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

评估移动应用程序提供的照明干预措施，以减少轮班工人的昼夜节律紊乱

• 批准号: 10384670
• 负责人: Philip Cheng
• 金额: $ 25.49万
• 依托单位: ARCASCOPE, INC
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10384670
• 关键词: Accelerometer; Address; Algorithms; Android; Anxiety; Apple watch; Behavior Therapy; Behavioral; Biological; Biological Clocks; Biological Rhythm; Body Temperature; Circadian Dysregulation; Circadian desynchrony; Clinical Trials; Collection; Conflict (Psychology); Consumption; Data; Development; Devices; Exposure to; Fatigue; Feedback; Functional disorder; Generations; Goals; Gold; Grant; Health; Health system; Home; Hour; Impaired cognition; Individual; Intervention; Interview; Knowledge; Laboratories; Light; Light Exercise; Lighting; Long-Term Effects; Mathematics; Melatonin; Mental disorders; Methods; Modeling; Moods; Output; Performance; Persons; Phase; Placebos; Population; Positioning Attribute; Process; Quality of life; Randomized Clinical Trials; Recommendation; Reporting; Risk; Sales; Schedule; Signal Transduction; Sleep; Small Business Technology Transfer Research; Societies; System; Techniques; Testing; Time; Translating; Update; Work; Workplace; base; cancer risk; cardiometabolism; circadian; circadian pacemaker; comparison intervention; consumer product; cost; design; dosage; experience; global health; health data; improved; mathematical model; mobile application; novel; personalized intervention; recruit; rectal; response; salivary assay; shift work; simulation; smartphone based assessment; standard measure; technological innovation; tool; usability; wearable device

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.

项目概要 轮班工人的昼夜节律受到严重干扰，这可能会对他们的身体产生长期有害影响 通过移动可以改善轮班工人的健康和生活质量。 在预计工作时间之外安排他们的昼夜节律高峰睡眠时间 这是可以实现的。 通过有针对性的照明干预，因为光是人体生物钟的主要输入。 然而，对个人的干预需要了解该人的起始昼夜节律状态，这已经 传统上很难对轮班工人进行评估，衡量昼夜节律的黄金标准是昏暗的光线。 褪黑激素发作 (DLMO) 对于日间工作人员来说，DLMO 最常发生在 6 小时之前。 然而，对于固定夜班工人来说，DLMO 可能在一天 24 小时内的任何时间发生。 需要 24 小时收集褪黑激素才能得出内部时间的单一指标，这通常是 非常耗时且昂贵。 最近，我们开发了通过非侵入性预测昼夜节律的新技术 这些技术可以在 2 小时内预测消费者可穿戴设备（例如 Apple Watch）。 对于超过四分之三的夜班工人来说，这项补助金的绩效指标也已制定。 基于预测的昼夜节律时间生成照明建议的数学技术，旨在 将昼夜节律高峰转移到工作时间之外的睡眠时间。 在第一阶段 STTR 中，我们建议为轮班工人开发一个 iOS 移动应用程序，以跟踪 他们的昼夜节律状态，并就如何让自己暴露在光线下以感觉更好提出建议 并减少轮班工作对健康的长期负面影响。我们将根据访谈设计该应用程序。 将招募 25 名轮班工人进行可用性试验。 在可用性试验开始前，我们将让他们佩戴 Apple Watch 一周来评估该应用程序。 收集基线数据，我们将在收集后两周内收集 DLMO。 DLMO 的人员，我们将让他们与移动应用程序进行交互，包括遵循其提出的建议 并记录他们对建议的遵守情况。在试验结束时，我们将询问他们的情况。 对应用程序的反馈，以改进算法并更新设计。 最终，此类应用程序可以与家庭和工作场所智能照明系统交互，可以 为雇主的调度决策提供信息，并可用于提高关键轮班工作职业的保留率 同时减少夜班对工人健康的负面影响。