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Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions
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. Ambula...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6662750/ https://www.ncbi.nlm.nih.gov/pubmed/31358781 http://dx.doi.org/10.1038/s41598-019-47311-4 |
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author | Stone, Julia E. Phillips, Andrew J. K. Ftouni, Suzanne Magee, Michelle Howard, Mark Lockley, Steven W. Sletten, Tracey L. Anderson, Clare Rajaratnam, Shantha M. W. Postnova, Svetlana |
author_facet | Stone, Julia E. Phillips, Andrew J. K. Ftouni, Suzanne Magee, Michelle Howard, Mark Lockley, Steven W. Sletten, Tracey L. Anderson, Clare Rajaratnam, Shantha M. W. Postnova, Svetlana |
author_sort | Stone, Julia E. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6662750 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-66627502019-08-02 Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions Stone, Julia E. Phillips, Andrew J. K. Ftouni, Suzanne Magee, Michelle Howard, Mark Lockley, Steven W. Sletten, Tracey L. Anderson, Clare Rajaratnam, Shantha M. W. Postnova, Svetlana Sci Rep Article 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. Nature Publishing Group UK 2019-07-29 /pmc/articles/PMC6662750/ /pubmed/31358781 http://dx.doi.org/10.1038/s41598-019-47311-4 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Stone, Julia E. Phillips, Andrew J. K. Ftouni, Suzanne Magee, Michelle Howard, Mark Lockley, Steven W. Sletten, Tracey L. Anderson, Clare Rajaratnam, Shantha M. W. Postnova, Svetlana Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions |
title | Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions |
title_full | Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions |
title_fullStr | Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions |
title_full_unstemmed | Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions |
title_short | Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions |
title_sort | generalizability of a neural network model for circadian phase prediction in real-world conditions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6662750/ https://www.ncbi.nlm.nih.gov/pubmed/31358781 http://dx.doi.org/10.1038/s41598-019-47311-4 |
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