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Multi-scale modeling of sleep behaviors in social networks


Sleep is critical to a wide range of biological functions. Inadequate sleep results in impaired cognitive performance and mood, and adverse health outcomes including obesity, diabetes, and cardiovascular disease. Recent evidence suggests that sleep behaviors can spread between individuals connected by a social network and that these behaviors can even influence drug use in teenagers. While models exist separately for quantifying connectivity within social networks and for modeling sleep, there are currently no combined models for predicting and studying the emergent dynamics of sleep behaviors within social networks. We therefore propose to develop multi-scale physiologically-based models of the effects of social interactions on sleep behaviors. We have assembled a trans-disciplinary team of individuals who have: (i) developed mathematical methods for quantifying social network interactions; (ii) developed a physiologically based model of sleep and circadian physiology, including the effects of wake-promoting stimuli and drugs; (iii) studied healthy and pathological sleep behaviors under inpatient and outpatient conditions, including in undergraduate students; (iv) developed techniques for collecting multiple physiological and behavioral variables; and (v) studied pattern recognition and signal processing techniques for analyzing multimodal data. We will develop statistical and mathematical models from experimental data collected from 8 groups of closely-connected MIT undergraduates using mobile phones and wearable sensors to measure sleep patterns and duration, light exposure, subjective measures of sleepiness and mood, and social interactions including texting, calls, internet use, and spatial proximity to other participants. We will determine how social interactions, sleep duration and timing, light exposure, sleepiness and mood interact. These social interaction effects will then be added to our physiological sleep and circadian model, which will also be extended from the individual to the population level, while the physiological model results will inform the social network model work. Once developed, the mathematical model will be used to explore how emergent dynamics depend on network properties. Specifically, we will simulate the student network, including the observed rates and effects of social interactions. We will then test the effects of modifying the network properties, including the strengths of interactions and the degree of population heterogeneity (model parameter variability). We anticipate that the mathematical model developed in this project will provide a new means of predicting the dynamics of sleep behaviors within social networks. Due to its multi-scale nature, the model will relate observations at the network level to interactions between individuals. This will allow us to simulate candidate strategies for intervening in populations wit unhealthy sleep behaviors. Given the alarming increase in insufficient sleep in the U.S., and the rapidly escalating use of social media, establishing models that can be used to improve sleep behaviors could potentially improve multiple health outcomes.

Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.