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Prediction of setup times for an advanced upper limb functional electrical stimulation system

INTRODUCTION: Rehabilitation devices take time to don, and longer or unpredictable setup time impacts on usage. This paper reports on the development of a model to predict setup time for upper limb functional electrical stimulation. METHODS: Participants’ level of impairment (Fugl Meyer-Upper Extrem...

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Detalles Bibliográficos
Autores principales: Smith, Christine, Kenney, Laurence, Howard, David, Waring, Karen, Sun, Minxgu, Luckie, Helen, Hardiker, Nicholas, Cotterill, Sarah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6531802/
https://www.ncbi.nlm.nih.gov/pubmed/31191957
http://dx.doi.org/10.1177/2055668318802561
Descripción
Sumario:INTRODUCTION: Rehabilitation devices take time to don, and longer or unpredictable setup time impacts on usage. This paper reports on the development of a model to predict setup time for upper limb functional electrical stimulation. METHODS: Participants’ level of impairment (Fugl Meyer-Upper Extremity Scale), function (Action Research Arm Test) and mental status (Mini Mental Scale) were measured. Setup times for each stage of the setup process and total setup times were recorded. A predictive model of setup time was devised using upper limb impairment and task complexity. RESULTS: Six participants with stroke were recruited, mean age 60 (±17) years and mean time since stroke 9.8 (±9.6) years. Mean Fugl Meyer-Upper Extremity score was 31.1 (±6), Action Research Arm Test 10.4 (±7.9) and Mini Mental Scale 26.1 (±2.7). Linear regression analysis showed that upper limb impairment and task complexity most effectively predicted setup time (51% as compared with 39%) (F(2,21) = 12.782, adjusted R(2) = 0.506; p < .05). CONCLUSIONS: A model to predict setup time based on upper limb impairment and task complexity accounted for 51% of the variation in setup time. Further studies are required to test the model in real-world settings and to identify other contributing factors.