Quantifying Exposure to Illicit Drugs & Psychosocial Stress in Real Time

量化非法药物的暴露程度

• 批准号: 8933830
• 负责人: Kenzie Preston
• 金额: $ 167.95万
• 依托单位: NATIONAL INSTITUTE ON DRUG ABUSE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8933830
• 关键词: Agonist; Algorithms; Baltimore; Behavior; Biosensing Techniques; Biosensor; Characteristics; Cocaine; Cocaine Users; Collaborations; Computer Simulation; Data; Data Collection; Development; Devices; Disease; Drug usage; Electrocardiogram; Electronics; Environment; Environmental Exposure; Event; Exposure to; Galvanic Skin Response; Generations; Genetic; Geographic Locations; Goals; Heart Rate; Heroin; Hour; Illicit Drugs; Impaired health; Individual; Information Systems; Intervention; Knowledge; Laboratory Study; Life Experience; Location; Maintenance; Maps; Measures; Methodology; Methods; Modeling; Moods; Nature; Neighborhoods; Opioid; Outpatients; Participant; Patient Self-Report; Patients; Pharmaceutical Preparations; Physical activity; Physiologic Monitoring; Pilot Projects; Population; Positioning Attribute; Preclinical Drug Evaluation; Psychosocial Stress; Records; Relapse; Reporting; Resolution; Respiration; Respondent; Sampling; Series; Smoker; Smoking; Stress; Stressful Event; Students; System; Technology; Temperature; Testing; Time; Travel; Urine; Wireless Technology; Work; base; cocaine use; craving; diaries; drug craving; heart rate variability; improved; mHealth; meter; methadone maintenance; negative mood; prevent; response; sensor; social

Assessment of exposure to drug use and psychosocial stress is complicated by the fact that each is often transient and difficult to recall accurately. Assessment of their causal connections with one another, and of their genetic and environmental determinants, is complicated by the complexity of the causal connections and by the elusive nature of what constitutes the environment. In this project, we are assessing drug use and psychosocial stress in near-real time through Ecological Momentary Assessment (EMA), in which participants use handheld electronic diaries to record events as they occur and to report recent or ongoing events in response to randomly timed prompts throughout the day. We are also maintaining real-time records of where the reported events occur by having participants carry Global Positioning System (GPS) loggers to track their whereabouts with a spatial resolution of several meters. We use these data collectively in a method we are calling Geographical Momentary Assessment (GMA). Our goal with GMA has little to do with knowing the specific Baltimore locations where drug-related behaviors occur, and everything to do with gaining generalizable knowledge about how activity spaces (the spaces in which daily activities occur) are associated with such behaviors and their precipitants. We have completed an initial pilot study of our GMA methods. We collected time-stamped GPS data and EMA ratings of mood, stress, and drug craving over 16 weeks at randomly prompted times during the waking hours of opioid-dependent polydrug users receiving methadone maintenance. Locations of EMA entries and participants travel tracks were calculated for the 12 hours before each EMA entry were mapped. Associations between subjective ratings and objective environmental ratings were evaluated at the whole neighborhood and 12-hour track levels. Participants (N=27) were compliant with GMA data collection; 3,711 randomly prompted EMA entries were matched to specific locations. At the neighborhood level, physical disorder was negatively correlated with negative mood, stress, and heroin and cocaine craving (ps <.0001 to .0335); drug activity was negatively correlated with stress, heroin and cocaine craving (ps .0009 to .0134). Similar relationships were found for the environments around respondents tracks in the 12 hours preceding EMA entries. The results support the feasibility of GMA. The relationships between neighborhood characteristics and participants reports were counterintuitive and counter-hypothesized, and challenge some assumptions about how ostensibly stressful environments are associated with lived experience and how such environments ultimately impair health. GMA methodology may have applications for development of individual- or neighborhood-level interventions. We are continuing this work in a larger population of opioid/cocaine users in opioid agonist maintenance. This larger sample will enable us to investigate the relationships among individual characteristics and environmental influences on drug use. We are also developing more sophisticated methods for interpolating observer ratings between specific rated locations, better approaches to the generation of scores and maps from raw observer rating scores, and more sensitive approaches to modeling associations between momentary surroundings and mood, craving, and stress, taking advantage of our larger number of participants. As part of this larger trial, we are collaborating with the developers of AutoSense, a wireless sensor system that continuously measures heart rate, heart-rate variability, respiration, skin conductance, ambient temperature, and physical activity with biosensors that transmit data to a smartphone. Data from our study and from other AutoSense collaborations are being used to develop algorithms that can detect drug use, smoking, and stress. We field-tested AutoSense in 40 polydrug users during outpatient methadone maintenance who wore AutoSense for 4 one-week periods, during which they also self-reported drug use, stressful events, mood, and activities on handheld devices as they went about their daily lives. Urine drug screens were conducted 3 times weekly. Compliance with and acceptability of AutoSense was good; rates of wireless ECG data yield were acceptable (85.7%). That compliance of our participants compared favorably with that of a group of 30 students (smokers and social drinkers) who wore AutoSense for 1 week. One major goal of our ambulatory physiological monitoring (currently with AutoSense, but sure to evolve as biosensing technology improves) is to detect drug use in real time with minimal reporting burden for patients. With our AutoSense collaborators, we have established proof of principle. The major challenge was to develop physiologically informed computational models (e.g., for inferring an episode of cocaine use) that can work reliably in natural environments using ambulatory ECG. With our collaborators, we analyed AutoSense data collected in the field and from laboratory studies with administration of cocaine. With these data, we developed efficient methods to screen and clean the ECG time-series data and extract candidate time windows based on EMA self-reports. The resultant model achieved a 100% rate of true positives while keeping false positives to 1.13/day over 11+ hours/day of field data. This is a major step toward a key component of a mobile health intervention, which could prevent a lapse from devolving into a relapse.

每个人通常都是短暂的且难以准确回忆的事实，评估药物使用和社会心理压力的评估变得复杂。评估其因果关系的复杂性以及构成环境的因素的复杂性以及难以捉摸的性质，对它们的因果关系及其遗传和环境决定因素的评估变得复杂。 在这个项目中，我们通过生态瞬时评估（EMA）在近乎真实的时间内评估药物使用和社会心理压力，在该评估中，参与者使用手持电子日记在发生时记录事件，并报告最近或正在进行的事件，以响应全天的随机时间提示。我们还通过让参与者携带全球定位系统（GPS）记录仪来维护有关报告事件发生的情况的实时记录，以通过几米的空间分辨率来跟踪其下落。我们以一种我们称为地理瞬时评估（GMA）的方法共同使用这些数据。我们使用GMA的目标与了解发生与毒品有关的行为的特定位置无关，并且与获得有关活动空间（日常活动的空间）如何与此类行为及其降水物相关的可靠知识有关。 我们已经完成了对GMA方法的初步试点研究。 我们收集了时间戳记的GPS数据和EMA的情绪，压力和药物等级，在16周内，在接收美沙酮维持的阿片类药物依赖性聚剂量用户的醒来时间中随机提示的时间。在映射每个EMA进入之前的12小时内计算了EMA条目和参与者的旅行轨道位置。在整个社区和12小时的轨道水平上评估了主观评分与客观环境评级之间的关联。参与者（n = 27）符合GMA数据收集；将3,711个随机提示的EMA条目与特定位置匹配。在邻里层面上，身体障碍与负面情绪，压力以及海洛因和可卡因渴望呈负相关（ps <.0001至.0335）；药物活性与压力，海洛因和可卡因渴望（ps .0009至.0134）负相关。在EMA条目之前的12小时内，在受访者踪迹周围的环境中发现了类似的关系。结果支持GMA的可行性。 邻里特征与参与者报告之间的关系是违反直觉和反刺激性的，并挑战了表面上压力性环境如何与生活经验以及这种环境最终如何损害健康相关的一些假设。 GMA方法可能会应用于开发个人或社区水平干预措施。 我们正在在阿片类激动剂维护中继续在较大的阿片类药物/可卡因使用者中继续这项工作。这个较大的样本将使我们能够研究个体特征和环境对药物使用的影响之间的关系。 我们还开发了更复杂的方法，用于在特定额定位置之间插值观察者等级，从原始观察者等级分数中获得更好的分数和地图的方法，以及更敏感的方法来建模瞬时环境和情绪，渴望，压力之间的关联，并利用我们更多的参与者数量。 作为这项较大试验的一部分，我们正在与AutoSense的开发人员合作，AutoSense是一种无线传感器系统，该系统连续测量心率，心率变异性，呼吸，皮肤电导，环境温度以及将数据传输到智能手机的生物传感器。我们的研究和其他自动识别合作的数据被用于开发可以检测吸毒，吸烟和压力的算法。在门诊美沙酮维护期间，我们对40名Polydrug使用者进行了自动训练，他们穿着4周的自动赛，在此期间，他们还自我报告了药物使用，压力性事件，情绪和活动，因为他们在日常生活中进行了手持设备。每周进行3次尿液筛查。对自动志的遵守和可接受性很好；无线ECG数据产量的速率是可以接受的（85.7％）。我们参与者的遵守情况与戴自动训练1周的30名学生（吸烟者和社交饮酒者）的小组的遵守情况进行了比较。 我们门诊生理监测（目前具有自动锻炼，但肯定会随着生物传感技术的改善而发展）的一个主要目标是实时检测药物使用，患者的报告负担最小。借助我们的自动合作者，我们建立了原则证明。 主要的挑战是开发生理知情的计算模型（例如，用于推断可卡因的使用事件），可以在自然环境中使用Absuratory ECG可靠地工作。与我们的合作者一起，我们通过可卡因给药分析了该领域和实验室研究中收集的自动义数据。有了这些数据，我们开发了有效的方法来筛选和清洁ECG时间序列数据，并根据EMA自我报告提取候选时间窗口。最终的模型达到了100％的真实阳性率，同时将假阳性在11个以上的现场数据中保持1.13/天。这是迈向移动健康干预措施的关键组成部分的重要一步，这可能会阻止逐渐转化为复发。