Gait Variability as a Potential Motor Marker of Cerebellar Disease-Relationship between Variability of Stride, Arm Swing and Trunk Movements, and Walking Speed.
Daniel KronebergAstrid NümannMartina MinneropMaria RönnefarthMatthias EndresAndrea A KühnFriedemann PaulSarah DossSusanne SolbrigMorad ElshehabiWalter MaetzlerTanja Schmitz-HübschPublished in: Sensors (Basel, Switzerland) (2024)
Excessive stride variability is a characteristic feature of cerebellar ataxias, even in pre-ataxic or prodromal disease stages. This study explores the relation of variability of arm swing and trunk deflection in relationship to stride length and gait speed in previously described cohorts of cerebellar disease and healthy elderly: we examined 10 patients with spinocerebellar ataxia type 14 (SCA), 12 patients with essential tremor (ET), and 67 healthy elderly (HE). Using inertial sensors, recordings of gait performance were conducted at different subjective walking speeds to delineate gait parameters and respective coefficients of variability (CoV). Comparisons across cohorts and walking speed categories revealed slower stride velocities in SCA and ET patients compared to HE, which was paralleled by reduced arm swing range of motion (RoM), peak velocity, and increased CoV of stride length, while no group differences were found for trunk deflections and their variability. Larger arm swing RoM, peak velocity, and stride length were predicted by higher gait velocity in all cohorts. Lower gait velocity predicted higher CoV values of trunk sagittal and horizontal deflections, as well as arm swing and stride length in ET and SCA patients, but not in HE. These findings highlight the role of arm movements in ataxic gait and the impact of gait velocity on variability, which are essential for defining disease manifestation and disease-related changes in longitudinal observations.
Keyphrases
- cerebral palsy
- end stage renal disease
- lower limb
- ejection fraction
- newly diagnosed
- sars cov
- blood flow
- chronic kidney disease
- prognostic factors
- peritoneal dialysis
- machine learning
- respiratory syndrome coronavirus
- middle aged
- cross sectional
- patient reported outcomes
- body mass index
- deep learning
- early onset
- deep brain stimulation
- parkinson disease
- climate change
- sleep quality
- coronavirus disease
- neural network