Using Machine Learning to predict daily PTSD and cannabis use disorder symptoms among non-treatment seeking veterans

项目总结 创伤后应激障碍(PTSD)是退伍军人中最高的共生性障碍， 有问题的大麻使用。然而，许多退伍军人未能为两者寻求或参与医疗保健服务 因此，症状严重性和相应风险的增加可能不会被发现和 非托管。尽管人们越来越有兴趣通过向未寻求治疗的退伍军人提供 通过移动设备进行及时干预，这样的干预需要清楚地了解退伍军人 患有创伤后应激障碍和有问题的大麻使用增加了症状升级的风险。尽管仍在进行 努力确定哪些退伍军人在精神健康和药物使用困难方面需要支持 在重新融入社会(从部署返回时)，这些努力取得的成效微乎其微。机器学习-- 人工智能的一种特殊形式，有助于将个人归类到风险档案中--可能在 改进风险评估和症状上报。机器学习算法在被动学习中的应用 从移动和可穿戴设备收集的数据(例如，加速度计数据，查看屏幕所花费的时间， 睡眠数据、锻炼、GPS数据)可能是一种有前途的、负担最小的策略，以检测风险时期和 最终通知及时干预。来自智能手机和可穿戴设备的被动数据已经 在机器学习算法中用于预测创伤后应激障碍和其他情况(例如抑郁症)的风险，但 不适用于预测创伤后应激障碍和大麻的使用或了解两者之间的相互作用 这些条件。尽管过去的研究成功地让退伍军人参与了被动的数据收集，但这 策略将比主动数据收集负担更低，目前尚不清楚这是否是一种可行的方法 临床应用。因此，本应用程序的目标是了解被动数据在 结合自我报告数据或单独预测创伤后应激障碍症状和 不寻求治疗的退伍军人中有问题的大麻使用问题 军方的。75名男性和女性未寻求治疗的退伍军人，有创伤暴露和 过去一个月吸食大麻的人在平民重返社会六个月内将在网上招募。参与者 将获得一个FitBit，并在他们的智能手机(HeadSmart)上安装被动和主动数据收集应用程序。 他们将完成一次基线调查和三次每月跟踪调查。此外，在观察期内， 退伍军人将完成对创伤后应激障碍症状和大麻使用情况的简短每日调查，被动数据将 录制好了。被动和日常日记数据将在机器学习算法中进行分析，以预测症状 升级和未来的干酪率(例如，出现临床显著增加)(目标1)和理解 每日/每周症状相互作用(目标2)。我们还将评估这一方法的可行性和可接受性。 (目标3)。这项研究的结果将最终为预防或早期干预工作提供信息 退伍军人的高需求人群。

PROJECT SUMMARY Posttraumatic stress disorder (PTSD) is the highest co-occurring disorder among veterans who report problematic cannabis use. However, many veterans fail to seek or engage with health care services for both conditions, and as a result, increases in symptom severity and corresponding risk may go undetected and unmanaged. Although there is increasing interest in reaching non-treatment-seeking veterans by delivering just-in-time interventions via mobile devices, such interventions require a clear understanding of when veterans with PTSD and problematic cannabis use are at heightened risk for escalating symptoms. Despite ongoing efforts to identify veterans who need support for mental health and substance use difficulties at the time of reintegration (upon return from deployment), these efforts have achieved minimal success. Machine learning-- a special form of artificial intelligence that aids in classifying individuals into risk profiles--may have promise in improving risk assessment and symptom escalation. Machine learning algorithms applied to passively- collected data from mobile and wearable devices (e.g., accelerometer data, time spent looking at screens, sleep data, exercise, GPS data) could be a promising, minimal-burden strategy to detect periods of risk and ultimately inform just-in-time interventions. Passive data from smartphones and wearable devices has been used in machine learning algorithms to predict risk for PTSD and other conditions (e.g., depression), but has not been applied to the prediction of PTSD and cannabis use or the understanding of the interplay between these conditions. Although past research has successfully engaged veterans in passive data collection and this strategy would be lower-burden than active data collection, it is unclear whether this is a feasible approach in clinical applications. Thus, the objective of this application is to understand the utility of passive data, in conjunction with self-report data or alone, in predicting clinically significant escalations in PTSD symptoms and problematic cannabis use among non-treatment seeking veterans who have recently discharged from the military. Seventy-five male and female non-treatment-seeking veterans with a history of trauma exposure and past-month cannabis use who are within six months of civilian reintegration will be recruited online. Participants will be given a FitBit and install the passive and active data collection app on their smartphone (HeadSmart). They will complete a baseline and three monthly follow-up surveys. Further, over the observation period, veterans will complete brief daily surveys of PTSD symptoms and cannabis use, and passive data will be recorded. Passive and daily diary data will be analyzed in machine learning algorithms to predict symptom escalation and future caseness (e.g., presence of clinically significant increase) (Aim 1) and understand daily/weekly symptom interplay (Aim 2). We will also assess the feasibility and acceptability of this approach (Aim 3). The results of this research will ultimately inform prevention or early intervention efforts among this high-need population of veterans.