Accepted for/Published in: JMIR Serious Games
Date Submitted: May 16, 2021
Date Accepted: Dec 3, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Towards the optimal virtual reality exergame approach for balance therapy in persons with neurological disorders: a Rasch analysis
ABSTRACT
Background:
Virtual reality exergames have gained popularity in the rehabilitation of persons with neurological disorders as an add-on therapy to increase intensity of training. Intensity is strongly dependent on the motivation of the patient. Motivation can be increased by delivering variation within training and challenging exercises. However, patients are often underchallenged as exergame difficulty often does not match the patient’s ability. A Rasch analysis can establish hierarchy of exergame items in order to assist the delivery of patient-centred therapy.
Objective:
To apply the Rasch model creating a hierarchical order of existing virtual reality balance exergames and to relate these exergames to the abilities of persons with neurological disorders, in order to deliver challenge and variation.
Methods:
30 persons with stroke and 51 persons with multiple sclerosis were included in the study. All participants participated in a three-to-four week training program, in which they performed virtual reality balance exergames with a movement recognition-based system (MindMotion™ GO, MindMaze SA, Switzerland). Virtual reality exercise scores, Berg balance scale scores and clinical descriptive data were collected. Berg balance scale and device scores were analysed with the Rasch model using a repeated measure approach to examine if the distribution of the exercise scores fitted the Rasch model. Secondly, a person-item map was created to show the hierarchy of the exercise difficulty and person ability.
Results:
Participants played a selection of fifty-six balance exercises (items), which consisted of a combination of various balance tasks and levels (i.e. exercises). Using repeated measures this resulted in a count of 785 observations. Analysis showed strong evidence for unidimensionality of the data. Forty-seven exercises (items) had a sufficiently good fit to the Rasch model. Six items had underfit with infit mean-square values above 1.5. One item had underfit but was kept in the analysis. Three items had negative point-biserial correlations. The final model consisted of forty-seven exercises providing exercises for persons with low to moderate balance ability.
Conclusions:
The virtual reality exercises sufficiently fitted the Rasch model and resulted in a hierarchical order of virtual reality balance exercises for persons with stroke and with multiple sclerosis with low to moderate balance ability. In combination with the Berg balance scale, the results can guide clinical decision-making in the selection of patient-focused effective virtual reality balance exercises. Clinical Trial: NCT03993275
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