Measuring movement through wearable accelerometers may decrease reliance on subjective tools commonly used in clinical research. Accelerometry also allows the study of movement performance over time during a person's typical activities of daily living. Different techniques can be used to analyze and reduce accelerometer data, which could impact how the data is interpreted. The purpose of the present study was to determine which variables could be impacted by setting different parameters for data analysis. We performed a secondary analysis of data gained from a clinical trial conducted on older adults (>65; M=71.1, SD=5.3) (n=100) with neck and back disabilities and compared the effects of two different cut-point sets commonly used in the analysis of older adult accelerometry data; the Matthews (2005) and Freedson Melanson, & Sirard (1998) sets. The Matthews set was found to assign significantly greater moderate-to-vigorous physical activity (MVPA) per day than the Freedson set in all comparisons. Further results from multiple analyses of dependent variables: time in (MVPA) bouts of >10min per day; mean bout length; and number of bouts per day; will be discussed. Cut-point selection can impact key variables of interest in accelerometry data. Selection of methods with relevant justification, including the impact age may have on results, should be outlined apriori and results interpreted with caution.