Modeling Physiology and Behavior of Veterans to Avert Opioid Related Mortality Through Timely Intervention

对退伍军人的生理和行为进行建模，通过及时干预避免阿片类药物相关的死亡

• 批准号: 10481499
• 负责人: STEVEN D. FORMAN
• 金额: $ 25.35万
• 依托单位: BEHAIVIOR, LLC
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10481499
• 关键词: Address; Adherence; Affect; Age; Annual Reports; Anxiety; Award; Behavior; Behavior monitoring; Behavioral; COVID-19 pandemic; Caring; Cellular Phone; Centers for Disease Control and Prevention (U.S.); Clinical Research; Collection; Coupling; Cues; Custom; Data; Data Reporting; Data Set; Development; Education; Epidemic; Event; Face; Feeling suicidal; Gender Identity; General Population; Goals; Health Services Accessibility; High Prevalence; Human; Incidence; Income; Individual; Injury; Intelligence; Intervention; Learning; Life; Literature; Machine Learning; Mental Health; Methodology; Military Personnel; Modeling; Monitor; Opiate Addiction; Opioid; Outcome; Overdose; Pain; Patient Self-Report; Patients; Performance; Periodicity; Personal Satisfaction; Persons; Phase; Physiologic Monitoring; Physiological; Physiology; Population; Post-Traumatic Stress Disorders; Process; Production; Public Health; Race; Recovery; Relapse; Reporting; Resistance; Risk; Safety; Small Business Innovation Research Grant; Stress; Substance Use Disorder; Suicide; Suicide prevention; Surveys; System; Technology; Time; Training; Treatment Efficacy; Treatment Protocols; Trust; United States; United States Substance Abuse and Mental Health Services Administration; Veterans; Work; Wrist; addiction; base; behavior change; care providers; career; chronic pain; cloud platform; combat; comorbidity; craving; design; disorder later incidence prevention; drug craving; effectiveness evaluation; first responder; help-seeking behavior; high risk; improved; insight; machine learning model; mental state; military veteran; mortality; negative affect; novel; operation; opioid epidemic; opioid misuse; opioid overdose; opioid use; opioid use disorder; overdose death; peer support; personalized care; predictive modeling; programs; remote intervention; remote monitoring; research study; seal; severe mental illness; social; social stigma; software as a service; stressor; substance misuse; substance use; suicidal; suicide rate; support network; tool; wearable device; wearable sensor technology

Project Summary/Abstract Opioid addiction affects people of all races, ages, gender identities, education, and income levels across the United States. The epidemic has been intensifying in recent years, with relapse rates reaching close to 91% (Kadam et al., 2017) and provisional data from 2020 indicating the highest number of overdose deaths ever recorded in a year, over 88,000 (Centers for Disease Control and Prevention, 2020). Further, only 10-20% of people struggling with addiction receive care for their OUD (Substance Abuse and Mental Health Services Administration, 2020). An increasing body of literature exposes a population extremely vulnerable to opioid addiction and overdose: United States veterans. Veterans face unique challenges that predispose them to greater risk for misusing opioids such as higher prevalence of and more severe pain than the general population (Nahin, 2017), and the stresses and risks of deployment that often result in PTSD upon return to civilian life (Seal et al., 2012). Indeed, this group, though not monolithic, may be generally resistant to support; half of military personnel reported that they believe seeking help for mental health issues would harm their military career. Suicide rates continue to rise, faster in the veteran population than in the non-veteran population, even despite the decrease in the population proportion of veterans. In 2018, almost one in seven suicides in the U.S. was a veteran (“2020 National Veteran Suicide Prevention Annual Report”, 2020). Mental health issues have an effect on substance use disorders as well. Nationwide, the rate of overdose deaths in veterans increased by 65% from 2010 to 2016 (Lewei et al., 2019). The stressors of deployment during wartime that are often difficult to shake off upon return to civilian life, incidences of injuries resulting in severe and often chronic pain, and the nuances of military culture, combined with stigmas associated with OUD, perpetuate a detrimental cycle of addiction for veterans. To address this, we are proposing the development of a novel solution that addresses the mental health of veterans nationally via a first-of-its-kind remote monitoring and intervention software-as-a-service offering. Our system builds upon our technology stack and learnings from clinical studies on individuals with OUD. Given that substance use in the veteran population often precedes a deleterious cycle commonly resulting in serious and persistent mental illness (SPMI) and sometimes even suicide, advanced and real-time remote monitoring of the state of mental health would be an important indicator. This tool provides timely intervention that addresses OUD and further averts the progression of SPMI as well as suicidal tendencies.

项目摘要/摘要 阿片类药物成瘾会影响各个种族，年龄，性别认同，教育和收入水平的人们 美国。近年来，该流行病一直在加剧，继电器率接近91％ （Kadam等，2017）和2020年的临时数据表明有史以来过量死亡人数最多 记录一年，超过88,000（疾病控制与预防中心，2020年）。此外，只有10-20％ 挣扎着成瘾的人会为自己的OUD提供护理（药物滥用和心理健康服务 管理，2020年）。越来越多的文献暴露了极易受到阿片类药物的人群 成瘾和过量：美国退伍军人。 退伍军人面临独特的挑战，使他们容易出现丢失阿片类药物的风险 比一般人群更高的患病率和更严重的疼痛（Nahin，2017年），压力和压力和 部署风险通常会导致PTSD恢复平民生活（Seal等，2012）。确实，这个群体， 尽管不是单片，但通常可以抵抗支撑。一半的军事人员报告了他们 相信寻求解决心理健康问题的帮助会损害他们的军事职业。自杀率继续上升， 退伍军人人口的速度比非退伍军人人口快，甚至下降了人口的下降 退伍军人比例。 2018年，美国近七分之一的自杀是一名资深人士（“ 2020年国民） 资深自杀预防年度报告”，2020年）。心理健康问题对药物使用有影响 疾病也是如此。全国范围内，从2010年到2016年，退伍军人的过量死亡率增加了65％ （Lewei等，2019）。战时部署的压力源通常在返回时很难摆脱 对于平民生活，受伤的事件导致严重和经常慢性疼痛，而军事的细微差别 培养与与Oud相关的污名结合在一起，使退伍军人成瘾的有害周期永存。 为了解决这个问题，我们提出了一种解决心理健康的新颖解决方案的开发 全国范围内的退伍军人通过首个远程监控和干预软件即服务。 我们的系统基于我们的技术堆栈和对OUD个体的临床研究的学习。 鉴于资深人口中的物质使用通常是在有害周期之前的，通常导致 严重和持久的精神疾病（SPMI），有时甚至是自杀，高级和实时遥控 监测心理健康状况将是一个重要的指标。该工具提供及时的干预 这解决了OUD并进一步避免SPMI的进展以及自杀趋势。