Predicting Internet-Based Treatment Response for Major Depressive Disorder

预测重度抑郁症基于互联网的治疗反应

• 批准号: 9624631
• 负责人: RANDY PATRICK AUERBACH
• 金额: $ 18.08万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-08 至 2020-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9624631
• 关键词: Predicting; Internet; Based; Treatment; Response

As many as 53% of students report experiencing depression during college, and these depressive episodes are associated with a higher frequency of academic problems, comorbidity, and suicide. Although there are effective options for treatment, the majority of individuals (>70%) do not pursue services, and even for those who do, response rates remain modest (~40-50%). As a means of increasing accessibility to treatment, internet-based interventions for depression have been developed and tested. Despite increased availability, response to internet-based interventions continues to vary substantially, and failed treatment often contributes to persistence and worsening of symptoms. Therefore, identifying individuals with a high likelihood of responding to internet-based treatment would represent a major advance and address a critical unmet need. In recent years, promising approaches for testing the heterogeneity of the treatment effects – delineating which individuals are likely to respond to a given treatment – have been developed. However, their use for identifying predictors of treatment response in depression remains unclear. To address this unmet need, the proposed study will test a new, cost-effective, and feasibly-scaled method of predicting differential treatment response following internet-based cognitive behavioral therapy (iCBT) for depression in a large, representative college sample (Boston Consortium of Colleges and Universities which includes 7 schools). Members of the consortium have committed to screening all incoming freshmen (N = ~14,000) through a rigorous online assessment and to offer iCBT to students with elevated levels of depressive symptoms (i.e., minor or major depression). The following steps will be pursued. First, in the initial phase of the study, freshmen students from the Boston Consortium will be screened for depression through an online survey, and they also will complete web-based neurocognitive tasks probing key mechanisms underpinning depression. Depressed students will be invited to enroll in iCBT, and a predictive algorithm will be developed based on assessments across different units of analysis (i.e., clinical characteristics, neurocognitive indices) to identify iCBT responders. Second, after the development phase, an independent sample of college students will be recruited to validate the predictive algorithm. This validation phase will use clinical indicators and neurocognitive data. Additionally, functional magnetic resonance imaging (fMRI) data targeting key mechanisms within the Research Domain Criteria (RDoC) will be acquired from a subset of participants. Neural data will be integrated to determine whether they improve the predictive algorithm. Third, data across independent samples will be combined, which will increase power to refine our predictive model for both acute and sustained response. In summary, there are alarming rates of depression among college students, and only a minority of students utilize mental health services. The proposed research will personalize our approach to depression treatment, which, ultimately, will improve effectiveness and better inform mental health care across college campuses.

多达53％的学生报告大学期间经历抑郁症，这些抑郁症 情节与更高的学术问题，合并症和自杀相关。虽然 有有效的治疗选择，大多数个人（> 70％）不追求服务，甚至 对于那些这样做的人，响应率仍然适度（约40-50％）。作为增加可访问性的一种手段 治疗，基于Internet的抑郁症干预措施已经开发和测试。尽管增加了 可用性，对基于Internet的干预措施的反应持续差异很大，并且经常失败 有助于持续和担心症状。因此，确定可能性很高的人 响应基于互联网的治疗将代表一个重大进步，并满足了至关重要的未满足需求。 近年来，测试治疗效果异质性的前景方法 - 描述 哪些人可能会对给定的治疗做出反应 - 已经开发出来。但是，它们用于 识别抑郁症治疗反应的预测因素尚不清楚。为了满足这种未满足的需求， 拟议的研究将测试一种预测差异处理的新的，成本效益且可行的方法 基于Internet的认知行为疗法（ICBT）的抑郁症的反应 大学样本（波士顿大学和大学联盟，包括7所学校）。成员 财团已承诺通过严格的在线筛选所有新生（n = 〜14,000） 评估并向具有抑郁症状升高的学生提供ICBT（即未成年人或主要 沮丧）。将采取以下步骤。首先，在研究的最初阶段，来自 波士顿财团将通过在线调查中筛选抑郁症，他们还将完成 基于Web的神经认知任务探讨了抑郁症的关键机制。沮丧的学生会 被邀请参加ICBT，并将根据各种评估来制定预测算法 分析单位（即临床特征，神经认知指数）以识别ICBT响应者。 其次，在发展阶段之后，将招募独立的大学生样本以验证 预测算法。该验证阶段将使用临床指标和神经认知数据。此外， 功能磁共振成像（fMRI）数据靶向研究领域内的关键机制 标准（RDOC）将从参与者的一部分中获取。神经数据将集成以确定 它们是否改善了预测算法。第三，将组合跨独立样本的数据， 这将增加为急性和持续反应的我们的预测模型而提高能力。总之， 大学生的抑郁率令人震惊，只有少数学生利用精神 卫生服务。拟议的研究将个性化我们的抑郁症治疗方法，该方法，该方法 最终，将提高大学校园的有效性和更好的非正式医疗保健。

项目成果

ADHD Comorbidity Structure and Impairment: Results of the WHO World Mental Health Surveys International College Student Project (WMH-ICS).

• DOI: 10.1177/10870547211057275
• 发表时间: 2022-06
• 期刊: JOURNAL OF ATTENTION DISORDERS
• 影响因子: 3
• 作者: Mak, Arthur D. P.;Lee, Sue;Sampson, Nancy A.;Albor, Yesica;Alonso, Jordi;Auerbach, Randy P.;Baumeister, Harald;Benjet, Corina;Bruffaerts, Ronny;Cuijpers, Pim;Ebert, David D.;Gutierrez-Garcia, Raul A.;Hasking, Penelope;Lapsley, Coral;Lochner, Christine;Kessler, Ronald C.
• 通讯作者: Kessler, Ronald C.