Looking Beyond Walking Amount in a Large Sample Analysis to Describe Real-World Activity Patterns in Older Adults

Background Digital mobility outcomes (DMOs) measured under real-world conditions allow for differentiated insights into mobility patterns of older adults. This study provides an advanced description of real-world walking in community-dwelling older adults and identifies correlates of walking activity and speed. Methods This study used baseline data from the SMART-AGE intervention trial with community-dwelling older adults (age ≥ 67 years). Assessment procedures included one-week monitoring of real-world walking using a wearable device (Axivity AX6), clinical outcomes, and tablet-based questionnaires. DMOs describing the amount (steps, walking duration), pattern (e.g., number of walking bouts [WBs]) and pace (mean and 90th percentile [P90] walking speed) of real-world walking were extracted using a validated computational pipeline. Data were included with ≥ 12 h/day wear time (07:00–22:00) on ≥ 3 days. Stepwise hierarchical linear mixed modelling examined associations between DMOs and socio-demographic indicators, environmental conditions, health status, locomotor capacity, fall-related concerns and cognitive function. Results The final sample of 569 participants with a mean age of 75.0 ± 5.6 years (52% women; 56% higher education) showed a mean walking activity of 11 746 ± 5 446 steps/day, 432.3 ± 153.8 WBs / day in total, and a mean walking speed of 0.75 ± 0.13 m/s in longer (> 30s) WBs. Age-related declines were observed across all DMOs, showing non-linear trends. We found sex-specific patterns of walking activity, as women had more WBs in total but fewer longer (> 30s) WBs than men. Day-to-day variability in walking activity was evident, with lowest levels of walking activity on Sundays. Health status and locomotor capacity were consistent predictors of walking across all DMOs (β = |0.08–0.26|, p < 0.05). Conclusion This study provides highly granulated insights into age-related mobility decline, sex-specific activity patterns, and day-to-day variability in a highly active population of older adults. This method may enable the detection of critical developments in everyday mobility and inform the development and personalization of healthy mobility recommendations and preventive measures.