Remote alcohol monitoring to facilitate abstinence reinforcement with an underserved population

远程酒精监测有助于服务不足的人群加强戒酒

• 批准号: 10015192
• 负责人: Mikhail Nikolaas Koffarnus
• 金额: $ 65.88万
• 依托单位: UNIVERSITY OF KENTUCKY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-20 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10015192
• 关键词: Abstinence; Address; Adult; Alcohol consumption; Alcohols; Area; Automation; Biochemical; Caring; Cellular Phone; Clinical; Clinical Services; Communication; Communities; Control Groups; DSM-V; Data; Development; Drug Metabolic Detoxication; Effectiveness; Evidence based treatment; Goals; Health; Health Personnel; Hospitals; Incentives; Individual; Inpatients; Intervention; Knowledge; Lead; Maintenance; Medical; Modeling; Participant; Persons; Population; Prevalence; Prize; Psychological reinforcement; Randomized; Relapse; Research; Services; Severities; Stigmatization; System; Techniques; Testing; Underserved Population; United States; alcohol abstinence; alcohol monitoring; alcohol services; alcohol use disorder; base; contingency management; cost; disorder later incidence prevention; effective therapy; experimental study; feasibility trial; financial incentive; interest; novel; outpatient programs; prevent; readmission rates; recruit; secondary analysis; treatment as usual; treatment effect; treatment services

PROJECT SUMMARY/ABSTRACT Lifetime prevalence of DSM-5 alcohol use disorder in the United States is 29%, but only 20 to 24% of people with alcohol use disorder ever seek treatment. The most prevalent reasons given for not seeking treatment relate to an inability or unwillingness (e.g., due to stigmatization) to attend traditional in-person treatment. For individuals who do receive medical treatment or care for alcohol use disorder, their first point of contact with the medical community is often for alcohol detoxification. Unfortunately, relapse following alcohol detoxification is common and few people continue utilizing treatment services after being discharged. The pervasiveness of alcohol use disorder indicates a need for continued development of high-impact treatments that are effective, acceptable to the untreated, and easily disseminated widely. Contingency management, or the delivery of monetary incentives contingent on verified abstinence, is an effective treatment for alcohol use disorder. However, costs and barriers to accurate, frequent biochemical verification of alcohol abstinence limit the widespread use of this technique for alcohol use disorder. In our preliminary data, we’ve successfully demonstrated the feasibility of remote alcohol monitoring and incentives for the reduction of alcohol use. We used technologically advanced mobile breathalyzers to verify abstinence remotely, cell phones for communication, and a reloadable debit card to deliver incentives with little delay. Our preliminary study has shown a high rate of abstinence with this model and participants gave this approach high ratings for effectiveness and acceptability. Here, we will develop this model into two treatment options ready for dissemination: 1) a stand-alone remote treatment directed toward abstinence initiation and maintenance in underserved individuals, and 2) an adjunct to existing treatment services to prevent relapse after hospital detoxification. We will also address additional gaps in knowledge in both treatment contexts: 1) what the maximally effective duration and intensity of abstinence incentives is, and 2) what the persistence of treatment effects are once incentives are discontinued. Through the use of technological advancements combined in a novel way with abstinence incentives, the proposed research has the potential to provide a framework for bringing effective, evidence-based treatments to a large number of individuals with alcohol use disorder who are currently underserved by existing treatment options or are unable to maintain abstinence after detoxification. Due to the extent of automation and relatively low use of research staff to execute, this treatment model is highly scalable, an important feature when addressing the large problem of untreated alcohol use disorder.

项目总结/文摘