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.
Healthy and unhealthy sleep behaviors can be transmitted by social interactions between individuals within social networks. Using multimodal data collected from 8 cohorts of MIT undergraduates, we will develop the first statistical and multi-scale mathematical models of sleep dynamics within social networks based on sleep and circadian physiology. These models will provide insights into the emergent dynamics of sleep behaviors within social networks, and allow us to test the effects of candidate strategies for intervening in populations with unhealthy sleep behaviors.
|Bianchi, Matt T; Phillips, Andrew J K; Wang, Wei et al. (2017) Statistics for Sleep and Biological Rhythms Research. J Biol Rhythms 32:7-17|
|Klerman, Elizabeth B; Wang, Wei; Phillips, Andrew J K et al. (2017) Statistics for Sleep and Biological Rhythms Research. J Biol Rhythms 32:18-25|
|McHill, Andrew W; Klerman, Elizabeth B; Slater, Bridgette et al. (2017) The Relationship Between Estrogen and the Decline in Delta Power During Adolescence. Sleep 40:|
|Swaminathan, Krithika; Klerman, Elizabeth B; Phillips, Andrew J K (2017) Are Individual Differences in Sleep and Circadian Timing Amplified by Use of Artificial Light Sources? J Biol Rhythms 32:165-176|
|Bermudez, Eduardo B; Klerman, Elizabeth B; Czeisler, Charles A et al. (2016) Prediction of Vigilant Attention and Cognitive Performance Using Self-Reported Alertness, Circadian Phase, Hours since Awakening, and Accumulated Sleep Loss. PLoS One 11:e0151770|
|Bendrick, Gregg A; Beckett, Scott A; Klerman, Elizabeth B (2016) Human fatigue and the crash of the airship Italia. Polar Res 35:|
|Klerman, Elizabeth B; Beckett, Scott A; Landrigan, Christopher P (2016) Applying mathematical models to predict resident physician performance and alertness on traditional and novel work schedules. BMC Med Educ 16:239|
|Sano, Akane; Yu, Amy Z; McHill, Andrew W et al. (2015) Prediction of Happy-Sad mood from daily behaviors and previous sleep history. Conf Proc IEEE Eng Med Biol Soc 2015:6796-9|
|Sano, Akane; Phillips, Andrew J; Yu, Amy Z et al. (2015) Recognizing Academic Performance, Sleep Quality, Stress Level, and Mental Health using Personality Traits, Wearable Sensors and Mobile Phones. Int Conf Wearable Implant Body Sens Netw 2015:|
|Vijayan, Sujith; Klerman, Elizabeth B; Adler, Gail K et al. (2015) Thalamic mechanisms underlying alpha-delta sleep with implications for fibromyalgia. J Neurophysiol 114:1923-30|
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