Circadian Rhythm Analysis Using Wearable Device Data: A Novel Penalized Machine Learning Approach (Preprint)

Study Objective: Actigraphy has been widely used in clinical studies to study sleep-activity patterns, but the analysis remains the major obstacle for researchers. This study proposed a novel method to characterize sleep-wake circadian rhythm using actigraphy and further used it to describe early childhood daily rhythm formation and examine its association with physical development. Methods: We developed a machine learning-based Penalized Multi-band Learning (PML) algorithm to sequentially infer dominant periodicities based on Fast Fourier Transform (FFT) and further characterize daily rhythms. We implemented and applied the algorithm to Actiwatch data collected from a 262 healthy infant cohort at 6-, 12-, 18-, and 24-month old, with 159, 101, 111, and 141 subjects participating at each time point respectively. Autocorrelation analysis and Fisher's test for harmonic analysis with Bonferroni correction were applied to compare with PML. The association between activity rhythm features and early childhood motor development, assessed by Peabody Developmental Motor Scales-Second Edition (PDMS-2), was studied through linear regression. Results: PML results showed that 1-day periodicity is most dominant at 6 and 12 months, whereas 1-day, 1/3-day, and 1/2-day periodicities are most dominant at 18 and 24 months. These periodicities are all significant in Fisher's test, with 1/4-day periodicity also significant at 12 months. Autocorrelation effectively detected 1-day periodicity but not others. At 6 months, PDMS-2 is associated with assessment seasons. At 12 months, PDMS-2 is associated with seasons and FFT signals at 1/3-day periodicity (p<0.001) and 1/2-day periodicity (p=0.039). In particular, subcategories of stationary, locomotion, and gross motor are associated with FFT signals at 1/3-day periodicity (p<0.001). Conclusions: The proposed PML algorithm can effectively conduct circadian rhythm analysis using actigraphy and characterize sleep-wake rhythm development of the early childhood population. Furthermore, our study found the association between daily rhythm formation and motor development during early childhood.