Large-scale Data Scientific Assessment of Unhealthy Alcohol Consumption Among Front-Line Restaurant Workers

大数据科学评估一线餐厅员工不健康饮酒情况

• 批准号: 10171732
• 负责人: Hansen Andrew Schwartz
• 金额: $ 64.86万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10171732
• 关键词: Affect; Affective; Alcohol consumption; Behavior; Car Phone; Caring; Collection; Communities; Conflict (Psychology); Consent; Cross-Sectional Studies; Data; Data Science; Development; Ecological momentary assessment; Emotional; Empathy; Employee; Environment; Future; Grain; Heavy Drinking; Individual; Industry; Instruction; Laboratories; Language; Licensing; Life; Linguistics; Link; Literature; Machine Learning; Mental Health; Occupations; Pattern; Peer Review; Personality; Population; Populations at Risk; Privacy; Process; Psychiatry; Psychology; Registries; Research; Research Personnel; Restaurants; Risk Assessment; Risk Factors; Secure; Services; Software Tools; Stress; Surveys; Techniques; Testing; Text Messaging; Vocabulary; Work; alcohol availability; base; computer science; design; digital; drinking; drinking behavior; experience; improved; innovation; interest; large scale data; mobile application; multidisciplinary; novel; open source; predictive modeling; prospective; psychologic; recruit; social; social media; statistics; tool

Summary: Unhealthy alcohol consumption is embedded within people’s everyday lives, but it is difficult to study individuals outside of laboratories and treatment offices. Many individuals engaging in excessive alcohol consumption do not make it to treatment until it has had large, sometimes catastrophic, negative effects on their life. Mobile phone apps and social media, with care taken for consent and privacy, offer an avenue for large-scale behavior-based study within an ecological context. This proposal seeks to develop techniques for the study of and prediction of unhealthy alcohol consumption within the real-world context of the restaurant industry, a population where excessive alcohol consumption is highly prevalent. Using innovative and rigorous data science techniques, we will study the cross-sectional, prospective longitudinal, and community-based relationships between unhealthy drinking and (a) affective states, (b) stress, and (c) two types of empathy: depleting and beneficial. In the process we will: (1) build a large and secure registry of digital mobile data (N = 5,925) about drinking behavior, (2) evaluate existing data-driven assessments of psychological states, (3) use machine learning to improve assessments of psychological states and predict future drinking behavior, and (4) perform one of the largest scale studies, to date, of the relationship between psychological state and unhealthy drinking. Our specific aims include: (1) Automatically assess the association of unhealthy alcohol consumption with affect, stress, and empathy among restaurant industry workers based on their linguistic behavior in social media and text messaging; (2) Develop a mobile app for longitudinal collection of fine-grained daily psychological health to analyze relation to and build prospective predictive models of daily drinking patterns; (3) Examine community affect, stress, empathy, and open-vocabulary factors, as represented by millions of local posts on public social media and assess their relationship to individual drinking behavior for restaurant industry workers. Each aim includes both the development of computational research tools and the testing of specific hypotheses. Constructs range from those with an extensive literature with respect to unhealthy drinking (emotional states), to those with burgeoning and conflicting research (stress), to those that are highly novel (empathy). We have extensive experience in collecting data and developing apps, including preliminary work at recruiting bartenders and servers. Related research and our preliminary work already suggests that there are strong links between unhealthy drinking and digital language data. We will release our software tools -- the app platform and predictive models -- under open source licenses accompanied with instructional tutorials. We see this work as trail-blazing a broad use-case for data scientific language-based assessments to study unhealthy drinking.

摘要： 不健康的饮酒根植于人们的日常生活中，但很难研究 实验室和治疗办公室以外的个人。许多人酗酒 消费直到对以下方面产生了巨大的、有时是灾难性的负面影响时才能得到治疗 他们的生活。手机应用程序和社交媒体，在考虑到同意和隐私的情况下，为 生态背景下的大规模行为研究。这项提议寻求开发技术，以 在餐厅的真实环境中对不健康饮酒的研究和预测 工业，过度饮酒非常普遍的人群。使用创新和 严格的数据科学技术，我们将研究不健康饮酒与(A)情感状态、(B)压力和(C)两种类型的饮酒之间的横截面、前瞻性纵向和基于社区的关系 移情：耗尽而有益。在这个过程中，我们将：(1)建立一个大型和安全的数字注册中心 有关饮酒行为的移动数据(N=5,925)，(2)评估现有数据驱动的评估 心理状态，(3)使用机器学习来改进心理状态的评估和预测 未来的饮酒行为，以及(4)进行迄今为止规模最大的关系研究之一 心理状态和不健康饮酒之间的关系。 我们的具体目标包括：(1)自动评估不健康饮酒的关联 餐饮业员工的情感、压力和同理心基于他们的语言 社交媒体和短信中的行为；(2)开发移动应用程序，纵向收集 细粒度日常心理健康状况分析与前瞻性预测模型构建 日常饮酒模式；(3)检查社区情感、压力、同理心和开放词汇 公共社交媒体上数以百万计的当地帖子所代表的因素，并评估它们之间的关系 针对餐饮业从业人员的个人饮酒行为。每个目标都包括发展 计算研究工具和特定假说的检验。构造的范围从具有 大量关于不健康饮酒(情绪状态)的文献，献给那些正在成长和 相互矛盾的研究(压力)，对于那些高度新奇的(同理心)。我们在以下方面有丰富的经验 收集数据和开发应用程序，包括招聘调酒师和服务器的前期工作。相关 研究和我们的初步工作已经表明，不健康的饮酒与 和数字语言数据。我们将在开放源码许可下发布我们的软件工具--应用程序平台和预测模型，并附带教学教程。我们认为这项工作是一项开拓性的工作 用于研究不健康饮酒的数据科学语言评估的用例。