Dual-task (DT) walking is of great interest in clinical evaluation to evaluate the risk of falling or cognitive declines in older adults. However, it appears necessary to investigate deeply the confounding factors to better understand their impact on dual-task performance. To evaluate the effect of age, sex, falls and frailty on cognitive and motor parameters in dual-task walking. 66 older participants (mean age = 75.5 ± 6.3; mean height = 165.8 ± 8.4 cm; mean weight = 68.4 ± 14 kgs) were split into groups based on their age, sex, fall and frailty status. Participants performed single-task walking, single-task cognitive (serial subtraction of 3), and dual-task walking (subtraction + walking) for 1 min at their fast pace. Gait speed, step length, step length variability, stance and swing phase time, single and double support, cadence, step time variability and gait speed variability were recorded in single- and dual-task walking and used to calculate the dual-task effect (DTE) as ((DT - ST) / ST) ∗ 100). The cognitive score (DTEcog) was calculated as the number of correct responses minus errors. Generalized linear mixed models (GLMM) were used to compare the effects of falls, frailty, age and sex on gait and cognitive variables. The interaction frailty*sex and frailty*age were the major effect on the DTEs. Specifically, the DTE was higher in women than men and in the frail group compared to non-frail. The present findings provide a better understanding on the confounding factors explaining the behavior in DT that could be used to develop more effective dual-task clinical programs for community-living older adults.

Gait analysis is essential for evaluating walking patterns and identifying functional limitations. Traditional marker-based motion capture tools are costly, time-consuming, and require skilled operators. This study evaluated a 3D Marker-less Motion Capture (3D MMC) system using pose and depth estimations with the gold-standard Motion Capture (MOCAP) system for measuring hip and knee joint angles during gait at three speeds (0.7, 1.0, 1.3 m/s). Fifteen healthy participants performed gait tasks which were captured by both systems. The 3D MMC system demonstrated good accuracy (LCC > 0.96) and excellent inter-session reliability (RMSE < 3°). However, moderate-to-high accuracy with constant biases was observed during specific gait events, due to differences in sample rates and kinematic methods. Limitations include the use of only healthy participants and limited key points in the pose estimation model. The 3D MMC system shows potential as a reliable tool for gait analysis, offering enhanced usability for clinical and research applications.