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

使用机器学习预测未寻求治疗的退伍军人的日常创伤后应激障碍和大麻使用障碍症状

• 批准号: 10470791
• 负责人: Jordan P Davis
• 金额: $ 20.34万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10470791
• 关键词: Accelerometer; Affect; Afghanistan; American; Applications Grants; Artificial Intelligence; Back; Behavior Therapy; Cannabis; Caring; Cellular Phone; Communities; Conflict (Psychology); Data; Data Collection; Data Reporting; Detection; Development; Diagnosis; Diagnostic; Disease; Early Intervention; Enrollment; Environment; Epidemiology; Event; Exercise; Female; Future; General Population; Goals; Healthcare Systems; Heart Rate; Individual; Intervention; Iraq; Lead; Legal; Life; Location; Machine Learning; Mental Depression; Mental Health; Mental disorders; Methods; Military Personnel; Modeling; Moods; Participant; Patient Self-Report; Patients; Population; Post-Traumatic Stress Disorders; Prevention; Recording of previous events; Reporting; Research; Risk; Risk Assessment; Sampling; Severities; Sleep; Stress; Surveys; Symptoms; Techniques; Testing; Time; United States Department of Veterans Affairs; Variant; Veterans; acceptability and feasibility; base; clinical application; clinical predictors; clinically significant; common symptom; comorbidity; data streams; diaries; fitbit; follow-up; handheld mobile device; health care service; high risk; improved; innovation; interest; machine learning algorithm; machine learning model; male; marijuana use; marijuana use disorder; meetings; military veteran; new technology; post-traumatic symptoms; preventive intervention; psychologic; recruit; research study; screening; substance use; success; trauma exposure; wearable device

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.

项目概要 创伤后应激障碍 (PTSD) 是退伍军人中最常见的并发疾病 有问题的大麻使用。然而，许多退伍军人未能寻求或参与医疗保健服务 情况，因此，症状严重程度的增加和相应的风险可能未被发现， 不受管理。尽管人们越来越有兴趣通过提供服务来接触未寻求治疗的退伍军人 通过移动设备进行及时干预，此类干预需要清楚地了解退伍军人何时 患有创伤后应激障碍（PTSD）和有问题的大麻使用导致症状升级的风险更高。尽管正在进行 努力确定在服役期间因心理健康和药物使用困难而需要支持的退伍军人 重返社会（部署返回后），这些努力收效甚微。机器学习—— 一种特殊形式的人工智能，有助于将个人分类为风险状况——可能有希望 改进风险评估和症状升级。机器学习算法应用于被动 从移动和可穿戴设备收集数据（例如，加速度计数据、查看屏幕的时间、 睡眠数据、运动数据、GPS 数据）可能是一种有前途的、负担最小的策略，用于检测风险期和 最终为及时干预提供信息。来自智能手机和可穿戴设备的被动数据已被 用于机器学习算法来预测 PTSD 和其他病症（例如抑郁症）的风险，但已 尚未应用于 PTSD 和大麻使用的预测或对两者之间相互作用的理解 这些条件。尽管过去的研究已成功地让退伍军人参与被动数据收集，而这一点 该策略比主动数据收集的负担更低，目前尚不清楚这是否是一种可行的方法 临床应用。因此，该应用程序的目的是了解被动数据的效用， 结合自我报告数据或单独预测 PTSD 症状和临床显着升级 最近退伍的未寻求治疗的退伍军人使用大麻的问题 军队。七十五名男性和女性非寻求治疗的退伍军人，有外伤暴露史和 过去一个月吸食大麻且在重返社会六个月内的人将在网上招募。参加者 将获得 FitBit 并在其智能手机上安装被动和主动数据收集应用程序 (HeadSmart)。 他们将完成基线调查和三个月的后续调查。此外，在观察期内， 退伍军人将完成关于创伤后应激障碍症状和大麻使用的简短每日调查，被动数据将被 记录了。将使用机器学习算法分析被动日记数据和每日日记数据以预测症状 升级和未来的情况（例如，临床上显着增加的存在）（目标 1）并了解 每日/每周症状相互作用（目标 2）。我们还将评估这种方法的可行性和可接受性 （目标 3）。这项研究的结果最终将为这些疾病的预防或早期干预工作提供信息 高需求的退伍军人人口。

项目成果

Longitudinal associations between insomnia, cannabis use and stress among US veterans.

美国退伍军人失眠、大麻使用和压力之间的纵向关联。

• DOI: 10.1111/jsr.13945
• 发表时间: 2024
• 期刊: Journal of sleep research
• 影响因子: 4.4
• 作者: Davis,JordanP;Prindle,John;Saba,ShaddyK;Castro,CarlA;Hummer,Justin;Canning,Liv;Pedersen,EricR
• 通讯作者: Pedersen,EricR