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: 10244991
• 负责人: Arie Kapteyn
• 金额: $ 50.45万
• 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 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10244991
• 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; 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; Unhealthy Diet; 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; efficacy outcomes; 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. !

不健康的行为导致大多数慢性病，占所有医疗保健的 86% 尽管进行了大量研究，但行为改变干预措施的发展 移动传感和应用领域的最新进展仍然具有挑战性。 基于智能手机的技术催生了一种新颖且有前途的干预形式，称为“即时、 适应性干预”（JITAI），它有潜力不断适应不断变化的环境和 尽管已经采取了干预措施，但仍应根据个人需求和行为改变机会进行个性化调整。 当基于合理的理论时，当前的行为理论被证明更有效，但缺乏时间性 基于测量开发有效的 JITAI 所需的粒度和多尺度动态结构 同时，缺乏可以进行复杂动态行为和上下文的建模框架。 表达动态、时间多尺度理论并表示动态、时间多尺度数据。 项目将解决理论发展、测量和建模的挑战和机遇 由集中收集的纵向数据呈现，重点关注身体活动和久坐行为， 为了提高效率，我们以 NIH 资助的为期一年的微观研究为基础。 HeartSteps 的随机试验 (MRT) (n=60)，一种基于社会认知的适应性移动健康干预措施 理论 (SCT) 的发展旨在增加心血管疾病患者的步行次数并减少久坐行为 这项新提案的目标是：1）完善和开发理论构建的动态措施。 影响我们的目标行为，2) 通过目标 1 中制定的措施增强 HeartSteps 并收集 来自另外两个为期一年的 HeartSteps 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），总数 n = 180），3）开发一个建模框架来操作动态和 干预环境下的行为情境理论，以及 4) 改进 SCT 结果的预测 这里提出的工作将提供新的数字化、数据驱动的测量方法。 在瞬间、每日和每周时间尺度构建关键行为理论，提供量身定制的新工具 用于规范行为动力学的复杂模型，以及定制的新模型估计工具 特别是现在复杂的、纵向的、多时间尺度的行为和情境数据 最后，我们将利用收集的数据和建议的模型。 开发和测试社会认知理论的增强的、动态的扩展的工具，充分运作 总的来说，这项工作将提供理论基础和预测动力模型。 显着提高基于身体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，包括它们在较长时间尺度上有效支持行为改变的能力。 ！