Association between Self-Reported Prior Night's Sleep and Single-Task Gait in Healthy, Young Adults: A Study Using Machine Learning

• Authors: Ali Boolani, Clarkson University; Joel Martin, George Mason University; Haikun Huang, George Mason University; Lap-Fai Yu, George Mason University; Maggie Stark, Lake Erie College of Osteopathic Medicine; Zachary Grin, Clarkson University; Marissa Roy, George Mason University; Chelsea Yager, Latham Medical Group; Seema Teymouri, State University of New York Canton; Dylan Bradley, Hanover College; Rebecca Martin, St. Joseph’s Hospital Health Center; George Fulk, Emory University; Rumit Singh Kakar, Oakland University
• Journal Title: SENSORS
• Volume: Volume 22, Number 19
• Publisher: MDPI | 2022-10-01
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.3390/s22197406
• Subjects: Health Sciences, Medicine and Surgery
• Keywords: Science & Technology; Physical Sciences; Technology; Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation; Chemistry; Engineering; partial sleep deprivation; sleep extension; lower extremity kinematics; gait assessment; SEX-DIFFERENCES; DUAL-TASK; DEPRIVATION; DURATION; PARAMETERS; PERFORMANCE; EXTENSION; FOCUS; ATTENTION; WALKING

Abstract

Failure to obtain the recommended 7–9 h of sleep has been associated with injuries in youth and adults. However, most research on the influence of prior night’s sleep and gait has been conducted on older adults and clinical populations. Therefore, the objective of this study was to identify individuals who experience partial sleep deprivation and/or sleep extension the prior night using single task gait. Participants (n = 123, age 24.3 ± 4.0 years; 65% female) agreed to participate in this study. Self-reported sleep duration of the night prior to testing was collected. Gait data was collected with inertial sensors during a 2 min walk test. Group differences (<7 h and >9 h, poor sleepers; 7–9 h, good sleepers) in gait characteristics were assessed using machine learning and a post-hoc ANCOVA. Results indicated a correlation (r = 0.79) between gait parameters and prior night’s sleep. The most accurate machine learning model was a Random Forest Classifier using the top 9 features, which had a mean accuracy of 65.03%. Our findings suggest that good sleepers had more asymmetrical gait patterns and were better at maintaining gait speed than poor sleepers. Further research with larger subject sizes is needed to develop more accurate machine learning models to identify prior night’s sleep using single-task gait.