The Association Between Physical Activity/Heart Rate Variability Data Obtained Using a Wearable Device and Timed Motor Functional Tests in Patients with Duchenne Muscular Dystrophy: A Pilot Study

Abstract

Background: Duchenne muscular dystrophy (DMD) is a devastating X-linked muscle disease. Clinical evaluation of DMD uses patient-intensive motor function tests, and the recent development of wearable devices allows the collection of a variety of biometric information, including physical activity. Objective: In this study, we examined differences in physical activity and heart rate variability (HRV) between patients with DMD and healthy subjects using a wearable device, and investigated any association between these parameters and motor function in patients with DMD. Methods: Participants were 7 patients with DMD and 8 healthy males, whose physical activity and HRV were provided by a wearable device. These data were used to investigate the relationship between both physical activity and HRV parameters and timed motor functional tests [Time to stand from supine, 10-meter walking time (10MWT), North Star Ambulatory Assessment (NSAA), and 6-minute walking test (6MWT)] in patients with DMD. Results: Results of 24-hours physical activity, fat burning, total number of steps and active distance, average step rate, average exercise intensity during walking, exercise, degree of forward lean during walking, maximum heart rate, normalized low frequency power (LF norm), and maximum exercise intensity in patients with DMD were lower than those in control subjects. Physical activity and HRV parameters did not correlate with the time to stand from supine. The 10MWT positively correlated with average heart rate, while NSAA negatively correlated with average heart rate, total frequency power (TF), and very low frequency power (VLF) during arousal. The 6MWT negatively correlated with ratio LF/high frequency power (HF). CONCLUSIONS: Physical activity and HRV indices that differ from those of normal children and that correlate with motor function assessment may serve as digital biomarkers.