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Additional file 4: Peak and global mid ventricular strain (εcc %) in unaffected controls (n=15) and individuals with DMD (n=46) at baseline (UF Cohort).

Additional file 2: Longitudinal changes in global strain in DMD. Solid line for global strain was defined based on normal zone cut off of -17% as given by HARP software. Red lines indicates subjects with more than 5 years data, filled triangles represent unaffected controls.

Additional file 6: Comparison of cardiac function in control and DMD subjects at baseline (UF Cohort).

Additional file 3: Longitudinal change in global strain for mid ventricle of DMD.

Additional file 5: Strain for each LV segment in controls and individuals with DMD (n=46) at baseline (UF cohort).

Additional file 1: Global mid ventricular strain (εcc %) in unaffected controls (n=15) and individuals with DMD (n=58) at baseline.

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 3-16 years of age underwent eight walking/running activities, including five 25 meters walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-minute walk test (6MWT), a 100 meters 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.

With the rapid development of the internet of things (IoT) and artificial intelligence (AI) technologies, human activity recognition (HAR) has been applied in a variety of domains such as security and surveillance, human-robot interaction, and entertainment. Even though a number of surveys and review papers have been published, there is a lack of HAR overview papers focusing on healthcare applications that use wearable sensors. Therefore, we fill in the gap by presenting this overview paper. In particular, we present our projects to illustrate the system design of HAR applications for healthcare. Our projects include early mobility identification of human activities for intensive care unit (ICU) patients and gait analysis of Duchenne muscular dystrophy (DMD) patients. We cover essential components of designing HAR systems including sensor factors (e.g., type, number, and placement location), AI model selection (e.g., classical machine learning models versus deep learning models), and feature engineering. In addition, we highlight the challenges of such healthcare-oriented HAR systems and propose several research opportunities for both the medical and the computer science community.

ImportanceOne major advantage of developing large, federally funded networks for clinical research in neurology is the ability to have a trial-ready network that can efficiently conduct scientifically rigorous projects to improve the health of people with neurologic disorders.ObservationsNational Institute of Neurological Disorders and Stroke Network for Excellence in Neuroscience Clinical Trials (NeuroNEXT) was established in 2011 and renewed in 2018 with the goal of being an efficient network to test between 5 and 7 promising new agents in phase II clinical trials. A clinical coordinating center, data coordinating center, and 25 sites were competitively chosen. Common infrastructure was developed to accelerate timelines for clinical trials, including central institutional review board (a first for the National Institute of Neurological Disorders and Stroke), master clinical trial agreements, the use of common data elements, and experienced research sites and coordination centers. During the first 7 years, the network exceeded the goal of conducting 5 to 7 studies, with 9 funded. High interest was evident by receipt of 148 initial applications for potential studies in various neurologic disorders. Across the first 8 studies (the ninth study was funded at end of initial funding period), the central institutional review board approved the initial protocol in a mean (SD) of 59 (21) days, and additional sites were added a mean (SD) of 22 (18) days after submission. The median time from central institutional review board approval to first site activation was 47.5 days (mean, 102.1; range, 1-282) and from first site activation to first participant consent was 27 days (mean, 37.5; range, 0-96). The median time for database readiness was 3.5 months (mean, 4.0; range, 0-8) from funding receipt. In the 4 completed studies, enrollment met or exceeded expectations with 96% overall data accuracy across all sites. Nine peer-reviewed manuscripts were published, and 22 oral presentations or posters and 9 invited presentations were given at regional, national, and international meetings.Conclusions and relevanceNeuroNEXT initiated 8 studies, successfully enrolled participants at or ahead of schedule, collected high-quality data, published primary results in high-impact journals, and provided mentorship, expert statistical, and trial management support to several new investigators. Partnerships were successfully created between government, academia, industry, foundations, and patient advocacy groups. Clinical trial consortia can efficiently and successfully address a range of important neurologic research and therapeutic questions.

Introduction: The Duchenne Regulatory Science Consortium (D-RSC) was established to develop tools to accelerate drug development for DMD. The resulting tools are anticipated to meet validity requirements outlined by qualification/endorsement pathways at both the U.S. Food and Drug Administration (FDA) and European Medicines Administration (EMA), and will be made available to the drug development community. The initial goals of the consortium include the development of a disease progression model, with the goal of creating a model that would be used to forecast changes in clinically meaningful endpoints, which would inform clinical trial protocol development and data analysis. Methods: In April of 2016 the consortium and other experts met to formulate plans for the development of the model. Conclusions: Here we report the results of the meeting, and discussion as to the form of the model that we plan to move forward to develop, after input from the regulatory authorities.

ObjectiveWe studied the effects of LTBP4 and SPP1 polymorphisms on age at loss of ambulation (LoA) in a multiethnic Duchenne muscular dystrophy (DMD) cohort.MethodsWe genotyped SPP1 rs28357094 and LTBP4 haplotype in 283 of 340 participants in the Cooperative International Neuromuscular Research Group Duchenne Natural History Study (CINRG-DNHS). Median ages at LoA were compared by Kaplan-Meier analysis and log-rank test. We controlled polymorphism analyses for concurrent effects of glucocorticoid corticosteroid (GC) treatment (time-varying Cox regression) and for population stratification (multidimensional scaling of genome-wide markers).ResultsHispanic and South Asian participants (n = 18, 41) lost ambulation 2.7 and 2 years earlier than Caucasian subjects (p = 0.003

Introduction: Duchenne muscular dystrophy (DMD) subjects ≥5 years with nonsense mutations were followed for 48 weeks in a multicenter, randomized, double-blind, placebo-controlled trial of ataluren. Placebo arm data (N = 57) provided insight into the natural history of the 6-minute walk test (6MWT) and other endpoints. Methods: Evaluations performed every 6 weeks included the 6-minute walk distance (6MWD), timed function tests (TFTs), and quantitative strength using hand-held myometry. Results: Baseline age (≥7 years), 6MWD, and selected TFT performance are strong predictors of decline in ambulation (Δ6MWD) and time to 10% worsening in 6MWD. A baseline 6MWD of

Introduction: An international clinical trial enrolled 174 ambulatory males ≥5 years old with nonsense mutation Duchenne muscular dystrophy (nmDMD). Pretreatment data provide insight into reliability, concurrent validity, and minimal clinically important differences (MCIDs) of the 6-minute walk test (6MWT) and other endpoints. Methods: Screening and baseline evaluations included the 6-minute walk distance (6MWD), timed function tests (TFTs), quantitative strength by myometry, the PedsQL, heart rate–determined energy expenditure index, and other exploratory endpoints. Results: The 6MWT proved feasible and reliable in a multicenter context. Concurrent validity with other endpoints was excellent. The MCID for 6MWD was 28.5 and 31.7 meters based on 2 statistical distribution methods. Conclusions: The ratio of MCID to baseline mean is lower for 6MWD than for other endpoints. The 6MWD is an optimal primary endpoint for Duchenne muscular dystrophy (DMD) clinical trials that are focused therapeutically on preservation of ambulation and slowing of disease progression. Muscle Nerve 48: 357–368, 2013