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

基本信息

批准号：
10152695
负责人：
DAVID E. CONROY
金额：
$ 55.72万
依托单位：
PENNSYLVANIA STATE UNIVERSITY, THE
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-01 至 2023-04-30
项目状态：
已结题

项目摘要

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.

项目摘要身体上的不活动是一系列生活方式因素的一部分 - 吸烟和饮食 - 有助于体重成年初。损害心血管健康的危险因素开始积累过渡到成年。在此期间阻止身体活动下降（PA）的干预措施可以降低长期慢性疾病风险。短信干预措施对 PA但是通过裁缝，定位或个性化来增加这些干预效果的努力尚未实现他们的潜力。已经出现了基于治疗反应或上下文因素（例如，阶梯护理，即时自适应干预措施），但它们应用了单个决策规则对于所有参与者而言均匀。行为是复杂的，多次确定，因此可以治疗响应是特殊的，必要的个性化决策规则。基于精确的兴趣医学，我们提出了一种使用密集型开发个性化自适应消息干预措施的方法纵向数据（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，我们开发了体育活动的计算模型对单个文本消息的响应。最大的障碍在干预措施中实施该方法是预测建模所需的计算模型 PA动力学的不确定性高，并且太复杂了，无法在智能手机上有效运行其他可穿戴设备。我们建议通过（1）开发物理动态模型来解决这个问题基于对消息的历史响应，最近的行为，特定于位置的天气和临时的活动功能，以及（2）评估更多与更具侵略性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加PA消息干预并开发出对不同的响应的计算模型在不同条件下的消息。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）在变化下实现了指定的行为改变目标状况。然后，我们将使用独立的年轻人样本部署该控制器，以确定如何在接下来的六个月中，更多与较不积极的适应策略会影响用户体验。这项研究将贡献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干预措施，以增加PA。如果成功，它将增加PA和用户通过选择和计时消息来接触，以最大程度地提高效果并最大程度地减轻负担。这种方法可以应用于制定针对预防体重增加有关的其他行为的个性化干预措施保留心血管健康，并降低慢性疾病风险。

项目成果

期刊论文数量（16）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Identification of switched autoregressive exogenous systems from large noisy datasets.

DOI：
10.1002/rnc.4968
发表时间：
2020-10-01
期刊：
International journal of robust and nonlinear control
影响因子：
3.9
作者：
Hojjatinia S;Lagoa CM;Dabbene F
通讯作者：
Dabbene F

Steps towards digital tools for personalised physical activity promotion

DOI：
10.1136/bjsports-2021-104169
发表时间：
2021-09-16
期刊：
BRITISH JOURNAL OF SPORTS MEDICINE
影响因子：
18.4
作者：
Conroy, David E.;Bennett, Gary G.;Wolin, Kathleen Y.
通讯作者：
Wolin, Kathleen Y.

Dynamic models of stress-smoking responses based on high-frequency sensor data.

DOI：
10.1038/s41746-021-00532-2
发表时间：
2021-11-23
期刊：
NPJ digital medicine
影响因子：
15.2
作者：
Hojjatinia S;Daly ER;Hnat T;Hossain SM;Kumar S;Lagoa CM;Nahum-Shani I;Samiei SA;Spring B;Conroy DE
通讯作者：
Conroy DE

Wearable device adherence among insufficiently-active young adults is independent of identity and motivation for physical activity.

活动不足的年轻人对可穿戴设备的依从性与身体活动的身份和动机无关。

DOI：
10.1007/s10865-023-00444-4
发表时间：
2024
期刊：
Journal of behavioral medicine
影响因子：
3.1
作者：
Wu,Jingchuan;Olson,JennyL;Brunke-Reese,Deborah;Lagoa,ConstantinoM;Conroy,DavidE
通讯作者：
Conroy,DavidE

Distributionally Robust Portfolio Optimization.