Predicting AOD Relapse and Treatment Completion from Social Media Use

通过社交媒体使用预测 AOD 复发和治疗完成

• 批准号: 8827583
• 负责人: Brenda Curtis
• 金额: $ 1.02万
• 依托单位: TREATMENT RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8827583
• 关键词: Accounting; Address; Adult; Affect; Aftercare; Age; Alcohol abuse; Alcohol consumption; Alcohol or Other Drugs use; Alcohols; Algorithms; Area; Asthma; Attitude; Authorization documentation; Behavior; Behavior Therapy; Benzodiazepines; Businesses; Chronic; Clinic; Cocaine; Collection; Communication; Communications Media; Communities; Consent; Data; Data Sources; Development; Diabetes Mellitus; Dropout; Drops; Drug abuse; Drug usage; Fostering; Frequencies; Friends; Future; Gender; Geographic Locations; Health; Health Care Costs; Heroin; Individual; Intake; Internet; Intervention; Language; Marijuana; Marketing; Medical; Medicine; Mental Depression; Methods; Monitor; Observational Study; Outcome; Outpatients; Participant; Patients; Pattern; Pharmaceutical Preparations; Population Intervention; Prevalence; Process; Public Health; Race; Records; Recovery; Recruitment Activity; Relapse; Reporting; Research; Research Personnel; Risk; Risk Factors; Science; Signal Transduction; Site; Site Visit; Social Development; Social Interaction; Social Network; Stress; Surveys; System; Techniques; Technology; Testing; Time; Treatment outcome; Vocabulary; World Health; addiction; alcohol and other drug; base; clinical practice; computer science; high risk; improved; insight; multidisciplinary; peer; phrases; prescription opiate; prevent; programs; prospective; public health relevance; public health research; social; social networking website; substance abuse treatment; theories; tool; treatment duration; treatment program; web-based social networking

DESCRIPTION (provided by applicant): Public health research and practice have not yet taken advantage of emerging changes in communication media even though methods and tools to analyze social media interactions have been developed and successfully used in marketing and business to better target prospective customers, tailor products, and predict behavior. This research will adapt these tools and take important steps in advancing science in ways that have potential to be used to improve public health. These adaptations will allow processing and meaningful interpretation of large volumes of social media data generated by individuals in Alcohol or Drug Abuse (AOD) treatment and allow us to address 3 aims. 1) It will allow use of this data source for identifying social media content that might be used to identify individuals who are at high risk for substance use relapse and treatment dropout; 2) It will provide a description of the frequency and patterns of AOD patients' public dialogue on social media with respect to topics such as alcohol and drug information and use, as well as treatment information; and 3) It will help to identify the best social media platforms to reach individuals i AOD treatment. Research staff will recruit 1,000 patients entering drug-free outpatient AOD treatment from 4 community based substance abuse treatment programs (a total of 11 sites). Participants will complete an intake battery and survey of their social media use, report weekly on their alcohol and drug use, give permission to extract treatment entry and discharge data from their clinic records, and to extract data from their Facebook and Twitter accounts. To address the first aim, social media data will be analyzed using Differential Language Analysis (DLA), an open-vocabulary technique that does not rely on pre-conceived theories regarding reasons for relapse and treatment dropout, but allows the data itself to drive an inclusive exploration of language. It finds words, phrases, and topics and presents them using word clouds, but unlike most word clouds, which scale words by their frequency, DLA scales words according to the strength of the relationship between the word or phrase and the variable tested. This open-vocabulary approach has excellent potential to reveal new insights to aid our understanding of risk factors, attitudes, and behaviors associated with relapse and treatment dropout. Eventually this information could be used to generate algorithms in the development of social media applications that would provide additional support for individuals when they are at risk for relapse and treatment dropout, or provide deserved acknowledgement for efforts when patients are fully engaged in treatment. Identifying factors that adversely affect treatment retention and sustained recovery is imperative. Less than 45% of the patients who enter treatment complete it and relapse rates have been reported as high as 92% at 12 months, with most relapsing within 3 months. Identifying social interactions that predict treatment dropout or substance use and automatically sending messages to intervene before that happens could improve and extend the lives of the 22.2 million drug-dependent individuals in the US.

描述（申请人提供）：即使已经开发并成功地用于营销和业务中，公共卫生研究和实践尚未利用通信媒体的新兴变化，即可更好地针对潜在客户，量身定制产品并预测行为。这项研究将适应这些工具，并采取重要的步骤来推进科学，以有可能用于改善公共卫生的潜力。这些适应将允许对酗酒或药物滥用（AOD）治疗中个人产生的大量社交媒体数据进行处理和有意义的解释，并允许我们解决3个目标。 1）它将允许使用此数据源来识别可能用于识别有物质使用复发和治疗辍学率高风险的个人的社交媒体内容； 2）它将提供有关AOD患者在社交媒体上关于酒精和药物信息和使用等主题以及治疗信息等主题的频率和模式的描述； 3）这将有助于确定最佳的社交媒体平台以接触我AOD治疗的个人。研究人员将招募1,000名从4个基于社区的药物滥用治疗计划（总共11个地点）的患者进入无毒门诊AOD治疗。参与者将完成对其社交媒体使用的摄入量电池，每周报告其酒精和吸毒，允许从其诊所记录中提取治疗条目并排出数据，并从其Facebook和Twitter帐户中提取数据。为了解决第一个目标，将使用差异语言分析（DLA）分析社交媒体数据，这是一种开放式摄影技术，不依赖于有关复发和治疗辍学的原因的预先构想的理论，而是允许数据本身推动对语言的包容性探索。它找到了单词，短语和主题，并使用单词云来展示它们，但是与大多数单词云不同，这些单词云通过其频率缩放，DLA根据单词或短语或短语和所测试的变量之间的关系强度来缩放单词。这种开放式摄影方法具有巨大的潜力，可以揭示新的见解，以帮助我们理解与复发和治疗辍学有关的风险因素，态度和行为。最终，这些信息可用于在社交媒体应用的开发中生成算法，这些算法在患者有复发和治疗辍学的风险时会为他们提供额外的支持，或者在患者完全从事治疗时提供应有的确认。确定不利影响治疗保留和持续恢复的因素至关重要。据报道，进入治疗的患者中，只有不到45％的患者在12个月时据报道高达92％，其中大多数在3个月内复发。确定可以预测治疗辍学或物质使用并自动发送信息进行干预的社交互动可以改善和延长美国2220万毒品依赖人的寿命。