||In this work we investigate the inﬂuence of varying daily activity dataset characteristics on topic model performance stability for daily routine discovery. For this purpose, we denote a set of key dataset properties that inﬂuence the experimental design regarding recording, as well as data pre-processing steps. Using generated daily activity datasets, we identiﬁed optimal topic model stability for particular dataset properties. Results indicated that topic model routine duration should exceed document size by a factor of more than two. Recording durations of more than 9 days were required for a set of four routines and activity primitive overlap may not exceed 5%.