ABSTRACT
Analysis of rhythmicity in count data has become an important aspect in different fields from science and engineering to economy and process planning. Several methods have recently been implemented to investigate the rhythmicity of continuous data. However, in most cases, these need to be manually adapted to work with count data as well. Namely, non-negative integer data that are usually obtained by counting of specific events. Herein, we describe the implementation of RhythmCount, an open-source Python module specifically devoted to rhythmicity analysis of count data. RhythmCount combines the cosinor regression model with different count data models. The proposed implementation allows automatic identification of the most suitable model for a given dataset, assessment of different measures and parameters of the rhythmicity of the dataset, and production of publication-ready figures that can be used for a straightforward interpretation of the obtained results. We demonstrate an application of the proposed module in the analysis and comparison of the daily traffic trends during the COVID-19 epidemic with the daily traffic trends in normal (non-epidemic) conditions. RhythmCount is available at https://github.com/ninavelikajne/RhythmCount under the MIT license. The implementation reported in this paper corresponds to the software release v1.1.
ABSTRACT
BACKGROUND: Wearable 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. RESULTS: We 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. CONCLUSION: cosinoRmixedeffects 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.