Systematic review of outcome measures of walking training using electromechanical and robotic devices in patients with stroke
Christian Geroin, Stefano Mazzoleni, Nicola Smania, Marialuisa Gandolfi, Donatella Bonaiuti, Giulio Gasperini, Patrizio Sale, Daniele Munari, Andreas Waldner, Raffaele Spidalieri, Federica Bovolenta, Alessandro Picelli, Federico Posteraro, Franco Molteni, Marco Franceschini, Italian Robotic Neurorehabilitation Research Group
Neuromotor and Cognitive Rehabilitation Research Centre (CRRNC), Department of Neurological and Movement
Sciences, University of Verona, 37134 Verona, Italy: E-mail: firstname.lastname@example.org
Objective: The aim of this systematic review was to identify appropriate selection criteria of clinical scales for future trials, starting from those most commonly reported in the literature, according to their psychometric properties and International Classification of Functioning, Disability and Health (ICF) domains.
Data sources: A computerized literature research of articles was conducted in MEDLINE, EMBASE, CINALH, PubMed, PsychINFO and Scopus databases.
Study selection: Clinical trials evaluating the effects of electromechanical and robot-assisted gait training trials in stroke survivors.
Data extraction: Fifteen independent authors performed an extensive literature review.
Data synthesis: A total of 45 scales was identified from 27 studies involving 966 subjects. The most commonly used outcome measures were: Functional Ambulation Category (18 studies), 10-Meter Walking Test (13 studies), Motricity Index (12 studies), 6-Minute Walking Test (11 studies), Rivermead Mobility Index (8 studies) and Berg Balance Scale (8 studies). According to the ICF domains 1 outcome measure was categorized into Body Function and Structure, 5 into Activity and none into Participation.
Conclusion: The most commonly used scales evaluated the basic components of walking. Future studies should also include instrumental evaluation. Criteria for scale selection should be based on the ICF framework, psychometric properties and patient characteristics.