MULTI-MODEL APPROACH FOR MODELLING CIRCADIAN RHYTHM PHYSIOLOGICAL PARAMETERS

Present study focuses on modelling four circadian rhythmic physiological parameters (body temperature, heart rate, cortisol levels, and melatonin levels) using a multi-model approach. Four distinct models were employed: cosinor model, spline models, Fourier series (Fourier decomposition), and ARIMA models (a traditional statistical tool for modelling times series data). A convenience sample of twenty internee doctors was selected from four public sector hospitals located in Jeddah. Data were collected over a 24-hour period, ensuring the inclusion of typical circadian patterns for the four physiological parameters. The models were evaluated based on their fit and predictive accuracy, with Mean Squared Error (MSE) used as the primary metric. For extracting relevant results, R software was used and codes developed for the proposed study are given in Appendix ‘A’. Outcomes of the present study demonstrated that each model effectively captured different aspects of the circadian rhythms, with Fourier series (Fourier decomposition) and spline models providing the most flexible fits and excelled in performance accuracy. The multi-model approach enabled a comprehensive understanding of the circadian patterns, offering insights into the complex dynamics of physiological rhythms. This study highlights the importance of using diverse modelling techniques to capture the multifaceted nature of circadian rhythms, with implications for improving health outcomes through better understanding and management of biological cycles.