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: 10415982
• 负责人: Hansen Andrew Schwartz
• 金额: $ 60.96万
• 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/10415982
• 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; Persons; 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.

总结： 不健康的酒精消费是嵌入在人们的日常生活中，但很难研究 实验室和治疗办公室以外的个人。许多人过度饮酒 消费不会使它的治疗，直到它有大的，有时是灾难性的，负面影响， 他们的生活.移动的手机应用程序和社交媒体，在尊重同意和隐私的情况下， 在生态环境中进行大规模的行为研究。该提案旨在开发技术， 研究和预测餐厅真实环境中的不健康酒精消费 工业，过度饮酒非常普遍的人群。利用创新和 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 同理心：消耗和有益的。在这个过程中我们将：（1）建立一个大的和安全的数字注册表 关于饮酒行为的移动的数据（N = 5，925），（2）评价现有的数据驱动评估， 心理状态，（3）使用机器学习来改善心理状态的评估并预测 未来的饮酒行为，以及（4）进行最大规模的研究之一，到目前为止， 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）自动评估不健康的酒精消费的关联 影响，压力，和同情心的餐饮业工人基于他们的语言 （2）开发一个移动的应用程序，用于纵向收集 精细的日常心理健康，以分析与建立前瞻性预测模型的关系 日常饮酒模式;（3）检查社区影响，压力，同理心和开放词汇 因素，以公共社交媒体上数百万个本地帖子为代表，并评估它们的关系 餐饮业员工的个人饮酒行为。每个目标都包括发展 计算研究工具和特定假设的测试。构造的范围从具有 关于不健康饮酒（情绪状态）的大量文献，对于那些新兴的， 矛盾的研究（压力），到那些高度新颖的研究（同理心）。我们拥有丰富的经验， 收集数据和开发应用程序，包括招聘调酒师和服务员的初步工作。相关 研究和我们的初步工作已经表明， 和数字语言数据。我们将发布我们的软件工具--应用平台和预测模型--在开源许可证下，附带教学教程。我们认为这项工作是一个广泛的 基于数据科学语言的评估用例，以研究不健康的饮酒。