Novel use of mHealth data to identify states of vulnerability and receptivity to JITAIs

新颖地使用移动医疗数据来识别 JITAI 的脆弱性和接受度状态

• 批准号: 9768419
• 负责人: Inbal Billie Nahum-Shani
• 金额: $ 60.65万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9768419
• 关键词: Aphorisms; Attention; Behavior Therapy; Behavioral; Boredom; Cause of Death; Cigarette; Collection; Complex; Data; Depressed mood; Development; Effectiveness; Emotions; Enrollment; Formulation; Foundations; Future; Health behavior change; Home environment; Individual; Informal Social Control; Intervention; Knowledge; Learning Skill; Location; Machine Learning; Malignant Neoplasms; Measures; Modeling; Morbidity - disease rate; Nature; Pathway interactions; Patient Self-Report; Play; Randomized; Recommendation; Research; Risk; Role; Self Efficacy; Smoker; Smoking; Smoking Cessation Intervention; Societies; Time; Tobacco; Tobacco use; adaptive intervention; addiction; base; behavior change; cancer prevention; coping; craving; disability; ethnic diversity; evidence base; field theory; handheld mobile device; high risk; improved; innovation; mHealth; mindfulness; mortality; novel; positive emotional state; prevent; racial and ethnic; randomized trial; sensor; skills; smoking cessation; success; theories; willingness

Abstract: Smoking cessation decreases morbidity and mortality and is a cornerstone of cancer prevention. The ability to impact current and future vulnerability (e.g., high risk for a lapse) in real-time via engagement in self-regulatory activities (e.g., behavioral substitution, mindful attention) is considered an important pathway to quitting success. However, poor engagement represents a major barrier to maximizing the impact of self- regulatory activities. Hence, enhancing real-time, real-world engagement in evidence-based self-regulatory activities has the potential to improve the effectiveness of smoking cessation interventions. Just-In-Time Adaptive Interventions (JITAIs) delivered via mobile devices have been developed for preventing and treating addictions. JITAIs adapt over time to an individual’s changing status and are optimized to provide appropriate intervention strategies based on real time, real world context. Organizing frameworks on JITAIs emphasize minimizing disruptions to the daily lives and routines of the individual, by tailoring strategies not only to vulnerability, but also to receptivity (i.e., an individual’s ability and willingness to utilize a particular intervention). Although both vulnerability and receptivity are considered latent states that are dynamically and constantly changing based on the constellation and temporal dynamics of emotions, context, and other factors, no attempt has been made to systematically investigate the nature of these states, as well as how knowledge of these states can be used to optimize real-time engagement in self-regulatory activities. To close this gap, the proposed project will apply innovative computational approaches to one of the most extensive and racially/ethnically diverse collection of real time, real world data on health behavior change (smoking cessation). Intensive longitudinal self-reported and sensor data from 5 studies (3 completed and 2 ongoing) of ~1,500 smokers attempting to quit will be analyzed with advanced probabilistic latent variable models and machine learning to investigate how the temporal dynamics and interactions of emotions, self-regulatory capacity (SRC), context, and other factors can be used to detect (Aim 1) states of vulnerability to a lapse and (Aim 2) states of receptivity to engaging in self-regulatory activities. We will also investigate (Aim 3) how knowledge of these states can be used to optimize real-time engagement in self-regulatory activities by conducting a Micro-Randomized Trial (MRT) enrolling 150 smokers attempting to quit. Utilizing a mobile smoking cessation app, the MRT will randomize each individual multiple times per day to either (a) no intervention prompt; (b) a prompt recommending engagement in brief (low effort) strategies; or (c) a prompt recommending a more effortful practice of self-regulation strategies. The proposed research will be the first to yield a comprehensive conceptual, technical, and empirical foundation necessary to develop effective JITAIs based on dynamic models of vulnerability and receptivity.

摘要：戒烟可降低发病率和死亡率，是预防癌症的基石。 通过参与实时影响当前和未来的漏洞（例如，失误的高风险）的能力 自我调节活动（例如行为替代、正念注意力）被认为是重要途径 然而，参与度低是最大限度地发挥自我影响力的主要障碍。 因此，加强实时、现实世界的基于证据的自我监管活动。 活动有可能提高及时戒烟干预措施的有效性。 通过移动设备提供的适应性干预措施 (JITAI) 已被开发用于预防和治疗 JITAI 会随着时间的推移适应个人不断变化的状态，并进行优化以提供适当的服务。 基于实时、真实世界背景的干预策略强调 JITAI 的组织框架。 通过制定策略，最大限度地减少对个人日常生活和例行公事的干扰 脆弱性，而且还包括接受性（即个人利用特定的能力和意愿） 尽管脆弱性和接受性都被认为是动态的、潜在的状态。 根据情绪、背景和其他因素的星座和时间动态不断变化， 尚未尝试系统地调查这些状态的性质，以及这些状态的知识如何 这些状态可用于优化自我监管活动的实时参与。 拟议的项目将把创新的计算方法应用于最广泛和最广泛的领域之一 种族/族裔多样化的实时、真实世界健康行为变化数据收集（吸烟 来自 5 项研究（3 项已完成，2 项正在进行）的密集纵向自我报告和传感器数据。 将使用先进的概率潜变量模型对约 1,500 名试图戒烟的吸烟者进行分析 机器学习研究情绪的时间动态和相互作用、自我调节 能力（SRC）、背景和其他因素可用于检测（目标 1）易受故障影响的状态，并 （目标 2）参与自律活动的接受程度 我们还将调查（目标 3）如何参与。 对这些状态的了解可用于优化实时参与自律活动 开展一项微型随机试验 (MRT)，招募了 150 名试图使用手机戒烟的吸烟者。 戒烟应用程序中，MRT 每天会将每个人随机多次分为 (a) 否 干预提示；(b) 提示参与简短（省力）策略；或 (c) 提示； 建议采取更有效的自我监管策略实践。拟议的研究将是第一个。 为开发有效的 JITAI 提供必要的全面概念、技术和经验基础 基于脆弱性和接受性的动态模型。