Quantitative measures of motor development in Angelman syndrome

Abstract

Angelman Syndrome is a rare neurodevelopmental disorder characterized by developmental delay, lack of speech, seizures, intellectual disability, characteristic behavior, and movement disorders. Clinical gait analysis provides the opportunity for movement quantification to investigate an observed maladaptive change in gait pattern and offers an objective outcome of change. Pressure‐sensor‐based technology, inertial and activity monitoring, and instrumented gait analysis (IGA) were employed to define motor abnormalities in Angelman syndrome. Temporal–spatial gait parameters of persons with Angelman Syndrome (pwAS) show deficiencies in gait performance through walking speed, step length, step width, and walk ratio. pwAS walk with reduced step lengths, increased step width, and greater variability. Three‐dimensional motion kinematics showed increased anterior pelvic tilt, hip flexion, and knee flexion. PwAS have a walk ratio more than two standard deviations below controls. Dynamic electromyography showed prolonged activation of knee extensors, which was associated with a decreased range of motion and the presence of hip flexion contractures. Use of multiple gait tracking modalities revealed that pwAS exhibit a change in gait pattern to a flexed knee gait pattern. Cross‐sectional studies of individuals with AS show a regression toward this maladaptive gait pattern over development in pwAS ages 4‐11. PwAS unexpectedly did not have spasticity associated with change in gait pattern. Multiple quantitative measures of motor patterning may offer early biomarkers of gait decline consistent with critical periods of intervention, insight into appropriate management strategies, objective primary outcomes, and early indicators of adverse events.

Qualitative Insights into Key Angelman Syndrome Motor Related Concepts Reported by Caregivers—A Thematic Analysis of Semi-Structured Interviews

“…Digital outcome measures attracted attention in the neuromuscular diseases field, with one outcome from a wearable device, SV95C, recently approved by the EMA as an outcome measure in clinical trials for Duchenne’s Muscular Dystrophy [ 14 , 15 ]. A recent study using pressure-sensor-based technology, inertial and activity monitoring, and instrumented gait analysis (IGA) in children with AS demonstrated that wearable technologies could identify differences in gait compared to neurotypical children, and could capture gait decline in children with AS over the course of the disease [ 24 ]. This suggests that wearable measures of gait could provide an objective outcome measure for this disease.…”