Sensor-Based Estimation of Dim Light Melatonin Onset (DLMO) Using Features of Two Time Scales

TL;DR

This paper introduces a novel two-step, two-time-scale framework using machine learning models to accurately estimate dim light melatonin onset (DLMO), reducing errors compared to existing single-scale methods.

Contribution

It proposes a new two-step approach that combines summary data from previous days with current day features for improved DLMO estimation.

Findings

Two-step model outperforms single-scale models in accuracy

Statistically significant reduction in root-mean-square error

Effective use of both daily and frequent sampling data

Abstract

Circadian rhythms influence multiple essential biological activities including sleep, performance, and mood. The dim light melatonin onset (DLMO) is the gold standard for measuring human circadian phase (i.e., timing). The collection of DLMO is expensive and time-consuming since multiple saliva or blood samples are required overnight in special conditions, and the samples must then be assayed for melatonin. Recently, several computational approaches have been designed for estimating DLMO. These methods collect daily sampled data (e.g., sleep onset/offset times) or frequently sampled data (e.g., light exposure/skin temperature/physical activity collected every minute) to train learning models for estimating DLMO. One limitation of these studies is that they only leverage one time-scale data. We propose a two-step framework for estimating DLMO using data from both time scales. The first step summarizes data from before the current day, while the second step combines this summary with frequently sampled data of the current day. We evaluate three moving average models that input sleep timing data as the first step and use recurrent neural network models as the second step. The results using data from 207 undergraduates show that our two-step model with two time-scale features has statistically significantly lower root-mean-square errors than models that use either daily sampled data or frequently sampled data.