Citation

Danilevicz IM, van Hees VT, van der Heide FCT, Jacob L, Landré B, Benadjaoud MA, Sabia S. BMC Med. Res. Methodol. 2024; 24(1): e132.

Copyright

(Copyright © 2024, Holtzbrinck Springer Nature Publishing Group - BMC)

DOI

10.1186/s12874-024-02255-w

PMID

38849718

Abstract

Accelerometers, devices that measure body movements, have become valuable tools for studying the fragmentation of rest-activity patterns, a core circadian rhythm dimension, using metrics such as inter-daily stability (IS), intradaily variability (IV), transition probability (TP), and self-similarity parameter (named α ). However, their use remains mainly empirical. Therefore, we investigated the mathematical properties and interpretability of rest-activity fragmentation metrics by providing mathematical proofs for the ranges of IS and IV, proposing maximum likelihood and Bayesian estimators for TP, introducing the activity balance index (ABI) metric, a transformation of α , and describing distributions of these metrics in real-life setting. Analysis of accelerometer data from 2,859 individuals (age=60-83 years, 21.1% women) from the Whitehall II cohort (UK) shows modest correlations between the metrics, except for ABI and α. Sociodemographic (age, sex, education, employment status) and clinical (body mass index (BMI), and number of morbidities) factors were associated with these metrics, with differences observed according to metrics. For example, a difference of 5 units in BMI was associated with all metrics (differences ranging between -0.261 (95% CI -0.302, -0.220) to 0.228 (0.18, 0.268) for standardised TP rest to activity during the awake period and TP activity to rest during the awake period, respectively). These results reinforce the value of these rest-activity fragmentation metrics in epidemiological and clinical studies to examine their role for health. This paper expands on a set of methods that have previously demonstrated empirical value, improves the theoretical foundation for these methods, and evaluates their empirical use in a large dataset.

Language: en

Keywords

Humans; Aged; Female; Male; Middle Aged; Aged, 80 and over; Body Mass Index; Circadian rhythm; Likelihood Functions; Bayes Theorem; *Accelerometry/methods/statistics & numerical data; *Rest/physiology; Circadian Rhythm/physiology; Detrended fluctuation analysis; Inter-daily stability; Intradaily variability; Motor Activity/physiology; Transition probability; Whitehall II cohort