Reliability and validity of digital health metrics for assessing arm and hand impairments in an ataxic disorder.


Journal

Annals of clinical and translational neurology
ISSN: 2328-9503
Titre abrégé: Ann Clin Transl Neurol
Pays: United States
ID NLM: 101623278

Informations de publication

Date de publication:
04 2022
Historique:
revised: 01 12 2021
received: 25 10 2021
accepted: 08 12 2021
pubmed: 1 3 2022
medline: 13 4 2022
entrez: 28 2 2022
Statut: ppublish

Résumé

Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is the second most frequent recessive ataxia and commonly features reduced upper limb coordination. Sensitive outcome measures of upper limb coordination are essential to track disease progression and the effect of interventions. However, available clinical assessments are insufficient to capture behavioral variability and detailed aspects of motor control. While digital health metrics extracted from technology-aided assessments promise more fine-grained outcome measures, these have not been validated in ARSACS. Thus, the aim was to document the metrological properties of metrics from a technology-aided assessment of arm and hand function in ARSACS. We relied on the Virtual Peg Insertion Test (VPIT) and used a previously established core set of 10 digital health metrics describing upper limb movement and grip force patterns during a pick-and-place task. We evaluated reliability, measurement error, and learning effects in 23 participants with ARSACS performing three repeated assessment sessions. In addition, we documented concurrent validity in 57 participants with ARSACS performing one session. Eight metrics had excellent test-retest reliability (intraclass correlation coefficient 0.89 ± 0.08), five low measurement error (smallest real difference % 25.4 ± 5.7), and none strong learning effects (systematic change η -0.11 ± 2.5). Significant correlations (ρ 0.39 ± 0.13) with clinical scales describing gross and fine dexterity and lower limb coordination were observed. This establishes eight digital health metrics as valid and robust endpoints for cross-sectional studies and five metrics as potentially sensitive endpoints for longitudinal studies in ARSACS, thereby promising novel insights into upper limb sensorimotor control.

Identifiants

pubmed: 35224896
doi: 10.1002/acn3.51493
pmc: PMC8994987
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

432-443

Subventions

Organisme : CIHR
ID : TR2-119189
Pays : Canada

Informations de copyright

© 2022 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.

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Auteurs

Christoph M Kanzler (CM)

Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland.
Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence And Technological Enterprise (CREATE), Singapore.

Isabelle Lessard (I)

Groupe de Recherche Interdisciplinaire sur les Maladies Neuromusculaires (GRIMN), Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay-Lac-St-Jean, Saguenay, Quebec, Canada.

Roger Gassert (R)

Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland.
Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence And Technological Enterprise (CREATE), Singapore.

Bernard Brais (B)

The Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.

Cynthia Gagnon (C)

Groupe de Recherche Interdisciplinaire sur les Maladies Neuromusculaires (GRIMN), Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay-Lac-St-Jean, Saguenay, Quebec, Canada.
Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Quebec, Canada.

Olivier Lambercy (O)

Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland.
Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence And Technological Enterprise (CREATE), Singapore.

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