Assessing Chronotypes by Ambulatory Circadian Monitoring

Antonio Martinez-Nicolas, Maria Jose Martinez-Madrid, Pedro Francisco Almaida-Pagan, Maria-Angeles Bonmati-Carrion, Juan Antonio Madrid, Maria Angeles Rol, Antonio Martinez-Nicolas, Maria Jose Martinez-Madrid, Pedro Francisco Almaida-Pagan, Maria-Angeles Bonmati-Carrion, Juan Antonio Madrid, Maria Angeles Rol

Abstract

In order to develop objective indexes for chronotype identification by means of direct measurement of circadian rhythms, 159 undergraduate students were recruited as volunteers and instructed to wear ambulatory circadian monitoring (ACM) sensors that continuously gathered information on the individual's environmental light and temperature exposure, wrist temperature, body position, activity, and the integrated TAP (temperature, activity, and position) variable for 7 consecutive days under regular free-living conditions. Among all the proposed indexes, the night phase marker (NPM) of the TAP variable was the best suited to discriminate among chronotypes, due to its relationship with the Munich ChronoType Questionnaire (β = 0.531; p < 0.001). The NPM of TAP allowed subjects to be classified as early- (E-type, 20%), neither- (N-type, 60%), and late-types (L-type, 20%), each of which had its own characteristics. In terms of light exposure, while all subjects had short exposure times to bright light (>100 lux), with a daily average of 93.84 ± 5.72 min, the earlier chronotypes were exposed to brighter days and darker nights compared to the later chronotypes. Furthermore, the earlier chronotypes were associated with higher stability and day-night contrast, along with an earlier phase, which could be the cause or consequence of the light exposure habits. Overall, these data support the use of ACM for chronotype identification and for evaluation under free living conditions, using objective markers.

Keywords: activity; ambulatory circadian monitoring; chronotype; circadian rhythm; distal skin temperature; light exposure.

Copyright © 2019 Martinez-Nicolas, Martinez-Madrid, Almaida-Pagan, Bonmati-Carrion, Madrid and Rol.

Figures

FIGURE 1
FIGURE 1
Mean waveforms and actograms for early (E-types, in yellow; n = 20% of total subjects), neither (N-types, in dotted black; n = 60% of total subjects), and late-types (L-types, in blue; n = 20% of total subjects) for: (A) light exposure (L; total subjects = 137) and (B) environmental temperature (ET; total subjects = 137). Waveform data are expressed as mean ± SEM. Plotted actograms correspond to the first tercile values (values corresponding to the rest phase).
FIGURE 2
FIGURE 2
Mean waveforms and actograms for early (E-types, in yellow; n = 20% of total subjects), neither (N-types, in dotted black; n = 60% of total subjects), and late-types (L-types, in blue; n = 20% of total subjects) for: (A) wrist temperature (WT; total subjects = 159), (B) activity (A; total subjects = 159), and (C) body position (P; total subjects = 159). Waveform data are expressed as mean ± SEM. WT actogram corresponds to the third tercile values, while activity and body position actograms correspond to the first tercile values (values corresponding to the rest phase).
FIGURE 3
FIGURE 3
Mean waveforms and actograms for early (E-types, in yellow; n = 20% of the total subjects), neither (N-types, in dotted black; n = 60% of the total subjects), and late-types (L-types, in blue; n = 20% of the total subjects) for: (A) TAP (total subjects = 159) and (B) sleep (S; total subjects = 148). Waveform data are expressed as mean ± SEM. TAP and sleep actograms correspond to the first and third tercile values, respectively (values corresponding to the rest phase).

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