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

项目总结 体力活动和体重管理是理想的心血管健康的关键组成部分，但许多年轻人 成年人运动不足，体重迅速增加(平均0.5-1.0公斤/年)。体力活动是一种 经过验证的减轻体重增加的策略，因此迫切需要有效的干预措施来减少 通过激励人们多运动、少坐和减少体重增加来增加心血管风险。可移动和可穿戴 技术在年轻人中几乎无处不在，并提供了访问有关 人和他们的环境。这项技术还提供了一种在Just-in-in上提供干预的方法 以时间为基础，这样每个人都可以在正确的时间接受正确的干预内容。灵感来自于 精确的行为干预，我们已经应用了来自控制系统的系统识别工具 开发计算模型和算法的工程学，以优化特定人的文本剂量 促进体力活动的信息。在一系列初步研究中，我们(A)确定了天气指数 与体力活动相一致，(B)证明了长期佩戴活动跟踪器的可行性和 短信在年轻人中的可接受性，(C)丰富了我们的计算模型 使用瞬时天气数据来表征对报文的天气分级响应的活动，以及(D) 设计了一个控制器，它使用来自计算模型和最近的数据的特定于人的参数 行为和预报天气条件，以优化短信发送的选择和时机。这 控制器是Precision自适应干预报文传送(Precision AIM)干预的基础。在这 应用，我们提出了一个单一地点的临床试验来回答这样一个问题：Precision AIM增加了吗？ 不够活跃的年轻人的体力活动和体重增加比随机分配的要多 来自相同消息库或未分配的活动跟踪器的干预消息(随机目标) 干预消息(无AIM)？“本项目的具体目标是：(1)评估Precision的疗效 目的比较随机AIM和无AIM在增加体力活动和减少体重增加方面的作用 不够活跃的年轻人，以及(2)确定对以下问题反应更多的参与者的特征 精确目标比随机目标或无目标。我们提出了一个三臂随机对照试验来实现 这些目标。不够活跃的年轻人将收到一个消费者活动跟踪器，并随机 分配给三个组之一：精确目标、随机目标或无目标。干预期将持续 12个月，维护评估为18个月。主要结果是平均每天的步数 根据越来越多的证据，增加这一易于解释和广泛使用的指标可以降低 成年期的心血管风险(在基线、3、6、12和18个月进行评估)。如果成功，则基础 来自可穿戴设备的密集数据提供的算法分析将提供一种新的、可扩展的方法 在年轻人心血管风险的初级和初级预防中使用消费者可穿戴设备。

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.