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. !

不健康的行为导致了大多数慢性病，占所有医疗保健的86%