Abstract
A neural network model was previously developed to predict melatonin rhythms accurately from blue light and skin temperature recordings in individuals on a fixed sleep schedule. This study aimed to test the generalizability of the model to other sleep schedules, including rotating shift work. Ambulatory wrist blue light irradiance and skin temperature data were collected in 16 healthy individuals on fixed and habitual sleep schedules, and 28 rotating shift workers. Artificial neural network models were trained to predict the circadian rhythm of (i) salivary melatonin on a fixed sleep schedule; (ii) urinary aMT6s on both fixed and habitual sleep schedules, including shift workers on a diurnal schedule; and (iii) urinary aMT6s in rotating shift workers on a night shift schedule. To determine predicted circadian phase, center of gravity of the fitted bimodal skewed baseline cosine curve was used for melatonin, and acrophase of the cosine curve for aMT6s. On a fixed sleep schedule, the model predicted melatonin phase to within ± 1 hour in 67% and ± 1.5 hours in 100% of participants, with mean absolute error of 41 ± 32 minutes. On diurnal schedules, including shift workers, the model predicted aMT6s acrophase to within ± 1 hour in 66% and ± 2 hours in 87% of participants, with mean absolute error of 63 ± 67 minutes. On night shift schedules, the model predicted aMT6s acrophase to within ± 1 hour in 42% and ± 2 hours in 53% of participants, with mean absolute error of 143 ± 155 minutes. Prediction accuracy was similar when using either 1 (wrist) or 11 skin temperature sensor inputs. These findings demonstrate that the model can predict circadian timing to within ± 2 hours for the vast majority of individuals on diurnal schedules, using blue light and a single temperature sensor. However, this approach did not generalize to night shift conditions.
Highlights
The current gold standard method for assessing the phase of the central circadian pacemaker is the melatonin rhythm, measurable from plasma or saliva, or its urinary metabolite, 6-sulphatoxymelatonin[10,11]
It is unclear whether this approach is able to predict circadian phase when individuals are on other diurnal sleep schedules, or when the primary light-dark signal is uncoupled from the circadian system, as is observed in rotating shift workers prior to adaptation to the new work schedule[22,23,24,25,26]
A non-shift work study where healthy individuals were monitored over a week of habitual sleep (HS dataset), followed by two weeks of fixed sleep (FS dataset) with an 8:16 h sleep:wake schedule
Summary
The current gold standard method for assessing the phase of the central circadian pacemaker is the melatonin rhythm, measurable from plasma or saliva, or its urinary metabolite, 6-sulphatoxymelatonin (aMT6s)[10,11]. Healthy young male participants (n = 25) wore ambulatory recording equipment over seven days while maintaining a fixed sleep schedule, prior to circadian phase assessment under laboratory constant routine conditions. Using this method, peripheral skin temperature measured over distal and proximal skin sites and blue light irradiance measured at eye level provided the most precise estimates of melatonin phase (mean absolute error of 19 ± 13 minutes) compared to a multivariate non-linear regression approach using the same inputs, or univariate approaches such as phase estimates based on ambulatory core body temperature or sleep timing[14,15]. Further testing is required to validate and extend the neural network method for use in a wider range of conditions, including circadian misalignment
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
