This article is part of the series Trends on Robot-Assisted Therapy, edited by Michelle J Johnson.

Methodology

Multivariate analysis of the Fugl-Meyer outcome measures assessing the effectiveness of GENTLE/S robot-mediated stroke therapy

Farshid Amirabdollahian^1* , Rui Loureiro^2* , Elizabeth Gradwell³ , Christine Collin⁴ , William Harwin^2* and Garth Johnson^5*

¹  Think Lab, The University of Salford, Maxwell Building, Salford, M5 4WT, UK

²  Department of Cybernetics, University of Reading, Reading, RG6 6AY, UK

³  Community Therapy Team Florence Desmond Day Hospital, Royal Surrey County Hospital, Guildford, Surrey, GU2 7XX, UK

⁴  Department of Neurorehabilitation, South Block Annexe, Royal Berkshire Hospital, London Road, Reading, RG1 5AN, UK

⁵  Centre for Rehabilitation and Engineering Studies, School of Mechanical and Systems Engineering, University of Newcastle upon Tyne, Newcastle, NE1 7RU, UK

author email corresponding author email* Contributed equally

Journal of NeuroEngineering and Rehabilitation 2007, 4:4doi:10.1186/1743-0003-4-4

Published:	19 February 2007

Abstract

Background

Robot-mediated therapies offer entirely new approaches to neurorehabilitation. In this paper we present the results obtained from trialling the GENTLE/S neurorehabilitation system assessed using the upper limb section of the Fugl-Meyer (FM) outcome measure.

Methods

We demonstrate the design of our clinical trial and its results analysed using a novel statistical approach based on a multivariate analytical model. This paper provides the rational for using multivariate models in robot-mediated clinical trials and draws conclusions from the clinical data gathered during the GENTLE/S study.

Results

The FM outcome measures recorded during the baseline (8 sessions), robot-mediated therapy (9 sessions) and sling-suspension (9 sessions) was analysed using a multiple regression model. The results indicate positive but modest recovery trends favouring both interventions used in GENTLE/S clinical trial. The modest recovery shown occurred at a time late after stroke when changes are not clinically anticipated.

Conclusion

This study has applied a new method for analysing clinical data obtained from rehabilitation robotics studies. While the data obtained during the clinical trial is of multivariate nature, having multipoint and progressive nature, the multiple regression model used showed great potential for drawing conclusions from this study.

An important conclusion to draw from this paper is that this study has shown that the intervention and control phase both caused changes over a period of 9 sessions in comparison to the baseline. This might indicate that use of new challenging and motivational therapies can influence the outcome of therapies at a point when clinical changes are not expected.

Further work is required to investigate the effects arising from early intervention, longer exposure and intensity of the therapies. Finally, more function-oriented robot-mediated therapies or sling-suspension therapies are needed to clarify the effects resulting from each intervention for stroke recovery.