Dystonia in Angelman syndrome: a common, unrecognized clinical finding

Ferlazzo, Edoardo; Ascoli, Michele; Abate, Francesca; Gasparini, Sara; Mastroianni, Giovanni; Cianci, Vittoria; Ferrigno, Giulia; Sueri, Chiara; D’Agostino, Tiziana; Aguglia, Umberto

doi:10.1007/s00415-020-10395-4

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…In children with AS, walking is typically delayed until 3 or 5 years (Bindels-De Heus et al 2020), followed by ataxic and irregular gait (Williams et al 2006). Other AS gait characteristics include crouch gait (Bindels-De Heus et al 2020), spastic diplegia (Dan et al 2001) and dystonia (Ferlazzo et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Feasibility of wearable technology for ‘real‐world’ gait analysis in children with Prader–Willi and Angelman syndromes

Kraan

Date

Rattray

et al. 2022

J intellect Disabil Res

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Background Prader–Willi syndrome (PWS) and Angelman syndrome (AS) are neurodevelopmental disorders in need of innovative ‘real‐world’ outcome measures to evaluate treatment effects. Instrumented gait analysis (IGA) using wearable technology offers a potentially feasible solution to measure “real‐world’ neurological and motor dysfunction in these groups. Methods Children (50% female; 6–16 years) diagnosed with PWS (n = 9) and AS (n = 5) completed ‘real‐world’ IGA assessments using the Physilog®5 wearable. PWS participants completed a laboratory assessment and a ‘real‐world’ long walk. The AS group completed ‘real‐world’ caregiver‐assisted assessments. Mean and variability results for stride time, cadence, stance percentage (%) and stride length were extracted and compared across three different data reduction protocols. Results The wearables approach was found to be feasible, with all participants able to complete at least one assessment. This study also demonstrated significant agreement, using Lin's concordance correlation coefficient (CCC), between laboratory and ‘real‐world’ assessments in the PWS group for mean stride length, mean stance % and stance % CV (n = 7, CCC: 0.782–0.847, P = 0.011–0.009). Conclusion ‘Real‐world’ gait analysis using the Physilog®5 wearable was feasible to efficiently assess neurological and motor dysfunction in children affected with PWS and AS.

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Section: Introductionmentioning

confidence: 99%