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

   https://doi.org/10.1101/2020.08.10.244020  

   Yan, Runze ( January 2021 , ACM transactions on intelligent systems and technology)

   This paper presents the first 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 smartphones, Fitbit, and OURA smart ring to evaluate the framework's ability to 1) detect cyclic biobehavioral, 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. Burnout Prediction and Analysis in Shift Workers: Counterfactual Explanation Approach

   Tang, Ziang ; King, Zachary ; Choto Segovia Alicia ; Yu, Han ; Braddock, Gia ; Ito, Asami ; Sakamoto, Ryota ; Shimaoka, Motomu ; Sano, Akane ( November 2023 , IEEE)

   Shift work disrupts sleep and causes chronic stress, resulting in burnout syndrome characterized by emotional exhaustion, depersonalization, and decreased personal accomplishment. Continuous biometric data collected through wearable devices contributes to mental health research. However, direct prediction of burnout risk is still limited, and interpreting machine learning (ML) models in healthcare poses challenges. In this paper, we develop machine learning models that utilize wearable and survey data, including rhythm features, to predict burnout risk among shift workers. Additionally, we employ the DiCE (Diverse Counterfactual Explanations) framework to generate interpretable explanations for the ML model, aiding in the management of burnout risks. Our experiments on the AMED dataset show that incorporating rhythm features significantly enhances the predictive performance of our models. Specifically, sleep and heart rate features have emerged as significant indicators for accurately predicting burnout risk 

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5. 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 in 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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