Investigating Speed Deviation Patterns During Glucose Episodes: A Quantile Regression Approach

TL;DR

This study uses quantile regression to analyze how glucose episodes in diabetic drivers influence speed behavior, revealing distribution patterns that go beyond average speed analysis in naturalistic driving environments.

Contribution

It introduces a distribution-based analytic method to identify speed deviation patterns during glucose episodes, advancing beyond traditional average speed analyses.

Findings

Identified distinct speed deviation patterns during glucose episodes.

Demonstrated the effectiveness of quantile regression in analyzing driving behavior.

Provided insights into how glucose levels impact driving safety.

Abstract

Given the growing prevalence of diabetes, there has been significant interest in determining how diabetes affects instrumental daily functions, like driving. Complication of glucose control in diabetes includes hypoglycemic and hyperglycemic episodes, which may impair cognitive and psychomotor functions needed for safe driving. The goal of this paper was to determine patterns of diabetes speed behavior during acute glucose to drivers with diabetes who were euglycemic or control drivers without diabetes in a naturalistic driving environment. By employing distribution-based analytic methods which capture distribution patterns, our study advances prior literature that has focused on conventional approach of average speed to explore speed deviation patterns.