TITLE:
Impact of Specialist Assessment versus Paper-Based Prescription on the Suitability of Ankle-Foot Orthoses in Patients with Chronic Stroke: A Comparative Study Using a Prescription Algorithm
AUTHORS:
Kan Imai, Takeshi Onishi, Yasutaka Sotozono, Koshiro Sawada
KEYWORDS:
Ankle Foot Orthosis, Chronic Phase, Stroke, Orthosis Prescription, Algorithm
JOURNAL NAME:
Open Journal of Therapy and Rehabilitation,
Vol.14 No.2,
May
27,
2026
ABSTRACT: Introduction: In orthosis prescription for patients with chronic-phase stroke, balancing high-quality specialist assessment with geographical accessibility remains a global challenge. In Japan, two models currently coexist: one in which a specialized multidisciplinary team conducts face-to-face assessments (direct judgment) and another in which non-specialist physicians prescribe orthoses based solely on written documentation (document-based judgment). Concern exists that the latter may lead to prescriptions that deviate from algorithm-recommended physiological standards. This study aimed to compare the rate of agreement with a physiologically based evaluation algorithm between these two prescription models as a measure of potential suitability. Materials and Methods: Participants were 42 patients with cerebrovascular disease who could use an ankle-foot orthosis (AFO): 26 and 16 in the face-to-face assessment and document-based prescription groups, respectively. Using a five-item physical function assessment algorithm with previously established validity, we determined the “orthosis recommended based on physical status”. The primary outcome was the agreement rate between the algorithm-recommended device and the orthosis actually prescribed, which served as a benchmark for evaluating the prescription’s alignment with the patient’s physical status. Statistical analysis was performed using the chi-square test. Results: The agreement rate between the algorithm-recommended and actually prescribed orthoses was higher in the face-to-face assessment group (61.5%) than in the document-based prescription group (25.0%). Among cases in which the same orthosis was re-prescribed without modification, the agreement rate in the document-based prescription group was only 16.7%, indicating that changes in physical function were often overlooked. Conclusions: Document-based AFO prescription by non-specialists carries a high risk of mismatch with the patient’s physical condition. Even in settings with limited access to specialists, introducing an objective decision-support tool—such as the present algorithm—into clinical practice may help ensure orthosis prescription quality.