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CC BY-NC-ND 4.0 · Sleep Sci 2023; 16(04): e399-e407
DOI: 10.1055/s-0043-1776748
Original Article

Association between Self-reported Sleep Quality and Single-task Gait in Young Adults: A Study Using Machine Learning

Joel Martin 
1   School of Kinesiology, Sports Medicine Assessment Research & Testing (SMART) Laboratory, George Mason University, Manassas, VA, United States of America
,
Haikun Huang
2   Department of Computer Science, George Mason University, Fairfax, VA, United States of America
,
Ronald Johnson
1   School of Kinesiology, Sports Medicine Assessment Research & Testing (SMART) Laboratory, George Mason University, Manassas, VA, United States of America
,
Lap-Fai Yu
2   Department of Computer Science, George Mason University, Fairfax, VA, United States of America
,
Erica Jansen
3   Department of Nutritional Sciences, University of Michigan, Ann Arbor, MI, United States of America
4   Department of Neurology, University of Michigan, Ann Arbor, MI, United States of America
,
Rebecca Martin
5   Department of Physical Therapy, Hanover College, Hanover, IN, United States of America
,
Chelsea Yager
6   Department of Neurology, St. Joseph's Hospital Health Center, Syracuse, NY, United States of America
,
Ali Boolani
7   Department of Physical Therapy, Clarkson University, Potsdam, NY, United States of America
8   Department of Biology, Clarkson University, Potsdam, NY, United States of America
› Author Affiliations Sources of Funding The authors declare that they have received no funding from agencies in the public, private or non-profit sector for the conduction of the present study.
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Abstract

Objective The objective of the present study was to find biomechanical correlates of single-task gait and self-reported sleep quality in a healthy, young population by replicating a recently published study.

Materials and Methods Young adults (n = 123) were recruited and were asked to complete the Pittsburgh Sleep Quality Inventory to assess sleep quality. Gait variables (n = 53) were recorded using a wearable inertial measurement sensor system on an indoor track. The data were split into training and test sets and then different machine learning models were applied. A post-hoc analysis of covariance (ANCOVA) was used to find statistically significant differences in gait variables between good and poor sleepers.

Results AdaBoost models reported the highest correlation coefficient (0.77), with Support-Vector classifiers reporting the highest accuracy (62%). The most important features associated with poor sleep quality related to pelvic tilt and gait initiation. This indicates that overall poor sleepers have decreased pelvic tilt angle changes, specifically when initiating gait coming out of turns (first step pelvic tilt angle) and demonstrate difficulty maintaining gait speed.

Discussion The results of the present study indicate that when using traditional gait variables, single-task gait has poor accuracy prediction for subjective sleep quality in young adults. Although the associations in the study are not as strong as those previously reported, they do provide insight into how gait varies in individuals who report poor sleep hygiene. Future studies should use larger samples to determine whether single task-gait may help predict objective measures of sleep quality especially in a repeated measures or longitudinal or intervention framework.

Keywords

sleep - walking - lower extremity biomechanics

Supplementary Material



Publication History

Received: 13 October 2022

Accepted: 25 January 2023

Article published online:
22 November 2023

© 2023. Brazilian Sleep Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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