ArticleFrontiers in neuroscience2022
The circadian stimulus-oscillator model: Improvements to Kronauer's model of the human circadian pacemaker.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Circadian Biomarkers Before and After the Transition to Spring Daylight Saving Time.Sleep science (Sao Paulo, Brazil) · 2026Article
- Mathematical Analysis of Light-sensitivity Related Challenges in Assessment of the Intrinsic Period of the Human Circadian Pacemaker.Journal of biological rhythms · 2024Article
- Days to re-entrainment following the spring and autumn changes in local clock time: Beyond simple heuristics.Frontiers in photonics · 2024Article
- Method to determine whether sleep phenotypes are driven by endogenous circadian rhythms or environmental light by combining longitudinal data and personalised mathematical models.PLoS computational biology · 2023Article
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Authors and funding
4 authors.
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Abstract
Modeling how patterns of light and dark affect circadian phase is important clinically and organizationally (e.g., the military) because circadian disruption can compromise health and performance. Limit-cycle oscillator models in various forms have been used to characterize phase changes to a limited set of light interventions. We approached the analysis of the van der Pol oscillator-based model proposed by Kronauer and colleagues in 1999 and 2000 (Kronauer99) using a well-established framework from experimental psychology whereby the stimulus (S) acts on the organism (O) to produce a response (R). Within that framework, using four independent data sets utilizing calibrated personal light measurements, we conducted a serial analysis of the factors in the Kronauer99 model that could affect prediction accuracy characterized by changes in dim-light melatonin onset. Prediction uncertainty was slightly greater than 1 h for the new data sets using the original Kronauer99 model. The revised model described here reduced prediction uncertainty for these same data sets by roughly half.
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