The objective of this analysis was to apply Symbolic Aggregate approXimation (SAX) time-series analysis to accelerometer data for activity pattern visualization stratified by self-reported mobility difficulty. A total of 2,393 (71.6 ± 7.9 years old) participants wore an accelerometer on the hip (4+ days; 10+ hours) during the National Health and Nutrition Examination Survey (NHANES), a biannual series of health assessments of the US population. One minute epoch data was used to perform SAX, which converted accelerometry time series data into four activity levels. Intelligent icons of normalized activity transition prevalence, a visual representation of time-series data, were examined among those who self-reported mobility difficulty. Mobility difficulty questions assessed various levels difficulty performing activities such as walking a quarter mile, walking up ten steps, stooping/crouching/kneeling, and walking between rooms on the same floor. Daily activity counts were estimated across difficulty level using weighted-linear regression after adjusted for demographics, lifestyle factors, medical conditions, and accelerometer wear time. Those reporting higher mobility difficulty tended to be older, female, less educated, not married, and smokers. Additionally, those with higher mobility difficulty self-reported lower education, lower income, lower moderate-to-vigorous physical activity, and higher history of adverse medical conditions. Using SAX-derived intelligent icons, those with no difficulty showed high variations of transitions across all activity levels. With higher difficulty, the variations in transitions were lower and constricted around low activity level transitions. Among those who reported unable/don’t do mobility-related activities, there were only transitions in the lower tiered activity levels with high prevalence of prolonged low level activity. Adjusted estimates of daily activity were lower as higher difficulty reported occurred but only significant for those reporting unable/don’t do mobility-related activity (p<0.01). In summary, this analysis showed apparent differences in stratified activity patterns even when traditional regression analyses on volumetric accelerometer data yielded null results.