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1. Estimating circadian phase in elementary school children: leveraging advances in physiologically informed models of circadian entrainment and wearable devices

   https://doi.org/10.1093/sleep/zsac061  

   Moreno, Jennette P. ; Hannay, Kevin M. ; Walch, Olivia ; Dadabhoy, Hafza ; Christian, Jessica ; Puyau, Maurice ; El-Mubasher, Abeer ; Bacha, Fida ; Grant, Sarah R. ; Park, Rebekah Julie ; et al ( March 2022 , Sleep)

   Examine the ability of a physiologically based mathematical model of human circadian rhythms to predict circadian phase, as measured by salivary dim light melatonin onset (DLMO), in children compared to other proxy measurements of circadian phase (bedtime, sleep midpoint, and wake time).

   As part of an ongoing clinical trial, a sample of 29 elementary school children (mean age: 7.4 ± .97 years) completed 7 days of wrist actigraphy before a lab visit to assess DLMO. Hourly salivary melatonin samples were collected under dim light conditions (<5 lx). Data from actigraphy were used to generate predictions of circadian phase using both a physiologically based circadian limit cycle oscillator mathematical model (Hannay model), and published regression equations that utilize average sleep onset, midpoint, and offset to predict DLMO. Agreement of proxy predictions with measured DLMO were assessed and compared.

   DLMO predictions using the Hannay model outperformed DLMO predictions based on children’s sleep/wake parameters with a Lin’s Concordance Correlation Coefficient (LinCCC) of 0.79 compared to 0.41–0.59 for sleep/wake parameters. The mean absolute error was 31 min for the Hannay model compared to 35–38 min for the sleep/wake variables.

   Our findings suggest that sleep/wake behaviors were weak proxies of DLMO phase inmore »children, but mathematical models using data collected from wearable data can be used to improve the accuracy of those predictions. Additional research is needed to better adapt these adult models for use in children.

   The i Heart Rhythm Project: Healthy Sleep and Behavioral Rhythms for Obesity Prevention https://clinicaltrials.gov/ct2/show/NCT04445740.

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2. Seasonality and light phase-resetting in the mammalian circadian rhythm

   https://doi.org/10.1038/s41598-020-74002-2  

   Hannay, Kevin M. ; Forger, Daniel B. ; Booth, Victoria ( December 2020 , Scientific Reports)

   Abstract We study the impact of light on the mammalian circadian system using the theory of phase response curves. Using a recently developed ansatz we derive a low-dimensional macroscopic model for the core circadian clock in mammals. Significantly, the variables and parameters in our model have physiological interpretations and may be compared with experimental results. We focus on the effect of four key factors which help shape the mammalian phase response to light: heterogeneity in the population of oscillators, the structure of the typical light phase response curve, the fraction of oscillators which receive direct light input and changes in the coupling strengths associated with seasonal day-lengths. We find these factors can explain several experimental results and provide insight into the processing of light information in the mammalian circadian system. In particular, we find that the sensitivity of the circadian system to light may be modulated by changes in the relative coupling forces between the light sensing and non-sensing populations. Finally, we show how seasonal day-length, after-effects to light entrainment and seasonal variations in light sensitivity in the mammalian circadian clock are interrelated.
3. Optimal adjustment of the human circadian clock in the real world

   https://doi.org/10.1371/journal.pcbi.1008445  

   Christensen, Samuel ; Huang, Yitong ; Walch, Olivia J. ; Forger, Daniel B. ( December 2020 , PLOS Computational Biology)

   Csikász-Nagy, Attila (Ed.)

   Which suggestions for behavioral modifications, based on mathematical models, are most likely to be followed in the real world? We address this question in the context of human circadian rhythms. Jet lag is a consequence of the misalignment of the body’s internal circadian (~24-hour) clock during an adjustment to a new schedule. Light is the clock’s primary synchronizer. Previous research has used mathematical models to compute light schedules that shift the circadian clock to a new time zone as quickly as possible. How users adjust their behavior when provided with these optimal schedules remains an open question. Here, we report data collected by wearables from more than 100 travelers as they cross time zones using a smartphone app, Entrain . We find that people rarely follow the optimal schedules generated through mathematical modeling entirely, but travelers who better followed the optimal schedules reported more positive moods after their trips. Using the data collected, we improve the optimal schedule predictions to accommodate real-world constraints. We also develop a scheduling algorithm that allows for the computation of approximately optimal schedules "on-the-fly" in response to disruptions. User burnout may not be critically important as long as the first parts of a schedule aremore »followed. These results represent a crucial improvement in making the theoretical results of past work viable for practical use and show how theoretical predictions based on known human physiology can be efficiently used in real-world settings.« less
4. Predicting circadian phase across populations: a comparison of mathematical models and wearable devices

   https://doi.org/10.1093/sleep/zsab126  

   Huang, Yitong ; Mayer, Caleb ; Cheng, Philip ; Siddula, Alankrita ; Burgess, Helen J ; Drake, Christopher ; Goldstein, Cathy ; Walch, Olivia ; Forger, Daniel B ( May 2021 , Sleep)

   Abstract From smart work scheduling to optimal drug timing, there is enormous potential in translating circadian rhythms research results for precision medicine in the real world. However, the pursuit of such effort requires the ability to accurately estimate circadian phase outside of the laboratory. One approach is to predict circadian phase noninvasively using light and activity measurements and mathematical models of the human circadian clock. Most mathematical models take light as an input and predict the effect of light on the human circadian system. However, consumer-grade wearables that are already owned by millions of individuals record activity instead of light, which prompts an evaluation of the accuracy of predicting circadian phase using motion alone. Here, we evaluate the ability of four different models of the human circadian clock to estimate circadian phase from data acquired by wrist-worn wearable devices. Multiple datasets across populations with varying degrees of circadian disruption were used for generalizability. Though the models we test yield similar predictions, analysis of data from 27 shift workers with high levels of circadian disruption shows that activity, which is recorded in almost every wearable device, is better at predicting circadian phase than measured light levels from wrist-worn devices when processedmore »by mathematical models. In those living under normal living conditions, circadian phase can typically be predicted to within 1 h, even with data from a widely available commercial device (the Apple Watch). These results show that circadian phase can be predicted using existing data passively collected by millions of individuals with comparable accuracy to much more invasive and expensive methods.« less
5. Estimating Representative Group Intrinsic Circadian Period from Illuminance-Response Curve Data

   https://doi.org/10.1177/0748730419886992  

   Stack, Nora ; Zeitzer, Jamie M. ; Czeisler, Charles ; Diniz Behn, Cecilia ( April 2020 , Journal of Biological Rhythms)

   The human circadian pacemaker entrains to the 24-h day, but interindividual differences in properties of the pacemaker, such as intrinsic period, affect chronotype and mediate responses to challenges to the circadian system, such as shift work and jet lag, and the efficacy of therapeutic interventions such as light therapy. Robust characterization of circadian properties requires desynchronization of the circadian system from the rest-activity cycle, and these forced desynchrony protocols are very time and resource intensive. However, circadian protocols designed to derive the relationship between light intensity and phase shift, which is inherently affected by intrinsic period, may be applied more broadly. To exploit this relationship, we applied a mathematical model of the human circadian pacemaker with a Markov-Chain Monte Carlo parameter estimation algorithm to estimate the representative group intrinsic period for a group of participants using their collective illuminance-response curve data. We first validated this methodology using simulated illuminance-response curve data in which the intrinsic period was known. Over a physiological range of intrinsic periods, this method accurately estimated the representative intrinsic period of the group. We also applied the method to previously published experimental data describing the illuminance-response curve for a group of healthy adult participants. We estimated themore »study participants’ representative group intrinsic period to be 24.26 and 24.27 h using uniform and normal priors, respectively, consistent with estimates of the average intrinsic period of healthy adults determined using forced desynchrony protocols. Our results establish an approach to estimate a population’s representative intrinsic period from illuminance-response curve data, thereby facilitating the characterization of intrinsic period across a broader range of participant populations than could be studied using forced desynchrony protocols. Future applications of this approach may improve the understanding of demographic differences in the intrinsic circadian period.« less