BACKGROUND: Steps per day can provide a lot of information about the activity of the average person whose main source of activity is derived from walking. This study looks at the distribution of step-count data to identify different subgroups of people which could be used to indicate walking reserve. METHODS: A time series design of a secondary data analysis was conducted to track the variability of daily step count for 44 seniors post-fracture. The mean age was 75.8 years (SD: 9.75). The full percentile distribution was used in a cluster analysis and group-based trajectory analysis was used for the longitudinal data. Ordinal regression was used to identify factors associated with cluster membership. RESULTS: Four clusters best represented the distribution of reserve in this sample, hypothesized to be defined as the difference between the median and 90 percentile of the step-count distribution. Cluster 1, with the lowest reserve would also be classified as sedentary based on median step count (1,555 step count; 1,314 reserve). Cluster 2 represented people with limited activity with low reserve (4,081 step count; 2,439 reserve). Cluster 3 represented active people with high reserve (7,197 step count; 4,370 reserve). Cluster 4, was very active with very high reserve (9,202 step count, 6,964 reserve).The factors associated with cluster membership were gait speed, sit-to-stand, and depression. CONCLUSIONS: The median and 90 percentile over a longer period indicates the potential "reserve" for participating in activities that demand additional walking.