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Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

Analysis method and experimental conditions affect computed circadian phase from melatonin data.

Hadassa Klerman1, Melissa A St Hilaire, Richard E Kronauer

  • 1Division of Sleep Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America. ebklerman@hms.harvard.edu

Plos One
|April 19, 2012
PubMed
Summary
This summary is machine-generated.

Accurate circadian phase assessment relies on melatonin analysis. Method choice impacts results, especially with low melatonin amplitude or incomplete data, affecting phase estimation reliability.

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Human Circadian Phenotyping and Diurnal Performance Testing in the Real World

Published on: April 7, 2020

Area of Science:

  • Chronobiology
  • Physiology
  • Biomarker Analysis

Background:

  • Accurate circadian phase determination is crucial for research and clinical applications due to the master circadian pacemaker's influence on physiology.
  • Melatonin is the most reliable marker for assessing human circadian pacemaker activity.

Purpose of the Study:

  • To compare the accuracy and variability of melatonin phase estimates using seventeen different analysis methods.
  • To evaluate how data characteristics, such as melatonin amplitude and completeness, affect phase assessment.

Main Methods:

  • Analysis of plasma melatonin rhythms using three categories: curve-fitting, threshold-based, and physiologically-based linear differential equations.
  • Comparison of phase estimates from seventeen derived markers under controlled experimental conditions.
  • Systematic assessment of data loss effects on phase estimate variability.

Main Results:

  • Melatonin profiles with lower amplitude or from older subjects were less likely to be fitted by analysis methods.
  • No significant differences in melatonin onset phase estimates were found between methods after accounting for circadian drift.
  • Significant differences were observed in melatonin offset phase estimates (P<0.0001).
  • Data loss near melatonin secretion onset differentially impacted phase estimates, causing inaccuracies in some methods.

Conclusions:

  • Melatonin data characteristics, including amplitude and completeness, significantly influence the accuracy of circadian phase assessment.
  • The choice of melatonin analysis method is critical and can lead to differential results based on data quality.
  • Careful consideration of data collection and analysis methods is necessary for reliable circadian phase determination.