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.
