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

不健康的行为导致了大多数慢性病，占所有医疗保健的86% 在美国消费。尽管有大量的研究，行为改变干预的发展， 有效、可扩展和可持续的方法仍然具有挑战性。在移动的传感和 基于智能手机的技术已经导致了一种新颖的和有前途的干预形式，称为“及时， 适应性干预”（JITAI），它有可能不断适应不断变化的环境， 个性化的个人需求和行为改变的机会。虽然干预措施已 被证明是更有效的时候，基于健全的理论，目前的行为理论缺乏时间 开发基于测量的有效JITAI所需的粒度和多尺度动态结构 复杂的动态行为和环境。同时，缺乏建模框架， 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 项目将解决理论发展，测量和建模的挑战和机遇 通过集中收集的纵向数据呈现，重点关注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NIH资助的为期一年的微- HeartSteps（n=60）的随机试验（MRT），这是一种基于社会认知的自适应移动健康干预措施 理论（SCT）的发展，以增加步行和减少久坐不动的行为，心血管疾病患者 疾病这一新建议的目的是：1）完善和发展理论结构的动态措施 2）用目标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（久坐超重/肥胖成人（n=60）和2型糖尿病）的数据 糖尿病患者（n=60），总共n=180），3）开发模型框架以操作动态和 干预环境中的情境化行为理论，以及4）改善对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， 关键行为理论在瞬间，每日和每周的时间尺度上构建，提供了定制的新工具 用于行为动力学的复杂模型的规范，以及定制的新模型估计工具 特别是复杂的，纵向的，多时间尺度的行为和背景数据， 使用移动医疗技术。最后，我们将利用收集的数据和建议的建模 工具，以开发和测试增强，社会认知理论的动态扩展操作作为充分 量化的预测性动力学模型。总的来说，这项工作将提供理论基础， 显著提高基于身体活动的移动的健康干预措施的有效性所需的工具 在多个时间尺度上，包括他们在更长的时间尺度上有效支持行为改变的能力。 !

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. !