SCH: Control Systems Engineering for Counteracting Notification Fatigue: An Examination of Health Behavior Change

广泛的技术，如智能手机、可穿戴设备（例如，Fitbit、Apple Watch），以及医疗 设备使用警报来激励用户的动作。潜在有用的警报以警报疲劳为代价 由此个人随着时间的推移忽略警报。例如，一些身体活动干预使用警报 以激发活动;通知最初有效，但随着时间的推移效果会减弱。忽略警报是 在许多领域都存在问题。例如，通知疲劳降低了干预措施的效力 (e.g.,通知以激发行走），并且在其他区域（例如在医院中， 通知疲劳可能导致提供者忽略安全警报（例如，交叉药物相互作用） 电子病历。需要用于减少警觉疲劳的新颖解决方案。 位置、数字轨迹和其他数据使得能够推断人何时期望/需要警报， 此后标记为即时状态，但需要更高级的分析。例如是 建议步行（例如，短信说，“想去散步吗？“）只能产生预期的结果 当一个人的状态（例如，低压力）和上下文（例如，没有会议，好天气）对齐， 一个人欣赏通知（我们称之为接受性），并能对其采取行动（我们称之为 机会）。估计给定时刻处于即时状态的可能性不仅需要数据 而且是一种管理多变量、动态、特质和多时间尺度性质的方法， 问题.回到步行的例子，压力、天气和位置会随着每次步行而动态变化。 影响通知将激发步行的可能性。在我们的工作中，结果表明， 预测步数的因素：有些人在压力下走得更多，有些人走得更少，还有一些人没有 受到压力的影响。此外，即时通知不能在真空中查看，而是被 通常是一个更长期的过程的一部分，例如持续参与健康行为，从而使 这是一个多时间尺度问题。 本文的目的是提出一种实时状态估计策略，并在此基础上提出了一种新的状态估计算法。 用于步行的多时间尺度控制器作为控制系统方法的具体用例来抵消 警觉疲劳

A wide range of technologies, such as smartphones, wearables (e.g., Fitbit, Apple Watch), and medical devices use alerts to inspire actions of users. Potentially useful alerts come at the cost of alert fatigue whereby individuals ignore alerts over time. For example, several physical activity interventions use alerts to inspire activity; notifications work initially but with diminished efficacy over time. Ignoring alerts is problematic in a variety of domains. For example, notification fatigue reduces the potency of interventions (e.g., notifications to inspire walking) and can be a safety risk in other areas such as in hospitals where notification fatigue can lead providers to ignore safety alerts (e.g., cross-drug interaction) provided by the electronic medical record. There is a need for novel solutions for reducing alert fatigue. Location, digital traces, and other data enable inference of states when a person would desire/need alerts, henceforth labeled just-in-time states, but more advanced analytics are needed. For example, a suggestion to walk (e.g., SMS saying, "Want to go for a walk?") may only produce the desired outcome when a person's state (e.g., low stress) and context (e.g., no meetings, nice weather) align such that the person appreciates the notification (what we label receptivity) and can act on it (what we label opportunity). Estimating the likelihood that a given moment is a just-in-time state requires not only data but also an approach to manage the multivariate, dynamic, idiosyncratic, and multi-timescale nature of the problem. Returning to the walking example, stress, weather, and location change dynamically with each influencing the likelihood that a notification will inspire walking. In our work, results suggest idiosyncrasy in the factors that predict steps: some people walk more when stressed, others less, and still others are not influenced by stress. Further, just-in-time notifications cannot be viewed in a vacuum and, instead, are often part of a more long-term process, such as sustained engagement in a health behavior, thus making it a multi-timescale problem. The purpose of this work is to develop a just-in-time state estimation strategy and to stage a multi-timescale controller for walking as a concrete use-case of a control systems approach to counteract alert fatigue.