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

第一阶段临床试验，利用控制系统工程工具开发个性化自适应短信干预，以增加成年早期的体力活动

• 批准号: 9922375
• 负责人: DAVID E. CONROY
• 金额: $ 59.21万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9922375
• 关键词: Accelerometer; Adult; Algorithms; Behavior; Behavior Therapy; Behavioral; Behavioral Model; Cardiovascular Diseases; Caring; Cellular Phone; Chronic; Chronic Disease; Complex; Computer Models; Data; Development; Diabetes Mellitus; Diet; Engineering; Ensure; Frequencies; Future; Goals; Health; Healthcare Systems; Individual; Intervention; Location; Malignant Neoplasms; Methods; Modeling; Participant; Performance; Periodicity; Phase I Clinical Trials; Physical activity; Population; Posture; Preventive; Problem Solving; Randomized; Risk Factors; Running; Sampling; Smoking; Specific qualifier value; System; Text Messaging; Time; Uncertainty; Underinsured; Update; Weather; Weight Gain; Work; adaptive intervention; base; behavior change; behavioral response; cardiovascular disorder risk; cardiovascular health; contextual factors; cost; design; disorder risk; emerging adult; exercise intervention; experience; falls; indexing; interest; intervention effect; lifestyle factors; mathematical model; novel strategies; personalized decision; personalized intervention; personalized medicine; personalized strategies; phase II trial; physical inactivity; physical model; precision medicine; predictive modeling; preservation; prevent; public health relevance; recruit; response; service intervention; success; tool; treatment effect; treatment response; wearable device; wearable sensor technology; young adult

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.

项目摘要 缺乏身体活动是导致体重增加的一系列生活方式因素的一部分--吸烟和饮食 在成年早期获得。危害心血管健康的风险因素开始积累， 过渡到成年。在此期间，预防体力活动（PA）减少的干预措施可以 降低长期慢性病风险。短信干预显示出一致的积极影响， 但通过量身定制、针对性或个性化来提高这些干预效果的努力尚未实现 他们的潜力已经出现了新的方法，根据治疗反应或 背景因素（例如，阶梯式护理，及时适应性干预），但他们采用单一的决策规则 对所有参与者都一样。行为是复杂的，多方面决定的，因此治疗可能 反应是特殊的，需要个性化的决策规则。基于对精确性的兴趣 医学，我们提出了一种方法来开发个性化的自适应信息干预，使用密集的 纵向数据（来自可穿戴传感器和瞬时天气指数）和控制系统的工具 工程（系统识别和鲁棒控制合成）。在初步工作中，我们开发了一个 计算模型的物理活动响应个人短信。的最大障碍 在干预措施中实施这种方法的关键是预测建模所需的计算模型， 的PA动态具有高度的不确定性，并且过于复杂，无法在智能手机上有效运行， 其他可穿戴设备。我们建议解决这个问题，通过（1）发展一个动力学模型的物理 基于对消息的历史响应、最近的行为、特定位置的天气和时间的活动 特征，以及（2）评估更多与更少积极适应的可接受性和可行性 个性化干预控制器的策略。为了实现这些目标，我们将招募年轻人， 参与PA消息传递干预，并开发不同响应的计算模型 不同条件下的信息。将开发基于模型的控制器，以（a）优化消息 定时、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，我们将部署控制器与一个独立的样本的年轻人，以确定如何 在接下来的六个月里，更积极的适应策略与更不积极的适应策略会影响用户体验。本研究 将有助于基于模型的干预控制器和可接受的适应策略， 个性化的自适应消息传递干预，以增加PA。如果成功，它将增加PA和用户 通过选择和定时信息来最大限度地提高效果和减少负担。这种方法可以 应用于开发与预防体重增加相关的其他行为的个性化干预， 保持心血管健康，降低慢性病风险。