Quantifying Exposure to Illicit Drugs & Psychosocial Stress in Real Time

对吸毒和社会心理压力的评估是复杂的，因为它们往往都是短暂的，很难准确回忆。由于因果联系的复杂性和构成环境的难以捉摸的性质，对它们彼此之间的因果联系以及它们的遗传和环境决定因素的评估是复杂的。 在这个项目中，我们通过生态瞬间评估(EMA)近乎实时地评估药物使用和心理社会压力，在EMA中，参与者使用手持电子日记记录事件发生时，并根据全天随机定时的提示报告最近或正在发生的事件。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下落，从而保持对报告事件发生地点的实时记录。我们在一种我们称为地理瞬时评估(GMA)的方法中集体使用这些数据。我们与GMA的目标与了解巴尔的摩发生毒品相关行为的具体地点几乎没有关系，而是要获得关于活动空间(发生日常活动的空间)如何与此类行为及其沉淀物相关联的概括性知识。 我们已经完成了对我们的GMA方法的初步初步研究。我们收集了在接受美沙酮维持治疗的阿片依赖多药使用者醒着的16周内，随机提示的时间段的时间戳GPS数据和EMA情绪、压力和药物渴求的评级。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。主观评级和客观环境评级之间的关联在整个社区和12小时轨道水平上进行了评估。参与者(N=27)遵守GMA数据收集；3711个随机提示的EMA条目与特定位置匹配。在社区层面，躯体障碍与消极情绪、压力、海洛因和可卡因渴求呈负相关(PS&lt；.0001至.0335)；毒品活动与压力、海洛因和可卡因渴求呈负相关(PS.0009至.0134)。在进入EMA之前的12小时内，受访者轨道周围的环境也发现了类似的关系。研究结果支持了GMA方法的可行性。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，并挑战了一些假设，即表面上压力大的环境是如何与生活体验相关的，以及这种环境最终是如何损害健康的。GMA方法可能适用于制定个人或邻里层面的干预措施。 我们正在更多的阿片/可卡因使用者中继续这项工作，以维持阿片类激动剂。这个更大的样本将使我们能够调查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，用于在特定评级地点之间插入观察者评级，更好地从原始观察者评级分数生成分数和地图，以及利用我们更多的参与者的优势，开发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。 作为这项更大规模试验的一部分，我们正在与AutoSense的开发者合作，AutoSense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持续测量心率、心率变异性、呼吸、皮肤电导、环境温度和身体活动。来自我们的研究和其他AutoSense合作的数据正在被用于开发可以检测药物使用、吸烟和压力的算法。我们在门诊美沙酮维持期间对40名吸毒者进行了现场测试，他们使用AutoSense进行了4个一周的时间，在此期间，他们还自我报告了药物使用、应激事件、情绪和日常生活中手持设备上的活动。每周进行3次尿药筛查。AutoSense的依从性和可接受性良好，无线心电数据良好率为85.7%。我们参与者的依从性比30名学生(吸烟者和社交饮酒者)在一周内佩戴AutoSense的情况要好。 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)的一个主要目标是实时检测药物使用情况，并将患者的报告负担降至最低。与我们的AutoSense合作者一起，我们已经建立了原则证明。主要的挑战是开发具有生理信息的计算模型(例如，用于推断可卡因的使用情况)，该模型可以使用动态心电在自然环境中可靠地工作。与我们的合作者一起，我们分析了在现场收集的AutoSense数据，以及使用可卡因进行的实验室研究。利用这些数据，我们开发了有效的方法来筛选和清理心电时间序列数据，并基于EMA自我报告提取候选时间窗口。得到的模型实现了100%的真阳性率，同时在11个多小时/天的现场数据中将假阳性保持在1.13/天。这是朝着移动健康干预的关键组成部分迈出的重要一步，移动健康干预可以防止失误演变为复发。

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.