Passive mobile sensing and machine learning for the detection of drinking episodes

用于检测饮酒事件的被动移动传感和机器学习

基本信息

批准号：
10555250
负责人：
Kevin Michael King
金额：
$ 14.74万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-02-15 至 2026-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10555250
关键词：
Accelerometer Age Alcohol abuse Alcohol consumption Alcoholic beverage heavy drinker Alcohols Algorithms Award Behavioral Cellular Phone Collection Consultations Data Data Reporting Detection Development Development Plans Diagnosis Ecological momentary assessment Emotions Fostering Frequencies Funding Goals Heavy Drinking Impulsivity Independent Scientist Award Individual Intervention K-Series Research Career Programs Light Link Logistics Machine Learning Mental Health Meta-Analysis Methodology Methods Mining Modeling Moods Outcome Parents Participant Patient Self-Report Persons Population Probability Process Protocols documentation Psychological reinforcement Recovery Reporting Research Research Design Research Personnel Research Proposals Risk Risk Factors Sampling Sleep Social Environment Stress Techniques Telephone Testing Text Messaging Time Training alcohol risk alcohol use disorder analytical method behavior prediction burnout career development data streams design diaries disorder risk drinking drinking behavior experience feature selection handheld mobile device high risk drinking machine learning method marijuana use marijuana user meter mobile sensing mobile sensor open source outcome prediction perceived stress personalized intervention personalized medicine prediction algorithm predictive modeling programs real time monitoring research study sensor stressor substance use temporal measurement theories young adult

项目摘要

PROJECT SUMMARY Ambulatory assessment (AA) techniques (e.g., ecological momentary assessment, daily diaries, experiencing sampling) have provided critical tests of theories about the development of alcohol use disorder (AUD) by identifying within-person processes (such as negative or reinforcement, stress exposure, or social context) that can raise the risk for problem drinking and in turn AUD. AA methods are the leading methodological approach in the push towards personalized medicine because it provides a compelling platform for assessment, diagnosis, real-time monitoring, and just-in-time interventions. However, the current utility of AA for personalized models of AUD risk is limited because risky drinking and the risk factors for it (such as changes in moods, stress, or social contexts) change at different scales of time. In other words, even heavy drinkers may only drink a few times a week, but their emotions, stressors and social contexts change multiple times a day. Current AA methods that rely on self-report data have to sample frequently enough to be sensitive to change, long enough to observe sufficient drinking episodes, and to do so while avoiding participant burnout. Passive mobile sensing, which uses sensors (such as GPS, accelerometer, light meter, etc.) available on most smartphones, has been shown in preliminary studies to predict the probability of drinking episodes, but those studies have used relatively small samples. The present career development award aims to develop the candidate’s expertise in passive mobile sensing and the machine learning methods used to analyze passive mobile sensing data. The research proposal will analyze passive mobile sensing data collected in a large sample of regular drinking and marijuana using young adults (age 18 – 22, n = 500; 95.2% who drink), who will be followed using AA over 8 successive weekends as part of a parent R01 (DA 047247). The research goal is to identify passive mobile sensing models of risk factors for drinking (stress, social contexts, sleep, mood, and impulsive states), as well as the drinking episodes themselves. The candidate will develop expertise in these methods and models that will further the development of a research program aimed at developing person specific models of risk for AUD.

项目概要动态评估 (AA) 技术（例如生态瞬时评估、每日日记、经历采样）为酒精使用障碍的发展理论提供了关键的检验（AUD）通过识别人内过程（例如负面或强化、压力暴露或社交）背景）这会增加饮酒问题的风险，而 AA 方法是主要的方法。推动个性化医疗的方法论方法，因为它提供了一个引人注目的平台然而，目前的实用性是用于评估、诊断、实时监测和及时干预。 AUD 风险个性化模型的 AA 是有限的，因为危险饮酒及其风险因素（例如情绪、压力或社会环境的变化）会在不同的时间尺度上发生变化。饮酒者可能每周只喝几次酒，但他们的情绪、压力源和社会环境会发生多重变化目前依赖于自我报告数据的 AA 方法必须足够频繁地采样才能变得敏感。改变，足够长的时间以观察足够的饮酒事件，并在避免参与者的同时这样做被动移动传感，使用可用的传感器（如 GPS、加速计、测光计等）。初步研究表明，在大多数智能手机上，可以预测饮酒事件的概率，但这些研究使用的样本相对较小，目前的职业发展奖旨在发展。候选人在被动移动传感方面的专业知识以及用于分析的机器学习方法该研究提案将分析收集的被动移动传感数据。使用年轻人定期饮酒和吸食大麻的大样本（18 – 22 岁，n = 500；95.2% 饮酒），作为家长 R01 (DA 047247) 的一部分，我们将连续 8 个周末使用 AA 进行跟踪。目标是确定饮酒风险因素（压力、社会环境、睡眠、情绪和冲动状态），以及饮酒事件本身。这些方法和模型的专业知识将进一步发展旨在开发针对澳元的个人特定风险模型。