A Resource-Efficient, High-Dose, Gamified Neurorehabilitation Program for Chronic Stroke at Home: Retrospective Real-World Analysis

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Abstract

Background: Accumulating evidence and medical guidelines recommend high-dose neurorehabilitation for recovery after stroke. The reality, however, is that most patients receive a fraction of this dose, with therapist availability and costs of delivery being major implementational barriers. Objective: This study aimed to explore a potential solution by conducting a retrospective analysis of a real-world enhanced clinical service that used gamified self-training technologies at home under remote therapist supervision. Methods: Data from 17 patients who completed a 12‐18 week full-body, high-dose neurorehabilitation program entirely at home were analyzed. Program delivery relied primarily on patients training independently (asynchronously) with the MindMotion GO (MindMaze) gamified-therapy solution. Accompanying telerehabilitation training sessions with a therapist occurred weekly while therapists used a web application to continuously monitor and manage the program remotely. Effectiveness of the program was assessed through measured active training time, a measure that more closely reflects delivered dosage as opposed to scheduled dose. Patient recovery was evaluated with standardized impairment and functional clinical measures and patient self-reported outcome measures. Finally, a cost model was computed to evaluate the resource efficiency of the program. Results: Patients maintained high training adherence throughout the program and reached an average total active training time of 39.7 (SD 21.4) hours, with the majority delivered asynchronously (mean 82.2%, SD 10.8%). Patients improved in both upper-limb (Fugl-Meyer Upper Extremity, mean 6.4, SD 5.1; P

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Arbuckle, S. A., Knill, A. S., Chan-Cortés, M. H., Rozanski, G., Ford, A. E., Derungs, L. T., … Branscheidt, M. (2025). A Resource-Efficient, High-Dose, Gamified Neurorehabilitation Program for Chronic Stroke at Home: Retrospective Real-World Analysis. JMIR Serious Games, 13. https://doi.org/10.2196/69335

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