Objectives: We assessed the difference between quiet stance and gait in the spatial distribution and intensity of foot plantar pressures and whether it is possible to estimate the distribution during gait from data obtained during stance. Methods: A total of 60 healthy subjects with a mean age of 31.0 ± 9.4 years performed two trials for quiet stance and four trials for gait on a baropodometric walkway with their eyes open. Foot plantar pressures were recorded from 10 areas of the foot sole. Results: During quiet stance, the highest plantar pressure occurred at metatarsal heads (M2 to M4) and the medial (MH) and lateral halves of the heel (LH). During gait, the profile of plantar pressure values was like that during stance, but significantly higher. The differences concentrated at the big toe (T1), M2 to M4, MH, and LH, whilst toes (T2,3,4,5) and midfoot (MF) showed the smallest difference. A significant positive correlation was found between the corresponding areas of foot pressure during gait and stance. Conclusions: During quiet stance and gait, the overall profile of plantar pressure distribution was similar. During quiet stance, the subjects loaded more on the heels, in keeping with the known position of the center of pressure just in front of the ankles. During gait, higher pressures on the metatarsal areas are related to the forward propulsion of the center of mass. The correlation between the corresponding areas of foot pressure during gait and stance suggests that the pressure distribution during gait can partly be estimated from that during stance. This finding might be useful in most clinical settings when a single sensorized platform rather than a complete walkway is available.

BackgroundFalls are a serious concern in Parkinson’s disease (PD), often leading to hospitalisation, dependence and reduced quality of life. Effective fall management requires identification of those at risk. This study aimed to evaluate which selection of outcomes best discriminate fallers from non-fallers.MethodsPeople with PD were recruited (ICICLE-GAIT; 54- and 72-month follow up assessments). Fallers and non-fallers were stratified based on prospective fall reports. A total of 299 outcomes across 4 domains were collected: clinical (n=9), lab-based mobility (gait n=60, turning n=99), real-world mobility (n=131). Receiver operating characteristic analysis evaluated classification models distinguishing fallers from non-fallers. Area under the curve (AUC) determined which models were optimal. Models were re-evaluated at 72-months.ResultsOf the 48 participants, 32 (67%) were classified as fallers and 16 (33%) as non-fallers. Significant group differences (faller vs. non-faller) were found in all domains at 54-months; clinical (n=2/9), lab-based gait (n=4/60), lab-based turning (n=19/99) and real-world mobility (n=5/131). At 54-months, turning was the strongest single-domain model (AUC=0.89; sensitivity=0.74, specificity=0.93), followed by real-world mobility (AUC=0.82; sensitivity=0.68, specificity=1.00). All multi-domain combinations including turning achieved perfect discrimination (AUC=1.00). At 72-months, turning alone retained perfect accuracy (AUC=1.00), whereas clinical (AUC=0.52), gait (AUC=0.51) and real-world (AUC=0.69) models declined substantially.ConclusionTurning showed superior discriminative power in identifying PD fallers remaining robust over time and outperforming other assessments. Real-world mobility also had strong discriminative value, highlighting the importance of ecologically valid continuous monitoring. Future models should explore whether real-world turning provides superior discriminative value to lab-based turning.