Increased Gait Variability while Texting and Walking with Added Cognitive Loads in Challenging Environments.

OBJECTIVES: This study investigated (1) whether additional cognitive loads impair dual-task walking, especially when crossing obstacles of varying heights, and (2) if extreme gradient boosting (XGBoost) could identify key features of walking while texting. METHODS: Twenty participants completed the following conditions: baseline walking, Task (walking + Typing: copying sentences, or Texting: answering questions), Obstacle (walking + crossing obstacles of three heights: low, medium, high), and combined (Task + Obstacle). For texting performance, we computed the rate, accuracy, and dual-task cost (DTC). Gait measures included mean spatiotemporal parameters, coefficient of variation (%CV) for each parameter, and mediolateral trunk acceleration. Four machine learning models were trained for evaluation: K-Nearest Neighbor, Support Vector Machines, and Random Forest, XGBoost. RESULTS: XGBoost achieved the best performance, with a test weighted F1 score of 0.848, and identified rate, %CV for step and stride time, and DTC of rate as the most important features for classification of conditions. Task × Obstacle interaction was found for %CV of step time with greater variability during high obstacle crossing while Texting (β=1.742, p=0.029). CONCLUSIONS: Findings demonstrate that mobile phone use increases walking variability, particularly in complex environments. The result may lend weight to growing concerns about mobile phone use while walking in young adults.