Literature DB >> 25893509

Validation of a Quantitative Single-Subject Based Evaluation for Rehabilitation-Induced Improvement Assessment.

Marta Gandolla1, Franco Molteni2, Nick S Ward3, Eleonora Guanziroli2, Giancarlo Ferrigno4, Alessandra Pedrocchi4.   

Abstract

The foreseen outcome of a rehabilitation treatment is a stable improvement on the functional outcomes, which can be longitudinally assessed through multiple measures to help clinicians in functional evaluation. In this study, we propose an automatic comprehensive method of combining multiple measures in order to assess a functional improvement. As test-bed, a functional electrical stimulation based treatment for foot drop correction performed with chronic post-stroke participants is presented. Patients were assessed on five relevant outcome measures before, after intervention, and at a follow-up time-point. A novel algorithm based on variables minimum detectable change is proposed and implemented in a custom-made software, combining the outcome measures to obtain a unique parameter: capacity score. The difference between capacity scores at different timing is three holded to obtain improvement evaluation. Ten clinicians evaluated patients on the Improvement Clinical Global Impression scale. Eleven patients underwent the treatment, and five resulted to achieve a stable functional improvement, as assessed by the proposed algorithm. A statistically significant agreement between intra-clinicians and algorithm-clinicians evaluations was demonstrated. The proposed method evaluates functional improvement on a single-subject yes/no base by merging different measures (e.g., kinematic, muscular) and it is validated against clinical evaluation.

Entities:  

Keywords:  Algorithm for Clinical Global Impression scale for Improvement; Capacity score; Functional electrical stimulation (FES); Functional improvement; Therapeutic effect

Mesh:

Year:  2015        PMID: 25893509     DOI: 10.1007/s10439-015-1317-4

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  5 in total

1.  Robotic Exoskeleton Gait Training in Stroke: An Electromyography-Based Evaluation.

Authors:  Valeria Longatelli; Alessandra Pedrocchi; Eleonora Guanziroli; Franco Molteni; Marta Gandolla
Journal:  Front Neurorobot       Date:  2021-11-26       Impact factor: 2.650

2.  The Neural Correlates of Long-Term Carryover following Functional Electrical Stimulation for Stroke.

Authors:  Marta Gandolla; Nick S Ward; Franco Molteni; Eleonora Guanziroli; Giancarlo Ferrigno; Alessandra Pedrocchi
Journal:  Neural Plast       Date:  2016-03-17       Impact factor: 3.599

3.  Artificial neural network EMG classifier for functional hand grasp movements prediction.

Authors:  Marta Gandolla; Simona Ferrante; Giancarlo Ferrigno; Davide Baldassini; Franco Molteni; Eleonora Guanziroli; Michele Cotti Cottini; Carlo Seneci; Alessandra Pedrocchi
Journal:  J Int Med Res       Date:  2016-09-27       Impact factor: 1.671

4.  Automatic Setting Procedure for Exoskeleton-Assisted Overground Gait: Proof of Concept on Stroke Population.

Authors:  Marta Gandolla; Eleonora Guanziroli; Andrea D'Angelo; Giovanni Cannaviello; Franco Molteni; Alessandra Pedrocchi
Journal:  Front Neurorobot       Date:  2018-03-19       Impact factor: 2.650

5.  Brain Plasticity Mechanisms Underlying Motor Control Reorganization: Pilot Longitudinal Study on Post-Stroke Subjects.

Authors:  Marta Gandolla; Lorenzo Niero; Franco Molteni; Elenora Guanziroli; Nick S Ward; Alessandra Pedrocchi
Journal:  Brain Sci       Date:  2021-03-05
  5 in total

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