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: 10241985
• 负责人: Inbal Billie Nahum-Shani
• 金额: $ 61.41万
• 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 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10241985
• 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; 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.

翻译后摘要：戒烟降低发病率和死亡率，是癌症预防的基石。 影响当前和未来脆弱性的能力（例如，高风险的失误）通过参与实时 自我监管活动（例如，行为替代，注意力）被认为是一个重要的途径 放弃成功然而，参与度差是最大限度地发挥自我影响的主要障碍。 监管活动。因此，加强实时，现实世界的参与，以证据为基础的自我监管， 活动有可能提高戒烟干预措施的有效性。Just-In-Time 通过移动的设备提供的适应性干预（JITAI）已被开发用于预防和治疗 上瘾JITAI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 基于真实的时间、真实的世界背景的干预策略。JITAI的组织框架强调 最大限度地减少对个人日常生活和惯例的干扰，通过量身定制的战略，不仅 脆弱性，但也容易接受（即，一个人的能力和意愿，利用特定的 干预）。虽然脆弱性和接受性都被认为是动态的潜在状态， 根据情绪、环境和其他因素的星座和时间动态不断变化， 没有试图系统地调查这些国家的性质，以及如何了解这些国家的情况。 这些状态可用于优化自律活动的实时参与。为了缩小这一差距， 拟议的项目将采用创新的计算方法，以最广泛的， 收集关于健康行为变化（吸烟）的真实的时间、真实的世界数据的种族/民族多样性 停止）。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探头数据， 约1,500名试图戒烟的吸烟者将使用先进的概率潜变量模型进行分析， 机器学习研究情绪的时间动态和相互作用，自我调节 容量（SRC）、上下文和其他因素可用于检测（目标1）失效的脆弱性状态， (Aim（2）参与自我调节活动的接受状态。我们还将研究（目标3）如何 这些状态的知识可以用于优化自我调节活动的实时参与， 进行了一项微型随机试验（MRT），招募了150名试图戒烟的吸烟者。利用移动的 戒烟应用程序，MRT将每天多次随机分配每个人（a）不 干预提示;（B）建议参与简短（低努力）策略的提示;或（C）提示 建议采取更有效的自我调节策略。这项研究将是第一个 为开发有效的JITAI提供全面的概念、技术和经验基础 基于脆弱性和接受性的动态模型。