Exploring predictors of insomnia severity in shift workers using machine learning model.

ABSTRACT:

Introduction

Insomnia in shift workers has distinctive features due to circadian rhythm disruption caused by reversed or unstable sleep-wake cycle work schedules. While previous studies have primarily focused on a limited number of predictors for insomnia severity in shift workers, there is a need to further explore key predictors, and develop a data-driven prediction model for insomnia in shift workers. This study aims to identify potential predictors of insomnia severity in shift workers using a machine learning (ML) approach and evaluate the accuracy of the resulting prediction model.

Methods

We assessed the predictors of insomnia severity in large samples of individuals (4,572 shift workers and 2,093 non-shift workers). The general linear model with the least absolute sh

SUBMITTER: Yeo H 

PROVIDER: S-EPMC11949791 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature