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.

项目摘要 创伤后应激障碍（PTSD）是报道的退伍军人中最高的同时发生障碍 有问题的大麻使用。但是，许多退伍军人未能寻求或参与两者的医疗服务 条件，结果，症状的严重程度和相应风险的增加可能未被发现，并且 不受管理。尽管通过交付非治疗的退伍军人有越来越多的兴趣 通过移动设备的即时干预措施，此类干预措施需要清楚地了解退伍军人何时 有利于PTSD和使用问题的大麻使用症状升级的风险增加。尽管正在进行 努力确定需要支持心理健康和药物使用困难的退伍军人 重新整合（从部署回来），这些努力取得了最小的成功。机器学习 - 一种特殊的人工智能形式，有助于将个人分类为风险概况 - 可能有望 改善风险评估和症状升级。机器学习算法应用于被动 - 从移动设备和可穿戴设备收集的数据（例如，加速度计数据，查看屏幕的时间， 睡眠数据，锻炼，GPS数据）可能是一种有希望的，最小的责任策略，用于检测风险时期和 最终告知即时干预措施。智能手机和可穿戴设备的被动数据已经 用于机器学习算法，以预测PTSD和其他疾病的风险（例如抑郁症），但具有 未应用于PTSD和大麻使用的预测或对之间的相互作用的理解 这些条件。尽管过去的研究成功地使退伍军人参与了被动数据收集，但 策略将比积极的数据收集要低，目前尚不清楚这是否是可行的方法 临床应用。因此，此应用的目的是了解被动数据的实用性， 在预测PTSD症状的临床上显着升级时，与自我报告数据的结合和 在最近出院的退伍军人的非治疗中使用有问题的大麻 军队。 75名男性和女性非治疗的退伍军人，具有创伤史和 在民用重新融合六个月内，过去的大麻使用将在线招募。参与者 将为FITBIT授予FITBIT，并在其智能手机（HeadSmart）上安装被动和主动的数据收集应用程序。 他们将完成一项基线和三个每月的随访调查。此外，在观察期间， 退伍军人将对PTSD症状和大麻使用的每日简短调查，被动数据将是 记录。被动和每日日记数据将在机器学习算法中分析以预测症状 升级和未来的估计（例如，存在临床显着增加）（目标1）并了解 每日/每周症状相互作用（AIM 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预防或早期干预工作提供信息 高需求的退伍军人。

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