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.

吸毒暴露和社会心理压力的评估很复杂，因为它们往往是短暂的且难以准确回忆。由于因果关系的复杂性和环境构成的难以捉摸的性质，对它们之间的因果关系以及它们的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）近乎实时地评估药物使用和社会心理压力，其中参与者使用手持式电子日记记录发生的事件，并报告最近或正在进行的事件以响应随机定时的事件。全天提示。我们还通过让参与者携带全球定位系统 (GPS) 记录器以几米的空间分辨率跟踪他们的行踪，来维护所报告事件发生地点的实时记录。我们在称为地理瞬时评估 (GMA) 的方法中集体使用这些数据。我们 GMA 的目标与了解发生毒品相关行为的巴尔的摩的具体地点无关，而与获得关于活动空间（发生日常活动的空间）如何与此类行为及其诱因相关的一般性知识有关。 我们已经完成了 GMA 方法的初步试点研究。 我们在接受美沙酮维持治疗的阿片类药物依赖型多种药物使用者醒着的时间内随机收集了 16 周内的带时间戳的 GPS 数据和 EMA 情绪、压力和药物渴望评级。在绘制每个 EMA 条目之前的 12 小时内，计算 EMA 条目的位置和参与者的旅行轨迹。在整个社区和 12 小时赛道水平上评估了主观评级和客观环境评级之间的关联。参与者 (N=27) 遵守 GMA 数据收集； 3,711 个随机提示的 EMA 条目与特定位置相匹配。在社区层面，身体障碍与负面情绪、压力以及对海洛因和可卡因的渴望呈负相关（ps <.0001 至 .0335）；药物活性与压力、海洛因和可卡因的渴望呈负相关（ps .0009 至 .0134）。在 EMA 条目之前的 12 小时内，受访者轨迹周围的环境也发现了类似的关系。结果支持了GMA的可行性。 社区特征和参与者报告之间的关系是违反直觉和反假设的，并挑战了一些关于表面压力环境如何与生活经历相关以及此类环境如何最终损害健康的假设。 GMA 方法可能适用于制定个人或社区级别的干预措施。 我们正在继续在更多的阿片类药物/可卡因使用者中开展阿片类药物激动剂维持治疗的工作。这个更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。 我们还在开发更复杂的方法来在特定的评分位置之间插入观察者评分，更好的方法来根据原始观察者评分生成分数和地图，以及更敏感的方法来建模瞬时环境与情绪、渴望和压力之间的关联，我们的参与者数量较多的优势。 作为这项更大型试验的一部分，我们正在与 AutoSense 的开发人员合作，这是一种无线传感器系统，通过将数据传输到智能手机的生物传感器来连续测量心率、心率变异性、呼吸、皮肤电导、环境温度和身体活动。我们的研究和其他 AutoSense 合作的数据被用来开发可以检测吸毒、吸烟和压力的算法。我们在门诊美沙酮维持期间对 40 名多种药物使用者进行了 AutoSense 现场测试，他们佩戴 AutoSense 4 次，为期一周，在此期间，他们还自我报告了日常生活中的药物使用情况、压力事件、情绪和手持设备上的活动。每周进行3次尿液药物筛查。 AutoSense 的合规性和可接受性良好；无线心电图数据产出率是可以接受的（85.7%）。与佩戴 AutoSense 1 周的 30 名学生（吸烟者和社交饮酒者）相比，我们参与者的依从性更好。 我们的动态生理监测（目前使用 AutoSense，但肯定会随着生物传感技术的改进而发展）的主要目标之一是实时检测药物使用情况，同时最大限度地减轻患者的报告负担。我们与 AutoSense 合作者一起建立了原理证明。 主要挑战是开发生理学计算模型（例如，用于推断可卡因使用事件），该模型可以使用动态心电图在自然环境中可靠地工作。我们与合作者一起分析了在现场收集的 AutoSense 数据以及来自可卡因管理实验室研究的 AutoSense 数据。利用这些数据，我们开发了有效的方法来筛选和清理心电图时间序列数据，并根据 EMA 自我报告提取候选时间窗口。由此产生的模型在每天超过 11 小时的现场数据中实现了 100% 的真阳性率，同时将假阳性率保持在 1.13/天。这是迈向移动健康干预关键组成部分的重要一步，可以防止疾病复发。