Association of Gait Quality With Daily-Life Mobility: An Actigraphy and Global Positioning System Based Analysis in Older Adults

Abstract

Objective: Walking is a key component of daily-life mobility. We examined associations between laboratory-measured gait quality and daily-life mobility through Actigraphy and Global Positioning System (GPS). We also assessed the relationship between two modalities of daily-life mobility i.e., Actigraphy and GPS.

Methods: In community-dwelling older adults (N = 121, age = 77±5 years, 70% female, 90% white), we obtained gait quality from a 4-m instrumented walkway (gait speed, walk-ratio, variability) and accelerometry during 6-Minute Walk (adaptability, similarity, smoothness, power, and regularity). Physical activity measures of step-count and intensity were captured from an Actigraph. Time out-of-home, vehicular time, activity-space, and circularity were quantified using GPS. Partial Spearman correlations between laboratory gait quality and daily-life mobility were calculated. Linear regression was used to model step-count as a function of gait quality. ANCOVA and Tukey analysis compared GPS measures across activity groups [high, medium, low] based on step-count. Age, BMI, and sex were used as covariates.

Results: Greater gait speed, adaptability, smoothness, power, and lower regularity were associated with higher step-counts (0.20<|ρ_p| < 0.26, p < .05). Age(β = -0.37), BMI(β = -0.30), speed(β = 0.14), adaptability(β = 0.20), and power(β = 0.18), explained 41.2% variance in step-count. Gait characteristics were not related to GPS measures. Participants with high (>4800 steps) compared to low activity (steps<3100) spent more time out-of-home (23 vs 15%), more vehicular travel (66 vs 38 minutes), and larger activity-space (5.18 vs 1.88 km²), all p < .05.

Conclusions: Gait quality beyond speed contributes to physical activity. Physical activity and GPS-derived measures capture distinct aspects of daily-life mobility. Wearable-derived measures should be considered in gait and mobility-related interventions.

Fig. 1.

Step-count modeled as a function of demographics and combination of gait quality measures using linear regression. (A) Baseline model included Age, Body Mass Index (BMI), and Gait Speed and explained 37.3% variance in step-count. (B) A model including Age, Body Mass Index (BMI), Gait Speed, Adaptability, and Power and explained the greatest (41.2%) variance in step-count, i.e. 4% more variance explained when including gait adaptability and power than the by baseline model.

Fig. 2.

Radar plots to visualize and compare multi-variate data for activity groups based on step-count. *Indicates significant difference of at least p<.1, between at least two groups (Tukey post hoc performed after ANCOVA) (A) Activity-space measures from Global Positioning System (GPS) – time spent outside home (TOH), vehicular time, standard deviation ellipse area, and standard deviation ellipse (SDE) circularity. The low activity group has lowest TOH, lowest vehicular time, and smallest area. All activity groups have similar circularity. Medium and high activity groups have similar SDE area. (B) Gait quality measures from instrumented walkway (gait speed, walk-ratio, and stride-time variability), and accelerometer (adaptability, similarity, smoothness, power, regularity). Low, medium, and high activity groups are distinct in gait adaptability and power. Medium and high activity groups have similar gait speed, walk-ratio, smoothness, and regularity. (C) Other physical (short physical performance battery-SPPB, Life Space Assessment), cognition (time taken to do Trails A and Trails B), and psychosocial (walking confidence, number of individuals with fear of falling). High and medium activity groups have similar SPPB scores and walking confidence but are distinct in cognitive abilities and self-reported mobility.