粒度和多尺度动态结构 复杂的动态行为和背景。同时，还缺乏能够 表达动态的、时间上的多尺度理论，并表示动态的、时间上的多尺度数据。这 项目将解决理论开发、测量和建模方面的挑战和机遇 由密集收集的纵向数据呈现，重点是身体活动和久坐行为， 以及对其他行为的广泛影响。为了提高效率，我们建立在美国国立卫生研究院资助的长达一年的微型 基于社会认知的适应性移动健康干预--心跳步长随机试验(MRT)(n=60) 心血管疾病患者增加步行和减少久坐行为的理论(SCT) 疾病。这一新建议的目的是：1)完善和发展理论结构的动态测量 影响我们的目标行为，2)通过目标1和收集中制定的措施增强心跳 来自另外两个长达一年的HeartSteps队列的数据(久坐超重/肥胖成年人(n=60)和类型2 糖尿病患者(n=60，总计n=180)，3)开发建模框架以操作动态和 干预背景下的行为理论，以及4)改善SCT结果的预测 使用越来越复杂的模型。这里提出的工作将提供新的数字、数据驱动的措施 关键行为理论在瞬时、每日和每周时间尺度上构建，提供量身定制的新工具 用于行为动力学复杂模型的规范，以及量身定做的新模型估计工具 特别是现在复杂的、纵向的、多时间尺度的行为和背景数据 使用移动健康技术访问。最后，我们将利用收集的数据和建议的建模 开发和测试增强的、动态的社会认知理论扩展的工具 量化的、预测性的动态模型。总体而言，这项工作将提供理论基础和 显著提高以身体活动为基础的移动卫生干预措施的有效性所需的工具 在多个时间范围内，包括它们在更长时间范围内有效支持行为变化的能力。 好了！