Definition of an insole comfort equation using biomechanics data from a real-world data collection method - Lewin, M.; Jones, R.; Price, C. (Footwear Science, 2025)
Comfort is among the priorities of wearers, this creates a requirement to understand and measure footwear comfort within the real-world where footwear is being used. Comfort is subjective however associations have been drawn to biomechanical parameters. Prediction of real-world footwear or insole comfort based upon biomechanical parameters is appealing due to the reduction of potential participant biases. This study aims to develop an equation to predict insole comfort from real-world biomechanics data. Five conditions were evaluated, a control (participant shoe only) and four commercially available insole conditions (Insole A, B, C, D). The RunScribe IMU was worn for one day per condition for the participants normal daily activity, measuring previously validated variables: vertical ground reaction force (GRF), vertical GRF loading rate (GRFr), impact shock (IS), braking shock, total shock, pronation excursion (PE), and maximum pronation velocity (PV). Comfort was measured using a 100 mm visual analogue scale (VAS). A mixed model with fixed effects was used to develop the comfort equation, pseudo r2 statistics were assessed to identify the best equation. All combinations of conditions were tested from single conditions to all 5 together. The following equation was defined using data from insole A and insole B: Comfort = 96.557 + (-0.456*GRFr) + (-11.757*IS) + (-2.869*PE) + (0.142*PV). Marginal pseudo r2 = 0.175 and conditional pseudo r2 = 0.675, meaning that 17.5% of the variance in comfort was explained by the biomechanics variables. Previous footwear comfort equations focused on different biomechanics variables including EMG, plantar pressure, loading rates and lower limb kinematics and reported larger explained variance (34.9%-71.4%). Additional variables would be required to improve the current equation; however, it provides insight into how comfort could be improved for usage within product development, as well as measuring comfort during testing.