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 an 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.

中文翻译：

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移动医疗应用中长期平均结果的离策略估计


由于可穿戴设备和传感技术的最新进展，健康科学家越来越多地开发移动健康（mHealth）干预措施。在移动医疗干预中，移动设备用于在个人日常生活中为他们提供治疗。这些治疗通常旨在影响近期、近期结果，例如压力或体力活动。移动健康干预政策通常称为适时适应性干预，是一种决策规则，可将个人当前状态（例如，个人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）映射到在许多时间点的每个时间点进行特定的治疗。目前绝大多数移动医疗干预措施都部署了专家制定的政策。在本文中，我们提供了一种方法，使用在可能不同的政策下收集的历史数据来推断一个或多个此类政策的绩效。如果遵循特定的移动医疗政策，我们的绩效衡量标准是长期内近期结果的平均值。我们提供估计量和置信区间。这项工作是由 HeartSteps（一种移动健康身体活动干预措施）推动的。