Estimation of Human Circadian Phase via a Multi-Channel Ambulatory Monitoring System and a Multiple Regression Model

Abstract

Reliable detection of circadian phase in humans using noninvasive ambulatory measurements in real-life conditions is challenging and still an unsolved problem. The masking effects of everyday behavior and environmental input such as physical activity and light on the measured variables need to be considered critically. Here, we aimed at developing techniques for estimating circadian phase with the lowest subject burden possible, that is, without the need of constant routine (CR) laboratory conditions or without measuring the standard circadian markers, (rectal) core body temperature (CBT), and melatonin levels. In this validation study, subjects (N = 16) wore multi-channel ambulatory monitoring devices and went about their daily routine for 1 week. The devices measured a large number of physiological, behavioral, and environmental variables, including CBT, skin temperatures, cardiovascular and respiratory function, movement/posture, ambient temperature, and the spectral composition and intensity of light received at eye level. Sleep diaries were logged electronically. After the ambulatory phase, subjects underwent a 32-h CR procedure in the laboratory for measuring unmasked circadian phase based on the "midpoint" of the salivary melatonin profile. To overcome the complex masking effects of confounding variables during ambulatory measurements, multiple regression techniques were applied in combination with the cross-validation approach to subject-independent prediction of circadian phase. The most accurate estimate of circadian phase was achieved using skin temperatures, irradiance for ambient light in the blue spectral band, and motion acceleration as predictors with lags of up to 24 h. Multiple regression showed statistically significant improvement of variance of prediction error over the traditional approaches to determining circadian phase based on single predictors (motion acceleration or sleep log), although CBT was intentionally not included as the predictor. Compared to CBT alone, our method resulted in a 40% smaller range of prediction errors and a nonsignificant reduction of error variance. The proposed noninvasive measurement method could find applications in sleep medicine or in other domains where knowing the exact endogenous circadian phase is important (e.g., for the timing of light therapy).

A review of signals used in sleep analysis

“…Ortiz-Tudela et al (2010) suggested an integrated variable, based on thermometry, actimetry and body position to reduce individual recording artefacts and showed that it is well correlated with rest-activity logs. Kolodyazhniy et al (2011) evaluated circadian phase estimation using standard least squares algorithmic regression techniques on skin temperatures, accelerometry and ambient light level in the blue spectral band and showed a statistically significant improvement of variance of prediction error over traditional single predictor methods.…”

The Vigilance Decrement in Executive Function Is Attenuated When Individual Chronotypes Perform at Their Optimal Time of Day

“…With regard to evening-type, morning-type people show a 2–4 h advance in circadian phase in variables like subjective alertness, sleep times, core body temperature (CBT) or distal skin temperature (DST) [16]–[20]. Given that DST is closely associated to the CBT rhythm (showing an advanced rhythm phase and inverse temporal curve with maximum values within the sleeping period) [21], DST has been proposed as a reliable circadian index under free-living conditions [22], [23]. Additionally, infraclavicular temperature and the difference between distal and proximal temperatures (distal-proximal gradient, DPG) have been related to the vigilance state.…”

Human Circadian Phase Estimation from Signals Collected in Ambulatory Conditions Using an Autoregressive Model

“…Some commonly used signals are activity level, light exposure Kronauer et al, 1999;St. Hilaire et al, 2007b;Mott et al, 2011), and skin temperature (Kolodyazhniy et al, 2011(Kolodyazhniy et al, , 2012. The drawback with these methods is that they require the collection of data over extended periods of time, in some cases up to 1 week, to obtain accurate results.…”