Multi-scale modeling of sleep behaviors in social networks

社交网络中睡眠行为的多尺度建模

• 批准号: 8453066
• 负责人: Charles A Czeisler
• 金额: $ 49.43万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-18 至 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8453066
• 关键词: Adolescent; Affect; Behavior; Behavioral; Biological; Biological Process; Caffeine; Car Phone; Cardiovascular Diseases; Circadian Rhythms; Cognitive; Cues; Data; Development; Devices; Diabetes Mellitus; Drug usage; Electronics; Fatigue; Habits; Health; Hormones; Human; Individual; Individual Differences; Inpatients; Internet; Learning; Length; Life; Light; Measures; Melatonin; Metabolic; Methods; Modeling; Moods; Nature; Network-based; Obesity; Outcome; Outpatients; Participant; Pattern; Pattern Recognition; Performance; Pharmaceutical Preparations; Physiological; Physiology; Population; Population Heterogeneity; Property; Recovery; Risk-Taking; Schedule; Simulate; Skin Temperature; Sleep; Sleep Deprivation; Social Interaction; Social Network; Statistical Models; Stimulus; Stress; Students; System; Techniques; Teenagers; Testing; Time; Weight Gain; Wit; Work; awake; base; cognitive function; cohort; computerized data processing; immune function; improved; innovation; insight; mathematical model; multi-scale modeling; network models; public health relevance; research study; response; sensor; sleep onset; social; tool; undergraduate student; young adult

DESCRIPTION (provided by applicant): 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.

描述（由申请人提供）：睡眠对广泛的生物功能至关重要。睡眠不足会导致认知能力和情绪受损，并导致肥胖、糖尿病和心血管疾病等不良健康后果。最近的证据表明，睡眠行为可以在社交网络连接的个体之间传播，这些行为甚至可以影响青少年的药物使用。虽然分别存在用于量化社交网络内的连通性和用于对睡眠建模的模型，但是目前没有用于预测和研究社交网络内的睡眠行为的涌现动态的组合模型。 因此，我们建议开发多尺度的基于生理学的模型，社会互动对睡眠行为的影响。我们已经组建了一个跨学科的个人团队，他们：（i）开发了量化社交网络互动的数学方法;（ii）开发了一个基于生理学的睡眠和昼夜生理学模型，包括唤醒刺激和药物的影响;（iii）研究了住院和门诊条件下的健康和病理睡眠行为，包括本科生;（iv）发展收集多种生理和行为变量的技术;及（v）研究模式识别和信号处理技术，以分析多模式数据。 我们将开发统计和数学模型，从8组密切联系的麻省理工学院本科生使用移动的手机和可穿戴传感器收集的实验数据来测量睡眠模式和持续时间，光照，嗜睡和情绪的主观测量，以及社交互动，包括短信，电话，互联网使用和空间接近其他参与者。我们将确定社会互动，睡眠时间和时间，光照，嗜睡和情绪如何相互作用。然后，这些社会互动效应将被添加到我们的生理睡眠和昼夜节律模型中，该模型也将从个体扩展到群体水平，而生理模型结果将为社会网络模型的工作提供信息。 一旦开发出来，数学模型将用于探索涌现动力学如何依赖于网络特性。具体来说，我们将模拟学生网络，包括观察到的社交互动的速率和影响。然后，我们将测试修改网络属性的效果，包括相互作用的强度和群体异质性的程度（模型参数变异性）。 我们期望在这个项目中开发的数学模型将提供一种新的方法来预测社交网络内的睡眠行为的动态。由于其多尺度性质，该模型将在网络层面上的观察与个体之间的相互作用联系起来。这将使我们能够模拟干预具有不健康睡眠行为的人群的候选策略。鉴于美国睡眠不足的情况惊人增加，以及社交媒体的迅速升级，建立可用于改善睡眠行为的模型可能会改善多种健康结果。