Correlation Engine 2.0
Clear Search sequence regions

  • adult (2)
  • circadian rhythm (1)
  • dim (9)
  • humans (1)
  • light (11)
  • melatonin (10)
  • night shift workers (4)
  • phase (4)
  • saliva (1)
  • shift workers (1)
  • sleep (5)
  • wrist (5)
  • Sizes of these terms reflect their relevance to your search.

    A critical barrier to successful treatment of circadian misalignment in shift workers is determining circadian phase in a clinical or field setting. Light and movement data collected passively from wrist actigraphy can generate predictions of circadian phase via mathematical models; however, these models have largely been tested in non-shift working adults. This study tested the feasibility and accuracy of actigraphy in predicting dim light melatonin onset (DLMO) in fixed night shift workers. A sample of 45 night shift workers wore wrist actigraphs before completing DLMO in the laboratory (17.0 days ± 10.3 SD). DLMO was assessed via 24 hourly saliva samples in dim light (<10 lux). Data from actigraphy were provided as input to a mathematical model to generate predictions of circadian phase. Agreement was assessed and compared to average sleep timing on non-workdays as a proxy of DLMO. Model code and an open-source prototype assessment tool are available ( Model predictions of DLMO showed good concordance with in-lab DLMO, with Lin's concordance coefficient of 0.70, which was twice as high as agreement using average sleep timing as a proxy of DLMO. The absolute mean error of the predictions was 2.88 h, with 76% and 91% of the predictions falling with 2 and 4 h, respectively. This study is the first to demonstrate the use of wrist actigraphy-based estimates of circadian phase as a clinically useful and valid alternative to in-lab measurement of DLMO in fixed night shift workers. Future research should explore how additional predictors may impact accuracy. © Sleep Research Society 2020. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail


    Philip Cheng, Olivia Walch, Yitong Huang, Caleb Mayer, Chaewon Sagong, Andrea Cuamatzi Castelan, Helen J Burgess, Thomas Roth, Daniel B Forger, Christopher L Drake. Predicting circadian misalignment with wearable technology: validation of wrist-worn actigraphy and photometry in night shift workers. Sleep. 2021 Feb 12;44(2)

    Expand section icon Mesh Tags

    Expand section icon Substances

    PMID: 32918087

    View Full Text