Shift workers experience profound circadian disruption, which can have deleterious long term effects on theirhealth and quality of life. Mood, fatigue, and performance can be improved in shift workers by moving thetiming of their peak circadian drive to sleep outside the hours they are expected to work. This can be achievedwith a targeted lighting intervention, as light is the primary input to the body's circadian clock. Crafting such anintervention for an individual, however, requires knowledge of the person's starting circadian state, which hastraditionally been hard to assess in shift workers. The gold standard measure of circadian timing is dim lightmelatonin onset, or DLMO. For day workers, DLMO most commonly occurs in a six hour window prior tohabitual bedtime. For fixed night shift workers, however, DLMO can occur anytime over the 24-hour day. Thisrequires 24 hours of melatonin collection in order to arrive at a single indicator of internal time, which is oftenprohibitively time consuming and expensive. Recently, we have developed new techniques for noninvasively predicting circadian timing throughconsumer wearable devices (e.g. Apple Watch). These techniques can predict DLMO timing to within 2 hoursfor more than three-quarters of shift workers working night shifts. The PIs of this grant have also developedmathematical techniques for generating lighting recommendations based on predicted circadian timing, aimedat 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 tracktheir circadian state and to make recommendations for how they can expose themselves to light to feel betterand reduce the long term negative health impacts of shift work. We will design the app based on interviewswith shift workers in an iterative process. Twenty-five shift workers will be recruited to be in a usability trialassessing the app. We will have them wear an Apple Watch for one week prior to the start of the usability trialto collect baseline data, and we will collect DLMO at the conclusion of that week. For two weeks after collectionof DLMO, we will have them interact with the mobile app, including following the recommendations it makesand documenting their compliance with the recommendations. At the conclusion of the trial, we will ask for theirfeedback 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, couldinform employer scheduling decisions, and could be used to increase retention in critical shift work professionswhile reducing the negative health impacts of night shifts on workers.
Public Health Relevance Statement: PROJECT NARRATIVE
Shift workers experience significant disruption to their internal clocks, which has marked effects on their health
and quality of life. Lighting interventions hold promise for improving mood, fatigue, and performance in night
shift workers. In this project, we will develop a mobile app for shift workers that provides lighting and behavioral
recommendations, and assess its user experience in a usability trial at the Henry Ford Health System.
Project Terms:
