Development of a program to determine optimal settings for robotic-assisted rehabilitation of the post-stroke paretic upper extremity: a simulation study

Background: Robot-assisted therapy can effectively treat upper extremity (UE) paralysis in patients who experience stroke. Presently, UE, as a training item, is selected according to the severity of the paralysis, which is based on a clinician’s experience. The possibility of objectively selecting robot-assisted training items based on the severity of paralysis was simulated using the item response theory (IRT). Methods: : Sample data were generated using Monte Carlo method with 300 random cases. In this simulation, sample data (categorical data with three difficulty values of 0, 1, and 2 [0: too easy, 1: adequate, and 2: too difficult]) with 71 items per case were analyzed. First, the most appropriate method was selected to ensure local independence of the sample data necessary to use IRT. The method was to exclude: items with low response probability (maximum response probability) within a pair in the Quality of Compensatory Movement Score (QCM) 1-point item difficulty curve, items with low item information content within a pair in the QCM 1-point item difficulty curve, and items with low item discrimination. Second, 300 cases were analyzed to determine the most appropriate model (one-parameter or two-parameter item response therapy) to be used and the most favored method to establish local independence. We also examined whether robotic training items could be selected according to the severity of paralysis based on the ability of the person (θ) in the sample data as calculated by IRT. Results: : To ensure local independence, excluding items with low response probability (maximum response probability) in a pair in the categorical data 1-point item difficulty curve was effective.Additionally, to ensure local independence, the number of items should be reduced to 61 from 71, indicating that the two-parameter model IRT was an appropriate model. The ability of the person (θ) calculated by IRT suggested that seven training items could be estimated from 300 cases according to severity. Conclusion: From this simulation, it seemed possible to objectively estimate the training items according to the severity of paralysis in a sample of approximately 300 cases using this model.