Phase 1 clinical trial to develop a personalized adaptive text message intervention using control systems engineering tools to increase physical activity in early adulthood

项目摘要 缺乏运动是导致体重增加的一系列生活方式因素的一部分--包括吸烟和饮食 在成年初期变得更好。危害心血管健康的危险因素在 过渡到成年期。在此期间防止体力活动(PA)减少的干预措施可以 降低长期慢性病风险。短信干预显示出了一致的积极效果 PA但通过量身定制、定向或个性化来增加干预效果的努力尚未实现 他们的潜力。出现了新的方法，用于根据治疗反应或 背景因素(例如，阶梯式护理、适时适应性干预)，但它们适用单一决策规则 为所有参与者统一提供服务。行为是复杂的和多重决定的，所以治疗是可能的 回应是特殊的，需要个性化的决策规则。建立在对精度的兴趣之上 医学，我们提出了一种方法来开发个性化的自适应消息干预使用密集 纵向数据(来自可穿戴传感器和瞬时天气指数)和来自控制系统的工具 工程学(系统辨识和鲁棒控制综合)。在前期工作中，我们开发了一种 个体短信体力活动回复的计算模型。最大的障碍是 在干预中实施这一方法是预测性建模所需的计算模型 具有高度不确定性，且过于复杂，无法在智能手机上高效运行 其他可穿戴设备。我们建议通过(1)建立物理的动力学模型来解决这个问题 基于对消息的历史响应、最近行为、特定位置的天气和时间的活动 特征，以及(2)评估更多和更少侵略性适应的可接受性和可行性 干预控制器个性化的策略。为了实现这些目标，我们将招募年轻人来 参与PA消息传递干预，并开发不同响应的计算模型 不同条件下的消息。将开发基于模型的控制器以(A)优化消息 时间、频率和内容选择，以及(B)在变化的情况下实现指定的行为改变目标 条件。然后，我们将使用独立的年轻人样本来部署该控制器，以确定如何 在接下来的六个月里，更积极的适应策略与不那么激进的适应策略会影响用户体验。本研究 将提供基于模型的干预控制器和可接受的适应策略，以用于 用于增加PA的个性化自适应消息传递干预。如果成功，它将同时增加PA和用户 通过选择和选择消息的时间来参与，以最大限度地提高效果并最大限度地减少负担。这种方法可以 用于为其他与预防体重增加相关的行为开发个性化干预措施， 维护心血管健康，降低慢性病风险。

Project Summary Physical inactivity is part of a constellation of lifestyle factors – with smoking and diet – that contribute to weight gain in early adulthood. Risk factors that compromise cardiovascular health begin to accumulate during the transition into adulthood. Interventions that prevent decreases in physical activity (PA) during this period can reduce long-term chronic disease risk. Text message interventions have shown a consistent positive effect on PA but efforts to increase those intervention effects via tailoring, targeting or personalizing have not realized their potential. New approaches have emerged for tailoring interventions based on treatment responses or contextual factors (e.g., stepped care, just-in-time adaptive interventions) but they apply a single decision rule uniformly for all participants. Behavior is complex and multiply determined so it is possible that treatment responses are idiosyncratic, necessitating personalized decision rules. Building on interest in precision medicine, we propose a method to develop personalized adaptive messaging interventions using intensive longitudinal data (from wearable sensors and momentary weather indices) and tools from control systems engineering (system identification and robust control synthesis). In preliminary work, we developed a computational model of physical activity responses to individual text messages. The greatest barrier to implementing that approach in interventions is that the computational models required for predictive modeling of PA dynamics have a high degree of uncertainty and are too complex to run efficiently on smartphones and other wearable devices. We propose to solve that problem by (1) developing a dynamical model of physical activity based on historical responses to messages, recent behavior, location-specific weather, and temporal features, and (2) evaluating the acceptability and feasibility of more versus less aggressive adaptation strategies for personalizing an intervention controller. To accomplish these aims, we will recruit young adults to participate in a PA messaging intervention and develop a computational model of responses to different messages under different conditions. A model-based controller will be developed to (a) optimize message timing, frequency, and content selection, and (b) achieve specified behavior change goals under varying conditions. We will then deploy that controller with an independent sample of young adults to determine how more versus less aggressive adaptation strategies over the next six months impact user experience. This study will contribute a model-based intervention controller and an acceptable adaptation strategy to use in a personalized adaptive messaging intervention for increasing PA. If successful, it will increase both PA and user engagement by selecting and timing messages to maximize effects and minimize burden. This approach can be applied to develop personalized interventions for other behaviors relevant for preventing weight gain, preserving cardiovascular health, and reducing chronic disease risk.