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; 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）：睡眠对于广泛的生物学功能至关重要。睡眠不足会导致认知表现和情绪受损，以及包括肥胖，糖尿病和心血管疾病在内的不良健康结果。最近的证据表明，睡眠行为可以在社交网络联系的个体之间传播，这些行为甚至可以影响青少年的药物使用。尽管模型分别存在用于量化社交网络内的连通性和建模睡眠，但目前尚无组合模型来预测和研究社交网络中睡眠行为的新兴动态。 因此，我们建议开发社会互动对睡眠行为的影响的多尺度生理模型。我们组建了一个由个人组成的跨学科团队：（i）开发了用于量化社交网络互动的数学方法； （ii）开发了一种基于生理的睡眠和昼夜节律生理学模型，包括启动刺激性刺激和药物的影响； （iii）在住院和门诊疾病（包括本科生）下研究了健康和病理睡眠行为； （iv）开发了用于收集多个生理和行为变量的技术； （v）研究了用于分析多模式数据的模式识别和信号处理技术。 我们将使用手机和可穿戴传感器从8组紧密连接的MIT本科生收集的实验数据中开发统计和数学模型，以测量睡眠方式和持续时间，持续时间，轻度暴露，嗜睡和情绪的主观衡量以及社交互动，包括发短信，互联网，互联网，互联网，互联网，互联网，互联网，互联网，和与其他参与者的空间统治。我们将确定社交互动，睡眠持续时间和时机，轻度暴露，嗜睡和情绪相互作用。然后，这些社交互动效应将被添加到我们的生理睡眠和昼夜节律模型中，该模型也将从个人扩展到人群水平，而生理模型结果将为社交网络模型的工作提供信息。 一旦开发，数学模型将用于探索新兴动态如何取决于网络属性。具体来说，我们将模拟学生网络，包括观察到的社交互动的效果和影响。然后，我们将测试修改网络属性的效果，包括相互作用的优势和种群异质性的程度（模型参数变异性）。 我们预计该项目中开发的数学模型将提供一种预测社交网络中睡眠行为动态动态的新方法。由于其多尺度性质，该模型将在网络层面上将观察结果与个人之间的相互作用联系起来。这将使我们能够模拟候选策略，以干预人口不健康的睡眠行为。鉴于美国睡眠不足以及社交媒体迅速升级的使用令人震惊，建立可用于改善睡眠行为的模型可能会改善多种健康状况。