Journal of musculoskeletal & neuronal interactions · 2026
Increased Gait Variability while Texting and Walking with Added Cognitive Loads in Challenging Environments.
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Choi Chi-Whan, Gill Simone V
Аннотация
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.
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.