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.

概括： 不健康的饮酒植入人的日常生活中，但是很难学习 实验室和治疗办公室以外的人。许多人从事过多的酒精 消费直到对其进行巨大的，有时是灾难性的，负面影响之前的治疗 他们的生活。手机应用程序和社交媒体在谨慎的同意和隐私方面提供了途径 在生态背景下基于行为的大规模研究。该建议旨在开发 餐厅现实环境中对不健康饮酒的研究和预测 行业，超过饮酒的人口非常普遍。使用创新和 严格的数据科学技术，我们将研究不健康的饮酒与（a）情感状态之间的横断面，前瞻性纵向和基于社区的关系，（b）压力，以及（c）两种类型的 同理心：耗尽和有益。在此过程中，我们将：（1）建立一个大型且安全的数字注册表 有关饮酒行为​​的移动数据（n = 5,925），（2）评估现有的数据驱动评估 心理状态，（3）使用机器学习来改善对心理状态的评估并预测 未来的饮酒行为以及（4）迄今为止对关系的最大规模研究之一 在心理状态与不健康的饮酒之间。 我们的具体目的包括：（1）自动评估不健康饮酒的关联 基于语言的餐饮业工人的情感，压力和同情 社交媒体和文字消息的行为； （2）开发一个移动应用程序，用于纵向集合 细粒度的日常心理健康，以分析与前瞻性预测模型的关系 日常饮酒方式； （3）检查社区的影响，压力，同理心和开放式视频计 因素，由公共社交媒体上数百万本地帖子代表，并评估他们的关系 为餐饮行业工人提供个人饮酒行为。每个目标都包括发展 计算研究工具和特定假设的测试。构造范围从具有 关于不健康的饮酒（情绪状态）的广泛文学，对迅速发展和 对高度新颖的研究（压力）冲突（压力）（同理心）。我们有丰富的经验 收集数据并开发应用程序，包括在招聘调酒师和服务器方面的初步工作。有关的 研究和我们的初步工作已经表明，不健康的饮酒之间存在牢固的联系 和数字语言数据。我们将在使用教学教程进行的开源许可下发布我们的软件工具 - 应用程序平台和预测模型。我们认为这项工作是宽泛的 用于基于数据科学语言的评估的用例，以研究不健康的饮酒。