Assessment of mobile application-delivered lighting interventions for reducing circadian disruption in shift workers

项目总结 倒班工人会经历严重的昼夜节律紊乱，这可能会对他们的 健康和生活质量。轮班工作人员的情绪、疲倦和工作表现可以通过移动 他们的昼夜节律高峰期开车在预期的工作时间之外睡觉。这是可以实现的 有针对性的照明干预，因为光是人体生物钟的主要输入。精心制作这样一个 然而，对个人的干预需要了解这个人的开始昼夜节律状态，这已经 传统上很难在轮班工人中进行评估。暗光是衡量昼夜节律的黄金标准 褪黑素发作，或称DLMO。对于日间工作人员，DLMO最常发生在6小时前的窗口中 习惯的就寝时间。然而，对于固定的夜班工人，DLMO可以在24小时内的任何时间发生。这 需要24小时收集褪黑激素，才能得出单一的内部时间指标，这通常是 令人望而却步的耗时和昂贵。 最近，我们开发了非侵入性预测昼夜节律的新技术 消费者可穿戴设备(例如Apple Watch)。这些技术可以预测DLMO时间在2小时内 对于超过四分之三上夜班的轮班工人来说。这笔赠款的私人投资指数也开发了 基于预测的昼夜节律时序生成照明建议的数学技术 在将昼夜节律的高峰期转移到工作时间窗口之外睡觉的时候。 在此第一阶段STTR中，我们建议为轮班工作人员开发iOS移动应用程序，以实现这两个目标 他们的昼夜节律状态，并就如何让自己暴露在阳光下感觉更好提出建议 并减少倒班工作对健康的长期负面影响。我们将根据采访设计这款应用程序 与轮班工人在一个迭代的过程中。25名轮班工人将被招募参加可用性测试 正在评估该应用程序。我们将在可用性试验开始前让他们佩戴Apple Watch一周 收集基线数据，我们将在当周结束时收集DLMO。收集后的两个星期 对于DLMO，我们将让他们与移动应用程序进行互动，包括遵循它提出的建议 并记录他们对建议的遵守情况。在审判结束时，我们将要求他们 对应用程序的反馈，以改进算法并对设计进行更新。 最终，这类应用程序可以与家庭和工作场所的智能照明系统接口，可以 为雇主的日程安排决策提供信息，并可用于增加关键班次工作专业的留任率 同时减少夜班对工人健康的负面影响。

PROJECT SUMMARY Shift workers experience profound circadian disruption, which can have deleterious long term effects on their health and quality of life. Mood, fatigue, and performance can be improved in shift workers by moving the timing of their peak circadian drive to sleep outside the hours they are expected to work. This can be achieved with a targeted lighting intervention, as light is the primary input to the body’s circadian clock. Crafting such an intervention for an individual, however, requires knowledge of the person’s starting circadian state, which has traditionally been hard to assess in shift workers. The gold standard measure of circadian timing is dim light melatonin onset, or DLMO. For day workers, DLMO most commonly occurs in a six hour window prior to habitual bedtime. For fixed night shift workers, however, DLMO can occur anytime over the 24-hour day. This requires 24 hours of melatonin collection in order to arrive at a single indicator of internal time, which is often prohibitively time consuming and expensive. Recently, we have developed new techniques for noninvasively predicting circadian timing through consumer wearable devices (e.g. Apple Watch). These techniques can predict DLMO timing to within 2 hours for more than three-quarters of shift workers working night shifts. The PIs of this grant have also developed mathematical techniques for generating lighting recommendations based on predicted circadian timing, aimed at shifting the peak circadian drive to sleep outside the window of working hours. In this Phase I STTR, we propose to develop an iOS mobile application for shift workers, to both track their circadian state and to make recommendations for how they can expose themselves to light to feel better and reduce the long term negative health impacts of shift work. We will design the app based on interviews with shift workers in an iterative process. Twenty-five shift workers will be recruited to be in a usability trial assessing the app. We will have them wear an Apple Watch for one week prior to the start of the usability trial to collect baseline data, and we will collect DLMO at the conclusion of that week. For two weeks after collection of DLMO, we will have them interact with the mobile app, including following the recommendations it makes and documenting their compliance with the recommendations. At the conclusion of the trial, we will ask for their feedback on the app in order to improve the algorithms and make updates to the design. Ultimately, an app of this kind could interface with home and workplace smart lighting systems, could inform employer scheduling decisions, and could be used to increase retention in critical shift work professions while reducing the negative health impacts of night shifts on workers.