Application of Empirical Mode Decomposition to Mother and Infant Physical Activity

Author:

Shimizu Etsuko,Kim Jinhyuk,Yoshiuchi Kazuhiro,Yamamoto Yoshiharu,Nakamura Toru

Abstract

Summary Background: The mutual dependencies between mother and infant circadian rhythms are important for examining disturbances of maternal circadian rhythms, which are considered substantial risk factors for the development of maternal depression during childrearing periods. Objectives: We characterized the mutual dependencies of maternal–infant circadian rhythms by an index of synchronization properties and then tested the hypothesis that such an index, specifically the instantaneous phase differences between their rhythms, is associated with maternal mental health. Methods: We performed longitudinal recordings of maternal symptoms of fatigue, stress, and mood states by ecological momentary assessment, together with simultaneous measurements of mother and infant physical activity data in daily life, on 20 mother–infant pairs for a period of >1 week. The circadian components in their physical activity data were extracted by ensembled bivariate empirical mode decomposition, and the corresponding instantaneous phases were then obtained based on the Hilbert transformation. The associations between diurnal maternal symptoms and absolute phase differences between mother and infant circadian rhythms were tested by multilevel models. Results: Diurnal fatigue and depressive mood scores showed positive and significant correlations (p < 0.05) with the increase in instantaneous mother–infant phase differences, indicating the significant role of synchronization of mother–infant circadian rhythms for maintaining maternal mental health. Conclusions: These findings suggest that modifications of maternal and/or infant circadian rhythms may lead to the improvement of maternal mental health during child-rearing periods.

Funder

Ministry of Education, Culture, Sports, Science and Technology

Publisher

Georg Thieme Verlag KG

Subject

Health Information Management,Advanced and Specialized Nursing,Health Informatics

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