Abstract
Great toe strength (GTS) weakness is linked to declines in balance and mobility. Accurately assessing GTS, particularly great toe extension strength (GTES), is often neglected in clinical evaluations due to cumbersome and subjective methods. This study aims to characterize the force development curve output from the ToeScale and examine GTES variations with age, sex, BMI, and grip strength (GS) using traditional analyses and machine learning (ML). We conducted a pilot, cross-sectional feasibility study with convenience samples. We assessed GS using a hand-grip dynamometer and GTES using the ToeScale. The data analysis included descriptive statistics, correlations, independent samples t-tests, and accuracy and area under the curve (AUC) scores for three ML models. Thirty-one participants (males: 9; females: 22), 14 young (18-24 years) and 17 older (>65 years) adults, participated in the study. Males had significantly higher peak GTES than females in both age groups. The associations of GTES parameters with BMI and GS varied by age and sex. The ML model accuracies and AUC scores were low-moderate but aligned with traditional analyses. Future studies with larger samples and optimized ML models are needed.