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(如Fitbit、Apple Watch)和医疗 设备使用警报来激励用户的行动。潜在有用的警报是以警示疲劳为代价的 因此，随着时间的推移，个人会忽略警报。例如，几种体力活动干预使用警报 为了激发活动；通知最初是有效的，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，通知疲劳会降低干预措施的效力 (例如，激励步行的通知)，并且在其他领域可能是安全风险，例如在医院 通知疲劳可能会导致提供者忽略由 电子病历。需要新的解决方案来减少警觉疲劳。 位置、数字轨迹和其他数据使得能够推断当人希望/需要警报时的状态， 此后被贴上即时状态的标签，但还需要更高级的分析。例如，一个 建议散步(例如，发短信说“想去散散步吗？”)可能只会产生预期的结果 当一个人的状态(例如，压力较小)和环境(例如，没有会议，天气良好)一致时， 个人欣赏通知(我们标记为可接受性)并能对其采取行动(我们标记为 机会)。估计给定时刻处于即时状态的可能性不仅需要数据 也是一种管理多变量、动态、特殊和多时间尺度性质的方法 有问题。返回到步行的示例，压力、天气和位置都会随着每个因素而动态变化 影响通知将激励步行的可能性。在我们的工作中，结果表明 预测步数的因素：一些人在压力下走得更多，另一些人走得更少，还有一些人不走 受压力的影响。此外，即时通知不能在真空中查看，而是 通常是更长期过程的一部分，例如持续参与健康行为，从而使 这是一个多时间尺度的问题. 这项工作的目的是开发一种即时状态估计策略，并在 步行的多时间标度控制器--控制系统方法的具体用例 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.