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

摘要：戒烟可降低发病率和死亡率，是预防癌症的基石。 能够通过参与，实时影响当前和未来的漏洞(例如，失误的高风险) 自我调节活动(例如，行为替代、注意力集中)被认为是一条重要的途径 为放弃成功干杯。然而，较差的参与度是最大化自我影响的主要障碍 监管活动。因此，加强以证据为基础的自律的实时、真实的参与 戒烟活动有可能提高戒烟干预的有效性。准时制 通过移动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，并经过优化以提供适当的 基于实时、真实世界背景的干预策略。JITAI的组织框架强调 将对个人日常生活和例行公事的干扰降至最低，不仅是为了 脆弱性，但也对接受性(即个人利用特定事物的能力和意愿) 干预)。尽管脆弱性和接受性都被认为是动态的和潜在的状态 基于情绪、背景和其他因素的星座和时间动态而不断变化， 没有人试图系统地调查这些状态的性质，以及如何了解 这些状态可用于优化自律活动的实时参与度。为了缩小这一差距， 拟议的项目将把创新的计算方法应用于最广泛和 关于健康行为变化(吸烟)的实时、真实世界数据的种族/民族多样性收集 停止)。密集的纵向自我报告和传感器数据，来自5项研究(3项已完成，2项正在进行) 约1,500名试图戒烟的吸烟者将被用先进的概率潜变量模型和 机器学习研究情绪的时间动力学和相互作用，自我调节 容量(SRC)、上下文和其他因素可用于检测(目标1)易受失误和 (目标2)对从事自律活动的接受程度。我们还将调查(目标3)如何 这些状态的知识可用于通过以下方式优化自律活动的实时参与 进行一项微随机试验(MRT)，招募150名试图戒烟的吸烟者。利用移动电话 戒烟应用程序，捷运将每天多次随机对每个人进行(A)否 干预提示；(B)建议参与简短(低努力)战略的提示；或(C)提示 建议更努力地实践自我调节策略。这项拟议的研究将是第一个 提供开发有效的JITAI所需的全面的概念、技术和经验基础 基于脆弱性和接受性的动态模型。

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.