Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/21365
Title: Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions.
Austin Authors: Stone, Julia E;Phillips, Andrew J K;Ftouni, Suzanne;Magee, Michelle;Howard, Mark E ;Lockley, Steven W;Sletten, Tracey L;Anderson, Clare;Rajaratnam, Shantha M W;Postnova, Svetlana
Affiliation: Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Victoria, Australia
Institute for Breathing and Sleep, Austin Health, Heidelberg, Victoria, Australia
Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA
School of Physics, University of Sydney, Sydney, New South Wales, Australia
School of Psychological Sciences and Turner Institute for Brain and Mental Health, Faculty of Medicine, Nursing & Health Sciences, Monash University, Clayton, Victoria, Australia
Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
Issue Date: 29-Jul-2019
metadata.dc.date: 2019-07-29
Publication information: Scientific Reports 2019; 9(1): 11001
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.
URI: http://ahro.austin.org.au/austinjspui/handle/1/21365
DOI: 10.1038/s41598-019-47311-4
ORCID: 0000-0001-7706-7471
PubMed URL: 31358781
Type: Journal Article
Appears in Collections:Journal articles

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