Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches.

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.

Gait Event Detection and Travel Distance Using Waist-Worn Accelerometers across a Range of Speeds: Automated Approach.

Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed, and distance traveled, is an important component of community-based mobility evaluation using wearable accelerometers. However, accurate unsupervised computerized measurement of CFs of individuals with Duchenne muscular dystrophy (DMD) who have progressive loss of ambulatory mobility is difficult due to differences in patterns and magnitudes of acceleration across their range of attainable gait velocities. This paper proposes a novel calibration method. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction. The method demonstrates high accuracy in children with DMD and typically developing controls (TDs) regardless of the participant's level of ability. Fifteen children with DMD and fifteen TDs underwent supervised clinical testing across a range of gait speeds using 10 m or 25 m run/walk (10 MRW, 25 MRW), 100 m run/walk (100 MRW), 6-min walk (6 MWT), and free-walk (FW) evaluations while wearing a mobile-phone-based accelerometer at the waist near the body's center of mass. Following calibration by a trained clinical evaluator, CFs were extracted from the accelerometer data using a multi-step machine-learning-based process and the results were compared to ground-truth observation data. Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearson's r = -0.9929 to 0.9986, p < 0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground-truth observations for the combined 6 MWT, 100 MRW, and FW tasks. Our study findings indicate that a single waist-worn accelerometer calibrated to an individual's stride characteristics using our methods accurately measures CFs and estimates travel distances across a common range of gait speeds in both DMD and TD peers.

(-)-Epicatechin induces mitochondrial biogenesis and markers of muscle regeneration in adults with Becker muscular dystrophy.

INTRODUCTION: We conducted an open-label study to examine the effects of the flavonoid (-)-epicatechin in seven ambulatory adult patients with Becker muscular dystrophy (BMD). METHODS: Seven participants received (-)-epicatechin 50 mg twice per day for 8 weeks. Pre- and postprocedures included biceps brachii biopsy to assess muscle structure and growth-relevant endpoints by western blotting, mitochondria volume measurement, and cristae abundance by electron microscopy, graded exercise testing, and muscle strength and function tests. RESULTS: Western blotting showed significantly increased levels of enzymes modulating cellular bioenergetics (liver kinase B1 and 5'-adenosine monophosphate-activated protein kinase). Peroxisome proliferator-activated receptor gamma coactivator-1alpha, a transcriptional coactivator of genes involved in mitochondrial biogenesis and cristae-associated mitofilin levels, increased as did cristae abundance. Muscle and plasma follistatin increased significantly while myostatin decreased. Markers of skeletal muscle regeneration myogenin, myogenic regulatory factor-5, myoblast determination protein 1, myocyte enhancer factor-2, and structure-associated proteins, including dysferlin, utrophin, and intracellular creatine kinase, also increased. Exercise testing demonstrated decreased heart rate, maximal oxygen consumption per kilogram, and plasma lactate levels at defined workloads. Tissue saturation index improved in resting and postexercise states. DISCUSSION: (-)-Epicatechin, an exercise mimetic, appears to have short-term positive effects on tissue biomarkers indicative of mitochondrial biogenesis and muscle regeneration, and produced improvements in graded exercise testing parameters in patients with BMD.

Motor and cognitive assessment of infants and young boys with Duchenne Muscular Dystrophy: results from the Muscular Dystrophy Association DMD Clinical Research Network.

Therapeutic trials in Duchenne Muscular Dystrophy (DMD) exclude young boys because traditional outcome measures rely on cooperation. The Bayley III Scales of Infant and Toddler Development (Bayley III) have been validated in developing children and those with developmental disorders but have not been studied in DMD. Expanded Hammersmith Functional Motor Scale (HFMSE) and North Star Ambulatory Assessment (NSAA) may also be useful in this young DMD population. Clinical evaluators from the MDA-DMD Clinical Research Network were trained in these assessment tools. Infants and boys with DMD (n = 24; 1.9 ± 0.7 years) were assessed. The mean Bayley III motor composite score was low (82.8 ± 8; p ≤ .0001) (normal = 100 ± 15). Mean gross motor and fine motor function scaled scores were low (both p ≤ .0001). The mean cognitive comprehensive (p=.0002), receptive language (p ≤ .0001), and expressive language (p = .0001) were also low compared to normal children. Age was negatively associated with Bayley III gross motor (r = -0.44; p = .02) but not with fine motor, cognitive, or language scores. HFMSE (n=23) showed a mean score of 31 ± 13. NSAA (n = 18 boys; 2.2 ± 0.4 years) showed a mean score of 12 ± 5. Outcome assessments of young boys with DMD are feasible and in this multicenter study were best demonstrated using the Bayley III.