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

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

SUBMITTER: Martin J 

PROVIDER: S-EPMC10773524 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature