Development of a simple screening tool based on the 5-question geriatric locomotive function scale for locomotive syndrome

Abstract

Background: The optimal cut-off values of the 5-question Geriatric Locomotive Function Scale (GLFS-5) as a screening tool to identify Locomotive Syndrome (LS) diagnosed with the 25-question Geriatric Locomotive Function Scale (GLFS-25) has yet to be fully investigated. This study aimed to construct a simple screening tool, based on the GLFS-5, for the detection of LS-1, LS-2, and LS-3 diagnosed with the GLFS-25.

Methods: This research was approved by the institutional review board of Fukushima Medical University School of Medicine (No. 2907). A cross-sectional study of 1258 consecutive Japanese volunteers with a mean age of 76.0 ± 6.0 years who consecutively attended a basic health checkup was conducted. We excluded individuals of <65 years of age, individuals with comorbidities, and individuals who did not fully complete the GLFS-25. Subjects with a GLFS-25 total score of 0-6 points, 7-15 points, 16-23 points, and 24-100 points were diagnosed with non-LS, LS-1, LS-2, and LS-3, respectively. A conventional receiver-operating characteristic curve analysis was used to confirm the optimal cut-off values of the GLFS-5 total score and their sensitivity and specificity in the identification of LS-1, LS-2, and LS-3, with a preference for slightly higher sensitivity as the intended use of the tool is primarily for screening purposes.

Results: The diagnoses of the 1258 subjects were as follows: non-LS (n = 559), LS-1 (n = 396), LS-2 (n = 134), and LS-3 (n = 169). The mean GLFS-5 was 3.3 ± 3.5 points. The optimal cut-off values of the GLFS-5 total score for discriminating LS-1, LS-2, and LS-3 (as a screening tool) were 2 points (sensitivity 91.7% and specificity 77.8%), 4 points (sensitivity 95.7% and specificity 81.7%), and 6 points (sensitivity 92.9% and specificity 90.0%), respectively.

Conclusions: This simple screening tool based on GLFS-5 could help physicians and surgeons to easily and practically predict the severity of LS.