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

在 COVID-19 背景下测试针对青少年抑郁症的可扩展、单次干预措施

• 批准号: 10164526
• 负责人: Jessica Lee Schleider
• 金额: $ 39.28万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-16 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10164526
• 关键词: Adolescent; Affect; Age; Anhedonia; Anxiety; Behavioral; Belief; Buffers; COVID-19; COVID-19 pandemic; Caring; Child; Clinical; Cognitive; Data; Diagnosis; Disadvantaged; Distress; Dropout; Economic Recession; Educational process of instructing; Effectiveness; Evaluation; Exposure to; Face; Family; Feeling hopeless; Financial Hardship; Generations; Goals; Growth; Health Services Accessibility; Healthcare Systems; Heterogeneity; Individual; Intervention; Intervention Studies; Intervention Trial; Lead; Length; Link; Location; Logistics; Machine Learning; Major Depressive Disorder; Mental Depression; Mental Health; Mental Health Services; Neurobehavioral Manifestations; Occupations; Online Systems; Outcome; Parents; Persons; Positioning Attribute; Psyche structure; Psychopathology; Public Health; Randomized; Research; Risk; Sampling; Schools; Self Administration; Services; Severities; Social isolation; Students; Symptoms; Techniques; Testing; Trauma; Unemployment; Withdrawal; Work; Youth; active control; anxiety symptoms; base; child depression; depressive symptoms; disability; evidence base; experience; falls; follow-up; high risk; improved; innovation; intervention effect; loved ones; lower income families; novel; pandemic disease; predicting response; premature; psychosocial; racial minority; recruit; response; social media; therapy design; trait; trauma symptom; treatment trial; virtual

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.

项目总结/摘要 美国各州和地方政府正在采取前所未有的措施，以减少由以下因素造成的公共卫生威胁： COVID-19，包括影响超过5000万青年的学校关闭。大流行病还造成家庭 极端的经济困难，突然的失业和痛苦。这种集体创伤、社会 孤立和经济衰退大大增加了青少年抑郁症（MD）的风险： 青年残疾主要原因。然而，青年MD治疗面临的效力和可及性的问题。起来 到65%的接受MD治疗的年轻人没有反应，部分原因是MD的异质性： 反映了>1400种可能的症状组合，突出了与个人相匹配的治疗需求。 需要的获得治疗的问题同样严重。在大流行之前，<50%的青年MD患者 获得任何治疗;新发现的财政压力将进一步排除家庭负担得起的照顾能力， 子女因此，关键是要确定有效的，可扩展的战略，以缓冲对青年MD的背景下， 沿着的战略，使这些干预措施与最有可能受益的青年相匹配。这个项目 将整合机器学习方法和大规模的SSI研究，以快速测试有效的，可访问的 在COVID-19期间减少青少年MD的策略。通过有史以来规模最大的SSI试验（N= 1，200名患有 MD症状升高，年龄12-16岁），目的1是测试（1）基于证据的SSI是否改善近端 目标（例如，绝望和感知代理，预测了长期的SSI响应）和3- 在COVID-19大流行期间的月临床结果（MD严重程度），以及（2）SSI是否针对 认知与行为MD症状在这种情况下最具影响力。在一次完全在线的试验中， 从美国各地招募的受试者将被随机分配到3种自我管理SSI中的1种：行为激活SSI， 针对行为MD症状（快感缺乏;活动退缩）; SSI教学成长心态，信念 个人特质是可塑性的，针对认知性MD症状（例如绝望）;或对照SSI。每 基线、SSI后和3个月随访数据，我们将测试每种SSI相对于对照组的相对获益， COVID-19的背景。结果将揭示SSIs是否针对行为与认知症状 在这种情况下，差异性地降低总体MD严重程度。目标2是测试SSI是否（以及，如果是，哪些）可以 影响COVID-19特定的创伤和焦虑症状，告知是否新颖，COVID-19量身定制 可能需要提供支助，以减少大流行病特有的心理健康后遗症。目标3是测试人的水平 和SSI响应的上下文预测，通过机器学习技术，无论整体干预 观察到的效果。考虑到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）预测对针对不同症状类型的SSI的反应。我们还将 测试暴露于COVID-19相关逆境（例如父母失业;亲人因COVID-19住院），以及 一般不利因素（如家庭低收入;少数民族地位）预测SSI响应。