Predictive Models of Alcohol Consumption

酒精消费的预测模型

基本信息

批准号：
7976057
负责人：
GEORGIY BOBASHEV
金额：
$ 8.77万
依托单位：
RESEARCH TRIANGLE INSTITUTE
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-08-01 至 2012-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7976057
关键词：
Alcohol abuse Alcohol consumption Alcohol or Other Drugs use Alcoholic beverage heavy drinker Alcohols Arts Automobile Driving Awareness Behavior Biological Neural Networks Calendar Characteristics Clinical Consumption DSM-IV Data Data Collection Data Quality Data Set Dependence Discipline Economics Epidemiology Family Friends Future Gender General Population Holidays Individual Internet Intervention Linear Regressions Methodological Studies Methodology Modeling Non-linear Models Output Patient Self-Report Patients Pattern Physics Public Health Records Relapse Relative (related person)Reporting Research Research Personnel Sampling Schedule Series Statistical Models Stress Technology Testing Time Validation Woman Work addiction alcohol abuse therapy alcohol research base binge drinker cost disorder later incidence prevention drinking innovation insight meetings men motivational enhancement therapy pharmacokinetic model predictive modeling prevent public health relevance response sobriety stability testing success theories time use treatment program voice recognition

项目摘要

DESCRIPTION (provided by applicant): The objective of this methodological study is to develop and systematically test predictive models of an individual's alcohol consumption. The ability to forecast alcohol consumption in patients can potentially help individualized treatment programs. For binge drinkers, predicting the timing of their next episode provides additional awareness that could help to control, or even to prevent, the entire episode. For heavy drinkers, predictive models can forecast the windows of treatment opportunity when the patient is most sober and responsive to intervention. For those who are attempting to quit drinking, forecasting the next relapse episode could be used to trigger motivational interviewing or other timely interventions that will help prevent the relapse. We will analyze two data sets containing long time series (2 years and 6 months) of individual daily records of alcohol use, stress, and other factors. We will adapt innovative predictive models developed to forecast future alcohol consumption as well as identify and explain a variety of daily use patterns. The data on individual alcohol consumption is very limited, and this is the first study aiming to build forecasting models using such data. This study offers the field a 'next step' with an innovative analysis approach that can possibly offer clinical implications for relapse prevention, increased treatment efficiency, and enhanced understanding of the factors driving the variety of daily patterns of use. PUBLIC HEALTH RELEVANCE: The data on daily individual alcohol consumption is critical for understanding alcohol abuse/addiction. This study is the first one to build forecasting models using such data. This study offers the field a 'next step' with an innovative analysis approach that can possibly offer clinical implications for relapse prevention, increased treatment efficiency, and enhanced understanding of the factors driving the variety of daily patterns of use.

描述（由申请人提供）：这项方法论研究的目的是开发和系统地测试个人饮酒的预测模型。预测患者饮酒的能力可以有可能帮助个性化的治疗计划。对于狂热的饮酒者，预测下一集的时机提供了更多的意识，可以帮助控制甚至预防整个情节。对于大量饮酒者来说，预测模型可以预测患者最清醒和对干预措施的反应时，治疗机会的窗户。对于那些试图退出饮酒的人，预测下一个复发情节可以用于触发动机面试或其他及时的干预措施，这将有助于防止复发。我们将分析两个数据集，其中包含长时间序列（2年零6个月）的单个酒精使用，压力和其他因素的记录。我们将适应开发的创新预测模型，以预测未来的饮酒，并识别和解释各种日常使用模式。关于个人饮酒的数据非常有限，这是第一个旨在使用此类数据构建预测模型的研究。这项研究通过创新分析方法为领域提供了“下一步”，该方法可能会为预防复发，提高治疗效率以及对推动每日使用方式种类繁多的因素的理解提供临床意义。公共卫生相关性：关于每日个人饮酒的数据对于了解酒精滥用/成瘾至关重要。这项研究是第一个使用此类数据构建预测模型的研究。这项研究通过创新分析方法为领域提供了“下一步”，该方法可能会为预防复发，提高治疗效率以及对推动每日使用方式种类繁多的因素的理解提供临床意义。