Periodic phenomena or oscillating signals can be found frequently in nature and recent research has observed pe- riodicity appearing in lifelog data, the automatic digital recording of everyday activities. In this paper we are exploring periodicity and intensity of periodicity in big data settings, especially when the data is noisy, unevenly sampled and incomplete. An interesting possibility is to compute the intensity or strength of detected periodicity across the time span of a lifelog to see if it reveals changes in this strength at different times, indicating shifts in underlying behaviour. In this paper we propose several metrics to estimate the intensity of periodicity, longitudinally. Evaluation of these metrics is conducted on simulated high-level activity data generated from a proposed model. We also explore periodicity intensity calculated from two real lifelog datasets using. One is “big” data consists of low-level accelerometer data and another one is high level athletic performance data.