Reliability of a new method to determine foot arch height for clinicians

Background: Arch height is an important indicator of risk of foot pathology. The current non-invasive gold standard based on footprint information requires extensive preprocessing. Methods used to obtain arch height that are accurate and easier to use are required in routine clinical practice. Methods: The proposed arch index diagonals (AI_d) method for determining the arch index (AI) reduces the complexity of the preprocessing steps. All footprints were first prepared as required by the Cavanagh and Rodgers method for determining the AI and then compared to the proposed diagonals method. Results were classified according to the Cavanagh and Rodgers cut-off values into three groups of low, normal and high AI. ANOVA and Tukey’s post hoc tests were applied to identify significant differences between AI groups. Linear modeling was applied to determine the fit of the new AI_d method compared to the Cavanagh and Rodgers AI. Results: One hundred and ninety-six footprints were analyzed. The ANOVA indicated significant differences between the groups for AI_d (F1,194=94.49, p<0.0001) and the Tukey post hoc tests indicated significant differences between the pair-wise comparisons (p<0.001). Linear modeling indicated that the AI_d ratio classified more footprints in the high arch group compared to Cavanagh and Rodgers results (R²=32%, p< 0.01). Intra-and inter-rater correspondence was above 90% and confirmed that the AI_d results provided a better indication of arch height. Conclusions: The proposed method simplifies current processing steps to derive the arch height.