Testing Scalable, Single-Session Interventions for Adolescent Depression in the context of COVID-19

项目摘要/摘要 全国各州和地方正在采取前所未有的措施，以减少由 新冠肺炎，包括影响5,000万青年的学校关闭。这场大流行还导致家庭 极端的财政困难、突如其来的失业和困境。这种集体创伤、社会创伤和 与世隔绝和经济衰退极大地增加了青少年严重抑郁症(MD)的风险： 青年残疾的主要原因。然而，青年MD治疗面临效力和可获得性的问题。向上 接受MD治疗的年轻人中有65%没有反应，部分原因是MD的异质性：MD诊断 反映了1400种可能的症状组合，强调了与个人相匹配的治疗的必要性 需要。治疗可及性问题也同样严重。在大流行之前，50%的患有MD的青年 获得任何治疗；新发现的经济压力将进一步排除家庭负担得起护理的能力 他们的孩子。因此，关键是要确定有效的、可扩展的战略，以缓冲在这种背景下的青年MD 新冠肺炎的战略，以及使这些干预措施与最有可能受益的青年相匹配的战略。这个项目 将集成机器学习方法和大规模SSI研究，以快速测试有效、可访问 新冠肺炎期间减少青少年MD的策略。通过有史以来最大的SSI试验(N=1,200名青年 MD症状增加，年龄12-16岁)，目标1是测试(1)循证SSI是否改善近端 目标(例如，绝望和感知到的代理，它已经预测了较长期的SSI反应)和3- 新冠肺炎大流行期间的一个月临床结果(MD严重性)，以及(2)SSI是否针对 在这种情况下，认知与行为MD症状的影响最大。在一场完全在线的审判中，年轻人 从美国各地招募的人将被随机分成3个自我管理的SSI之一：行为激活型SSI， 有针对性的行为MD症状(快感缺乏；活动停止)；SSI教学成长心态，信念 个人特征是可塑性的，针对认知MD症状(例如绝望)；或控制性SSI。人均 基线、SSI后和3个月的跟踪数据，我们将测试每个SSI相对于对照的相对收益 新冠肺炎的背景。结果将揭示SSI针对的是行为症状还是认知症状 在这种情况下，不同地降低MD的总体严重性。目标2是测试SSI是否(如果可以，哪些SSI可以 影响新冠肺炎的具体创伤和焦虑症状，告知是否新颖、量身定做新冠肺炎 可能需要提供支持，以减少大流行特有的心理健康后遗症。目标3是测试人员级别 以及SSI响应的上下文预测因子，通过机器学习技术，而不考虑整体干预 观察到效果。鉴于MD的异质性，我们将测试基线症状(例如，有更严重的 认知或行为MD症状)预测针对不同症状类型的SSI的反应。我们还将 测试与新冠肺炎相关的逆境(例如，父母失业；亲人因新冠肺炎入院治疗)和 普遍的不利条件(如家庭低收入；少数族裔地位)预示着SSI的反应。

Project Summary/Abstract States and localities nationwide are taking unprecedented steps to reduce public health threats posed by COVID-19, including school closures affecting >50 million youth. The pandemic has also caused families extreme financial hardship, sudden unemployment, and distress. This combination of collective trauma, social isolation, and economic recession drastically increases risk for adolescent major depression (MD): already the lead cause of disability in youth. However, youth MD treatments face problems of potency and accessibility. Up to 65% of youth receiving MD treatment fail to respond, partly due to MD’s heterogeneity: an MD diagnosis reflects >1400 possible symptom combinations, highlighting the need for treatments matched to personal need. Treatment accessibility issues are similarly severe. Before the pandemic, <50% of youth with MD accessed any treatment at all; newfound financial strain will further preclude families’ capacity to afford care for their children. It is thus critical to identify effective, scalable strategies to buffer against youth MD in the context of COVID-19, along with strategies to match such interventions with youth most likely to benefit. This project will integrate machine learning approaches and large-scale SSI research to rapidly test potent, accessible strategies for reducing adolescent MD during COVID-19. Via the largest-ever SSI trial (N=1,200 youth with elevated MD symptoms, ages 12-16), Aim 1 is to test whether (1) evidence-based SSIs improve proximal targets (e.g., hopelessness and perceived agency, which has predicted longer-term SSI response) and 3- month clinical outcomes (MD severity) during the COVID-19 pandemic, and (2) whether SSIs targeting cognitive versus behavioral MD symptoms are most impactful in this context. In a fully-online trial, youths recruited from across the U.S. will be randomized to 1 of 3 self-administered SSIs: a behavioral activation SSI, targeting behavioral MD symptoms (anhedonia; activity withdrawal); an SSI teaching growth mindset, the belief that personal traits are malleable, targeting cognitive MD symptoms (e.g. hopelessness); or a control SSI. Per baseline, post-SSI, and 3-month follow-up data, we will test each SSI’s relative benefits, versus the control, in the context of COVID-19. Results will reveal whether SSIs targeting behavioral versus cognitive symptoms differentially reduce overall MD severity in this context. Aim 2 is to test whether (and, if so, which of) SSIs can impact COVID-19 specific trauma and anxiety symptoms, informing whether novel, COVID-19-tailored supports may be needed to reduce pandemic-specific mental health sequelae. Aim 3 is to test person-level and contextual predictors of SSI response, via machine-learning techniques, regardless of overall intervention effects observed. Given MD’s heterogeneity, we will test whether baseline symptoms (e.g., having more severe cognitive or behavioral MD symptoms) predict response to SSIs targeting different symptom types. We will also test exposure to COVID-19-related adversities (e.g. parent job loss; loved one hospitalized for COVID-19) and general disadvantage (e.g. family low-income; racial minority status) forecast SSI response.