Efficacy of Precision Text Messaging to Increase Physical Activity in Insufficiently-Active Young Adults

项目摘要 身体活动和体重管理是理想心血管健康的关键组成部分，但许多年轻人 成年人活动不足，体重迅速增加（平均每年0.5-1.0公斤）。体力活动是一种 一个行之有效的战略衰减体重增加，所以有迫切需要有效的干预措施，减少 通过激励人们多运动，少坐，减少体重增加，降低心血管风险。移动的和可穿戴的 技术在年轻人中几乎无处不在，并提供了访问有关 人与环境。这项技术还提供了一种手段，提供干预措施， 因此，每个人都可以在正确的时间接收正确的干预内容。灵感来自于 精确的行为干预，我们已经应用了控制系统的系统识别工具， 工程开发计算模型和算法，优化个人特定的文本剂量 促进身体活动的信息。在一系列初步研究中，我们（a）确定了 与体力活动一致相关，（B）证明了长期佩戴活动跟踪器的可行性， 年轻人对短信的接受程度，（c）丰富了我们的物理计算模型 使用瞬时天气数据来表征对消息的天气分级响应的活动，以及（d） 设计了一个控制器，该控制器使用来自计算模型的个人特定参数和最近的数据， 行为和预报的天气条件来优化文本消息传递的选择和定时。这 控制器是精确自适应干预消息传递（Precision AIM）干预的基础。在这 应用程序，我们提出了一个单一的临床试验，以回答问题，“精度AIM是否增加 不积极的年轻人的身体活动和减少体重增加比随机分配 来自同一消息库或活动跟踪器的干预消息（随机AIM）， 干预消息（无AIM）？”本项目的具体目标是：（1）评估精密度的有效性 AIM与随机AIM和无AIM相比，增加体力活动和减少体重增加， 不积极活跃的年轻人，（2）确定参与者的特征， 精确瞄准比随机瞄准或无瞄准。我们提出了一个三臂随机对照试验，以实现 这些目标。不活跃的年轻人将收到一个消费者活动跟踪器，并随机 分配到三个组之一：精确AIM、随机AIM或无AIM。干预期将持续 12个月，18个月时进行维持评估。主要结果是平均每日步数 基于越来越多的证据表明，增加这种易于解释和广泛可用的指标， 成年期心血管风险（在基线、3、6、12和18个月时评估）。如果成功，底层 由可穿戴设备的密集数据提供信息的算法分析将提供一种新的可扩展方法 使用消费者可穿戴设备对年轻人的心血管风险进行原始和初级预防。

PROJECT SUMMARY Physical activity and weight management are key components of ideal cardiovascular health but many young adults are insufficiently active and gaining weight rapidly (0.5-1.0 kg/year on average). Physical activity is a proven strategy for attenuating weight gain so there is a pressing need for effective interventions that reduce cardiovascular risk by motivating people to move more, sit less, and reduce weight gain. Mobile and wearable technologies are nearly ubiquitous among young adults and provide access to dense digital data about the person and their environment. This technology also provides a means of delivering interventions on a just-in- time basis so each person can receive the right intervention content at the right time. Inspired by the vision of precision behavioral interventions, we have applied system identification tools from control systems engineering to develop computational models and algorithms that optimize person-specific dosing of text messages to promote physical activity. In a series of preliminary studies, we (a) identified weather indices consistently linked with physical activity, (b) demonstrated the feasibility of long-term activity tracker wear and the acceptability of text messages among young adults, (c) enriched our computational model of physical activity using momentary weather data to characterize weather-graded responses to messages, and (d) designed a controller that uses person-specific parameters from the computational model and data on recent behavior and forecasted weather conditions to optimize the selection and timing of text message delivery. This controller is the basis for the Precision Adaptive Intervention Messaging (Precision AIM) intervention. In this application, we propose a single-site clinical trial to answer question, “Does Precision AIM increase insufficiently-active young adults’ physical activity and reduce weight gain more than randomly-assigned intervention messages (Random AIM) from the same message library or an activity tracker with no assigned intervention messages (No AIM)?” The specific aims of this project are (1) to evaluate the efficacy of Precision AIM compared to Random AIM and No AIM for increasing physical activity and reducing weight gain in insufficiently-active young adults, and (2) to identify characteristics of participants who respond more to Precision AIM than Random AIM or No AIM. We propose a three-arm randomized controlled trial to achieve these aims. Insufficiently-active young adults will receive a consumer activity tracker and be randomly assigned to one of three groups: Precision AIM, Random AIM, or No AIM. The intervention period will last for 12 months with a maintenance assessment at 18 months. The primary outcome is average daily step counts based on accumulating evidence that increasing this readily-interpretable and widely-available metric reduces cardiovascular risk in adulthood (assessed at baseline, 3, 6, 12, and 18 months). If successful, the underlying algorithmic analyses informed by dense data from wearable devices will provide a new and scalable approach for using consumer wearables in primordial and primary prevention of cardiovascular risk in young adults.