Automatic setting optimization for robotic upper-extremity rehabilitation in patients with stroke using ReoGo-J: a cross-sectional clinical trial - Takashi Takebayashi; Yuki Uchiyama; Kazuhisa Domen (Scientific Reports)
Abstract Stroke-induced upper-extremity paralysis affects a substantial portion of the population, necessitating effective rehabilitation strategies. This study aimed to develop an automated program, incorporating the item response theory, for rehabilitation in patients with post-stroke upper-extremity paralysis, focusing on the ReoGo-J device, and to identify suitable robot parameters for robotic rehabilitation. ReoGo-J, a training device for upper-extremity disorders including 71 items, was administered to over 300 patients with varying degrees of post-stroke upper-extremity paralysis. Each item was rated on a three-point scale (0, very difficult; 1, adequate; 2, very easy). The results were analyzed using the graded response model, an extension of the two-parameter logistic model within the framework of the item response theory, to grade the training items based on ability of the patients. The relationship between the predicted ability, an indicator of the predicted ability the paralyzed upper-extremity to perform an item in the item response theory analysis (higher numbers indicate higher ability, lower numbers indicate lower ability), and the items in the Fugl–Meyer assessment (FMA), which indicate the degree of paralysis, was analyzed using Pearson’s correlation coefficient. This study included 312 patients with post-stroke upper-extremity paralysis. The predicted ability (θ) of the tasks included in the original ReoGo-J test for forward reaching, reaching for mouth, rotational reaching, radial reaching (2D), abduction reaching, reaching in eight directions, radial reach (3D), and reaching for head ranged from − 2.0 to − 0.8, − 1.3 to − 0.8, − 1.0 to − 0.1, − 0.7 to 0.3, − 0.2 to 0.4, − 0.4 to 0.6, − 0.1 to 0.6, and 0.5 to 0.6, respectively. Significantly high correlations (r = 0.80) were observed between the predicted ability of all patients and the upper-extremity items of shoulder-elbow-forearm in the FMA. We have introduced an automated program based on item response theory and determined the order of difficulty of the 71 training items in ReoGo-J. The strong correlation between the predicted ability and the shoulder-elbow-forearm items in FMA may be used to ameliorate post-stroke upper-extremity paralysis. Notably, the program allows for estimation of appropriate ReoGo-J tasks, enhancing clinical efficiency. Trial registration: https://www.umin.ac.jp/ctr/index-j.htm (UMIN000040127).