![]() Unfortunately, there remains considerable disagreement regarding foot posture categorisation as several techniques have been reported in the literature, including visual observation, footprint parameters, measurement of frontal plane heel position, assessment of the position of the navicular tuberosity and a range of angular measurements obtained from foot radiographs. Measurement of foot posture is widely considered to be an important component of musculoskeletal examination in clinical practice and research, as variations in foot posture have been found to influence lower limb gait kinematics, muscle activity, balance and functional ability, and predisposition to overuse injury. Conclusionsįoot posture can be quickly and reliably categorised as high, normal or low in older people using a simplified visual categorisation tool based on the AI. ![]() There was a slight tendency for examiners to categorise participants as having higher arches than their AI scores indicated. Examiner’s scores were strongly correlated with actual AI values (Spearman’s rho = 0.91 to 0.94 and significant differences between all categories with ANOVA p < 0.001) and AI categories (percentage agreement = 95 to 98% Spearman’s rho = 0.89 to 0.94, and weighted kappas = 0.87 to 0.94). Inter- and intra-tester reliability of the examiners was almost perfect (percentage agreement = 93 to 97% Spearman’s rho = 0.91 to 0.95, and weighted kappas = 0.85 to 0.93). The validity of the examiner’s assessments was evaluated by comparing their categorisations to the actual AI score using Spearman’s rho and analysis of variance (ANOVA), and to the actual AI category using percentage agreement, Spearman’s rho and weighted kappa. ![]() Inter- and intra-tester reliability was determined using Spearman’s rho, percentage agreement and weighted kappa statistics. Two examiners were then asked to use the tool to independently grade the footprints of 60 participants (20 for each of the three categories, randomly presented), and then repeat the process two weeks later. A visual tool was then created using representative examples for the boundaries of each category. normal = +/− 1 standard deviation from the mean). Methodsįully weightbearing footprints from 602 people aged 62 to 96 years were obtained using a carbon paper imprint material, and cut-off AI scores dividing participants into three categories (high, normal and low) were determined using the central limit theorem (i.e. The objective of this study was therefore to develop and evaluate a simple visual tool for foot posture assessment based on the Arch Index (AI) that could be used in clinical and research settings. However, many measurement approaches are not suitable for routine use as they are time-consuming or require specialised equipment and/or clinical expertise. Foot posture is considered to be an important component of musculoskeletal assessment in clinical practice and research.
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