Liao Peng, Klasnja Predrag, Murphy Susan
Department of Statistics, University of Michigan.
School of Information, University of Michigan.
J Am Stat Assoc. 2021;116(533):382-391. doi: 10.1080/01621459.2020.1807993. Epub 2020 Oct 1.
Due to the recent advancements in wearables and sensing technology, health scientists are increasingly developing mobile health (mHealth) interventions. In mHealth interventions, mobile devices are used to deliver treatment to individuals as they go about their daily lives. These treatments are generally designed to impact a near time, proximal outcome such as stress or physical activity. The mHealth intervention policies, often called just-in-time adaptive interventions, are decision rules that map a individual's current state (e.g., individual's past behaviors as well as current observations of time, location, social activity, stress and urges to smoke) to a particular treatment at each of many time points. The vast majority of current mHealth interventions deploy expert-derived policies. In this paper, we provide an approach for conducting inference about the performance of one or more such policies using historical data collected under a possibly different policy. Our measure of performance is the average of proximal outcomes over a long time period should the particular mHealth policy be followed. We provide an estimator as well as confidence intervals. This work is motivated by HeartSteps, an mHealth physical activity intervention.
由于可穿戴设备和传感技术最近的进展，健康科学家们越来越多地开发移动健康（mHealth）干预措施。在移动健康干预中，移动设备用于在个体日常生活过程中为其提供治疗。这些治疗通常旨在影响近期、近端的结果，如压力或身体活动。移动健康干预政策，通常称为即时自适应干预，是一种决策规则，它将个体的当前状态（例如，个体过去的行为以及当前对时间、地点、社交活动、压力和吸烟冲动的观察）映射到多个时间点中每个时间点的特定治疗。当前绝大多数移动健康干预措施采用专家制定的政策。在本文中，我们提供了一种方法，用于使用在可能不同的政策下收集的历史数据对一个或多个此类政策的性能进行推断。我们的性能衡量标准是如果遵循特定的移动健康政策，在很长一段时间内近端结果的平均值。我们提供了一个估计器以及置信区间。这项工作的灵感来自于移动健康身体活动干预措施“心脏步数”（HeartSteps）。
