Abstract
BackgroundWearable devices have been widely used in clinical studies to study daily activity patterns, but the analysis remains a major obstacle for researchers.ObjectiveThis study proposes a novel method to characterize sleep-activity rhythms using actigraphy and further use it to describe early childhood daily rhythm formation and examine its association with physical development.MethodsWe developed a machine learning–based Penalized Multiband Learning (PML) algorithm to sequentially infer dominant periodicities based on the Fast Fourier Transform (FFT) algorithm and further characterize daily rhythms. We implemented and applied the algorithm to Actiwatch data collected from a cohort of 262 healthy infants at ages 6, 12, 18, and 24 months, with 159, 101, 111, and 141 participants at each time point, respectively. Autocorrelation analysis and Fisher test in harmonic analysis with Bonferroni correction were applied for comparison with the PML. The association between activity rhythm features and early childhood motor development, assessed using the Peabody Developmental Motor Scales-Second Edition (PDMS-2), was studied through linear regression analysis.ResultsThe PML results showed that 1-day periodicity was most dominant at 6 and 12 months, whereas one-day, one-third–day, and half-day periodicities were most dominant at 18 and 24 months. These periodicities were all significant in the Fisher test, with one-fourth–day periodicity also significant at 12 months. Autocorrelation effectively detected 1-day periodicity but not the other periodicities. At 6 months, PDMS-2 was associated with the assessment seasons. At 12 months, PDMS-2 was associated with the assessment seasons and FFT signals at one-third–day periodicity (P<.001) and half-day periodicity (P=.04), respectively. In particular, the subcategories of stationary, locomotion, and gross motor were associated with the FFT signals at one-third–day periodicity (P<.001).ConclusionsThe proposed PML algorithm can effectively conduct circadian rhythm analysis using time-series wearable device data. The application of the method effectively characterized sleep-wake rhythm development and identified the association between daily rhythm formation and motor development during early childhood.
Highlights
Wearable devices have been increasingly used in research recently, as they can provide continuous objective monitoring of activities as well as vital sign data such as body temperature and pulse rates[1,2,3]
Penalized Multi-band Learning (PML) extracts periodic information using Fast Fourier Transform (FFT) and performs penalized selection based on regularization, a classic approach used in machine learning, to identify dominant periodicities and further characterize daily rhythms [22, 23]
For 6 months and 12 months, the first harmonic at 1-day is most dominant, as we can observe a large dip in mean squared error (MSE) when the first periodicity is selected while the following periodicities that are further selected do not cause the same level of decrease
Summary
Wearable devices have been increasingly used in research recently, as they can provide continuous objective monitoring of activities as well as vital sign data such as body temperature and pulse rates[1,2,3]. We propose a Penalized Multi-band Learning (PML) approach that can complement current methods to characterize daily rhythms based on periodic information in time series wearable device data. We present one application of the method to early childhood wearable device activity data, in which we were able to characterize the formation and consolidation of sleep-activity rhythms and further studied its association with physical development during early childhood. Study Objective: This study proposed a novel method to characterize sleep-activity rhythms using actigraphy and further used it to describe early childhood daily rhythm formation and examine its association with physical development
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
