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1. A Computational Framework for Modeling Biobehavioral Rhythms from Mobile and Wearable Data Streams

   https://doi.org/10.1145/3510029  

   Yan, Runze; Liu, Xinwen; Dutcher, Janine; Tumminia, Michael; Villalba, Daniella; Cohen, Sheldon; Creswell, David; Creswell, Kasey; Mankoff, Jennifer; Dey, Anind; et al (June 2022, ACM Transactions on Intelligent Systems and Technology)

   This paper presents a computational framework for modeling biobehavioral rhythms - the repeating cycles of physiological, psychological, social, and environmental events - from mobile and wearable data streams. The framework incorporates four main components: mobile data processing, rhythm discovery, rhythm modeling, and machine learning. We evaluate the framework with two case studies using datasets of smartphone, Fitbit, and OURA smart ring to evaluate the framework’s ability to (1) detect cyclic biobehavior, (2) model commonality and differences in rhythms of human participants in the sample datasets, and (3) predict their health and readiness status using models of biobehavioral rhythms. Our evaluation demonstrates the framework’s ability to generate new knowledge and findings through rigorous micro- and macro-level modeling of human rhythms from mobile and wearable data streams collected in the wild and using them to assess and predict different life and health outcomes. 

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2. Towards a Computational Framework for Automated Discovery and Modeling of Biological Rhythms from Wearable Data Streams

   https://doi.org/10.1007.978-3-030-82199-9_44  

   Yan, Runze Doryab (January 2021, Proceedings of SAI Intelligent Systems Conference)

   Modeling biological rhythms helps understand the complex principles behind the physical and psychological abnormalities of human bodies, to plan life schedules, and avoid persisting fatigue and mood and sleep alterations due to the desynchronization of those rhythms. The first step in modeling biological rhythms is to identify their characteristics, such as cyclic periods, phases, and amplitude. However, human rhythms are susceptible to external events, which cause irregular fluctuations in waveforms and affect the characterization of each rhythm. In this paper, we present our exploratory work towards developing a computational framework for automated discovery and modeling of human rhythms. We first identify cyclic periods in time series data using three different methods and test their performance on both synthetic data and real fine-grained biological data. We observe consistent periods are detected by all three methods. We then model inner cycles within each period by identifying change points to observe fluctuations in biological data that may inform the impact of external events on human rhythms. The results provide initial insights into the design of a computational framework for discovering and modeling human rhythms. 

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3. 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)

   null (Ed.)

   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 processed 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. 

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4. Unraveling the interplay of circadian rhythm and sleep deprivation on mood: A Real-World Study on first-year physicians

   https://doi.org/10.1371/journal.pdig.0000439  

   Shapiro, Benjamin; Fang, Yu; Sen, Srijan; Forger, Daniel (January 2024, PLOS Digital Health)

   Pani, Danilo (Ed.)

   The interplay between circadian rhythms, time awake, and mood remains poorly understood in the real-world. Individuals in high-stress occupations with irregular schedules or nighttime shifts are particularly vulnerable to depression and other mood disorders. Advances in wearable technology have provided the opportunity to study these interactions outside of a controlled laboratory environment. Here, we examine the effects of circadian rhythms and time awake on mood in first-year physicians using wearables. Continuous heart rate, step count, sleep data, and daily mood scores were collected from 2,602 medical interns across 168,311 days of Fitbit data. Circadian time and time awake were extracted from minute-by-minute wearable heart rate and motion measurements. Linear mixed modeling determined the relationship between mood, circadian rhythm, and time awake. In this cohort, mood was modulated by circadian timekeeping (p<0.001). Furthermore, we show that increasing time awake both deteriorates mood (p<0.001) and amplifies mood’s circadian rhythm nonlinearly. These findings demonstrate the contributions of both circadian rhythms and sleep deprivation to underlying mood and show how these factors can be studied in real-world settings using Fitbits. They underscore the promising opportunity to harness wearables in deploying chronotherapies for psychiatric illness. 

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5. Efficient assessment of real-world dynamics of circadian rhythms in heart rate and body temperature from wearable data

   https://doi.org/10.1098/rsif.2023.0030  

   Kim, Dae Wook; Mayer, Caleb; Lee, Minki P; Choi, Sung Won; Tewari, Muneesh; Forger, Daniel B (August 2023, Journal of The Royal Society Interface)

   Laboratory studies have made unprecedented progress in understanding circadian physiology. Quantifying circadian rhythms outside of laboratory settings is necessary to translate these findings into real-world clinical practice. Wearables have been considered promising way to measure these rhythms. However, their limited validation remains an open problem. One major barrier to implementing large-scale validation studies is the lack of reliable and efficient methods for circadian assessment from wearable data. Here, we propose an approximation-based least-squares method to extract underlying circadian rhythms from wearable measurements. Its computational cost is ∼ 300-fold lower than that of previous work, enabling its implementation in smartphones with low computing power. We test it on two large-scale real-world wearable datasets: $\sim 600\hspace{0.17em}\mathrm{days}$of body temperature data from cancer patients and ∼ 184 000 days of heart rate and activity data collected from the ‘Social Rhythms’ mobile application. This shows successful extraction of real-world dynamics of circadian rhythms. We also identify a reasonable harmonic model to analyse wearable data. Lastly, we show our method has broad applicability in circadian studies by embedding it into a Kalman filter that infers the state space of the molecular clocks in tissues. Our approach facilitates the translation of scientific advances in circadian fields into actual improvements in health. 

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