Abstract
BackgroundWearable devices enable monitoring and measurement of physiological parameters over a 24-h period, and some of which exhibit circadian rhythm characteristics. However, the currently available R package cosinor could only analyze daily cross-sectional data and compare the parameters between groups with two levels. To evaluate longitudinal changes in the circadian patterns, we need to extend the model to a mixed-effect model framework, allowing for random effects and interaction between COSINOR parameters and time-varying covariates.ResultsWe developed the cosinoRmixedeffects R package for modelling longitudinal periodic data using mixed-effects cosinor models. The model allows for covariates and interactions with the non-linear parameters MESOR, amplitude, and acrophase. To facilitate ease of use, the package utilizes the syntax and functions of the widely used emmeans package to obtain estimated marginal means and contrasts. Estimation and hypothesis testing involving the non-linear circadian parameters are carried out using bootstrapping. We illustrate the package functionality by modelling daily measurements of heart rate variability (HRV) collected among health care workers over several months. Differences in circadian patterns of HRV between genders, BMI, and during infection with SARS-CoV2 are evaluated to illustrate how to perform hypothesis testing.ConclusioncosinoRmixedeffects package provides the model fitting, estimation and hypothesis testing for the mixed-effects COSINOR model, for the linear and non-linear circadian parameters MESOR, amplitude and acrophase. The model accommodates factors with any number of categories, as well as complex interactions with circadian parameters and categorical factors.
Highlights
Wearable devices enable monitoring and measurement of physiological parameters over a 24-h period, and some of which exhibit circadian rhythm charac‐ teristics
heart rate variability (HRV) can be written as: Y(t) = M + β × xt + γ × zt + ei(t). To broaden this to a longitudinal framework, where individual circadian patterns change over a period of many days, we extended the model (Eq 1) to a mixed-effect COSINOR model, where the measure Y of subject i at time t can be written as Yit = (M + β × xit + γ × zit) + Wit × θi + ei(t), ei(t) ~ N(0, s), where θi is a vector of random effects with multivariate normal distribution θi ~ N(0,Σ) that intrinsically modeled the within-patient correlation
We present how to use the functions in the consinoRmixedeffects package to fit simple models comparing Rhythm-adjusted mean (MESOR), amplitude and acrophase of HRV: (1) between COVID-19 positive and COVID-19 negative infection status and (2) across body mass index (BMI) categories within each biological sex
Summary
Wearable devices enable monitoring and measurement of physiological parameters over a 24-h period, and some of which exhibit circadian rhythm charac‐ teristics. To evaluate longitudinal changes in the circadian patterns, we need to extend the model to a mixed-effect model framework, allowing for random effects and interaction between COSINOR parameters and time-varying covariates. Development of techniques that appropriately model the non-uniform, sparsely sampled circadian rhythm data in a longitudinal setting, and implement a proper hypothesis testing framework, are needed to advance the use of integrated wearable data for prediction of any relevant outcomes. The currently available R package cosinor [5] implements such a model and allows the user to evaluate cross-sectional differences in the COSINOR parameters between groups with only two levels. To evaluate longitudinal changes in the circadian patterns, we extended the COSINOR model to a mixed-effect model framework, allowing for random effects and interaction between COSINOR parameters and time-varying covariates. We recently proposed a mixed-effect COSINOR model framework to evaluate changes in circadian HRV patterns after infection with SARS-CoV2 in healthcare workers [6], as well as to evaluate the association of HRV and self-reported stress [7]
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