Enhanced Circadian Phase Tracking: A 5-h DLMO Sampling Protocol Using Wearable Data

Abstract

Circadian medicine aims to leverage the body's internal clock to develop safer and more effective therapeutics. Traditionally, biological time has been estimated using dim light melatonin onset (DLMO), a method that requires collecting saliva samples over a long period under controlled conditions, to ensure the observation of DLMO, making it time-consuming and labor-intensive. While some studies have mitigated this by reducing the length of the sampling window, they significantly failed to identify the DLMO for shift workers. In this study, we present a framework that reduces the DLMO experiment time for shift workers to just 5 h. This approach combines sleep-wake pattern data from wearable devices with a mathematical model to predict DLMO prospectively. Based on this prediction, we define a targeted 5-h sampling window, from 3 h before to 2 h after the estimated DLMO. Testing this framework with 19 shift workers, we successfully identified the DLMO for all participants, whereas traditional methods failed for more than 40% of participants. This approach significantly reduces the experiment time required for measuring the DLMO of shift workers from 24 h to 5 h, simplifying the circadian phase measurements for shift workers.