Autocorrelation-based gait regularity reveals progressive changes during prolonged walking

Gait regularity reflects the consistency of locomotor patterns. During prolonged load carriage, such as military marches, gait patterns may change due to accumulating fatigue, pain, and environmental demands. However, the progression of gait regularity and its determinants during extended real-world walking remain insufficiently understood.

This observational field study examined gait regularity during a 90 km loaded military endurance march in Swiss cadets. Triaxial acceleration was continuously recorded from the upper arm using a Polar Verity Sense. Gait regularity was quantified using step-to-step autocorrelation of acceleration magnitude. Data were split into five consecutive 18 km segments and analyzed using repeated-measures ANOVA with Greenhouse-Geisser correction and Holm-adjusted post hoc tests. Linear mixed-effects models assessed associations of gait regularity with accumulated distance, walking speed, and terrain slope.

Nineteen cadets (21.4 ± 1.6 years) were included. Mean gait regularity decreased progressively across march segments. A significant effect of segment was observed (p < 0.001, η_p² = 0.655), with nine of ten significant pairwise comparisons (p < 0.05), and eight large effect sizes (|d| > 0.8). Mixed-effects analyses showed gait regularity was associated with all fixed effects (all p < 0.001). Distance and speed accounted for 32.3% of the variance in gait regularity, while terrain slope contributed only minimally.

Gait regularity decreased systematically during an ultra-long-distance endurance march under real-world conditions. Autocorrelation-based analysis of arm-worn acceleration data captured progressive changes in locomotor patterns. These findings support wearable-based monitoring of locomotor adaptations during prolonged load carriage and future fatigue and injury risk assessment.