Harnessing Network Science to Personalize Scalable Interventions for Adolescent Depression

利用网络科学对青少年抑郁症进行个性化的可扩展干预措施

• 批准号: 10473071
• 负责人: Jessica Lee Schleider
• 金额: $ 7.19万
• 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 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10473071
• 关键词: Achievement; Address; Adolescent; Age; Anhedonia; Anxiety; Area; Behavioral; Behavioral Symptoms; Belief; Clinical; Clinical Psychology; Cognitive; Complex; Data; Data Collection; Department chair; Development; Diagnosis; Disease; Economic Burden; Educational process of instructing; Feeling hopeless; Funding; Goals; Growth; Guidelines; Health Services Accessibility; Heterogeneity; Individual; Infrastructure; Intervention; Intervention Studies; Intervention Trial; Leadership; Link; Major Depressive Disorder; Mental Depression; Mental disorders; Mentors; Mentorship; Meta-Analysis; Methods; National Research Service Awards; Nature; Neurobehavioral Manifestations; Occupations; Online Systems; Paper; Parents; Pathway Analysis; Physiological; Positioning Attribute; Psychological Theory; Psychology; Psychopathology; Publishing; Randomized; Recovery; Research; Research Personnel; Research Project Grants; Sampling; Schools; Science; Secure; Services; Statistical Methods; Stress; Structure; Support System; Symptoms; Techniques; Testing; Time; Training; Universities; Withdrawal; Work; Youth; base; career; child depression; college; depressive symptoms; design; disability; doctoral student; evidence base; experience; faculty mentor; follow-up; improved; innovation; medical schools; multilevel analysis; novel; outcome prediction; pleasure; posters; predicting response; professor; programs; prospective; randomized trial; response; senior faculty; single episode major depressive disorder; social; stem; teacher; tenure track; therapy design; tool; trait; vector

Project Summary/Abstract Major depression (MD) is the leading cause of disability in youth, with a global economic burden of >$210 billion annually. However, up to 70% of youth with MD do not receive services. Even among those who do access treatment, 30-65% fail to respond, demonstrating a need for more potent, accessible interventions. A challenge underlying limited treatment potency is MD's heterogeneity: An MD diagnosis reflects >1400 symptom combinations, creating a need for treatments matched to personal clinical need. Separately, low treatment accessibility stems from the structure of existing interventions. Most span many weeks and are designed for delivery by highly trained clinicians , making them difficult to scale. This proposal aims to address the need for accessible, potent youth MD interventions by integrating methods and findings from previously separate areas: single-session intervention (SSI) research and network science. In a meta-analysis of 50 randomized trials, the investigator has found that SSIs can reduce diverse youth psychiatric problems, including MD. The investigator also found that a web-based SSI teaching growth mindset (the belief that personal traits are malleable) reduced depression and anxiety in high-symptom youth across 9 months. Thus, well-targeted SSIs can yield lasting benefits—but given MD's heterogeneity, there is a need for tools that can match youth to SSIs optimized for personal symptom structures. The proposed project harnesses computational advances from the network approach to psychopathology, which views psychiatric disorders as causal interactions between symptoms, to evaluate such a tool. The first goal is to establish a new method of characterizing MD symptom structures; the second is to test parameters from these structures as predictors of response to two SSIs targeting distinct MD features (behavioral vs. cognitive symptoms). Specifically, Aim 1 is to establish guidelines for computing personalized symptom networks using experience sampling method (ESM) data from youth with MD collected 7x/day for 3 weeks (N=50, ages 11-16; 147 time-points each). This will include a comparison of two leading approaches for computing network parameters, such as outward centrality (the degree to which a symptom prospectively predicts other symptoms). Aim 2 is to test network parameters as SSI outcome predictors among youth with MD (N=180). Youth will be randomized to a behavioral activation (BA) SSI (adapted from evidence-based BA SSIs); the mindset SSI noted above; or a control SSI. Network parameters will be tested as predictors of SSI response. For instance, youth with stronger centrality on a behavioral symptom (e.g. withdrawal from pleasurable activities) may respond more favorably to the BA SSI, and youth with stronger centrality on a cognitive symptom (e.g. hopelessness) to the mindset SSI. Results may identify a novel means of matching youth to targeted MD SSIs by personal need. The project will also include the first RCT comparing two youth MD SSIs, with the longest follow-up of any SSI trial to date (2 years), gauging their relative promise to reduce youth MD.

项目总结/摘要 重度抑郁症（MD）是青年残疾的主要原因，全球经济负担超过210美元 每年十亿。然而，高达70%的MD青少年没有得到服务。即使是那些 在获得治疗方面，30-65%的人没有做出反应，这表明需要更有效、更容易获得的干预措施。一 治疗效力有限的潜在挑战是MD的异质性：MD诊断反映>1400 症状组合，创造了与个人临床需求相匹配的治疗需求。单独，低 治疗的可及性源于现有干预措施的结构。大多数跨越数周， 设计用于由训练有素的临床医生提供，使得它们难以扩展。该提案旨在解决 需要通过整合以前的方法和发现， 单独的领域：单次干预（SSI）研究和网络科学。在一项对50名 在随机试验中，研究人员发现SSI可以减少各种青少年精神问题， 包括医学博士。调查人员还发现，基于网络的SSI教学成长心态（相信， 个人特质是可塑性的）在9个月内减少了高症状青年的抑郁和焦虑。 因此，目标明确的SSI可以产生持久的效益-但鉴于MD的异质性，需要工具 可以将年轻人与针对个人症状结构优化的SSI相匹配。拟议项目利用 精神病理学网络方法的计算进步，将精神疾病视为 症状之间的因果关系，以评估这样的工具。第一个目标是建立一种新的方法， 表征MD症状结构;第二个是测试这些结构的参数作为MD症状的预测因子。 对针对不同MD特征（行为与认知症状）的两种SSI的反应。具体而言，目标1是 建立使用经验抽样方法计算个性化症状网络的指南 (ESM)从患有MD的青年收集数据，每天7次，持续3周（N=50，年龄11-16;每个147个时间点）。这 将包括计算网络参数的两种主要方法的比较，例如向外 中心性（一种症状预测其他症状的程度）。目标2是测试网络 参数作为青年MD患者SSI结局的预测因子（N=180）。青年将被随机分配到 行为激活（BA）SSI（改编自基于证据的BA SSI）;上述心态SSI;或 控制SSI。网络参数将作为SSI响应的预测因子进行测试。例如，年轻人拥有更强的 对行为症状的集中性（例如从愉快的活动中退缩）可能会对 BA SSI，以及在认知症状（例如绝望）上具有较强中心性的青年对心态SSI。 结果可能会确定一种新的手段匹配青年有针对性的MD SSI的个人需要。该项目将 还包括第一个比较两个青年MD SSI的RCT，迄今为止随访时间最长的任何SSI试验（2 年），衡量他们的相对承诺，以减少青年MD。