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Author (up) Stone, J.E.; Phillips, A.J.K.; Ftouni, S.; Magee, M.; Howard, M.; Lockley, S.W.; Sletten, T.L.; Anderson, C.; Rajaratnam, S.M.W.; Postnova, S. url  doi
openurl 
  Title Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions Type Journal Article
  Year 2019 Publication Scientific Reports Abbreviated Journal Sci Rep  
  Volume 9 Issue 1 Pages 11001  
  Keywords Human Health  
  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.  
  Address School of Physics, University of Sydney, Sydney, New South Wales, Australia  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2045-2322 ISBN Medium  
  Area Expedition Conference  
  Notes PMID:31358781; PMCID:PMC6662750 Approved no  
  Call Number GFZ @ kyba @ Serial 2597  
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