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: 10018942
• 负责人: Jessica Lee Schleider
• 金额: $ 40.28万
• 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 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10018942
• 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.

项目摘要/摘要 重度抑郁症（MD）是青年人残疾的主要原因，全球经济燃烧> 210美元 每年十亿美元。但是，多达70％的MD青年没有获得服务。即使在那些这样做的人中 访问治疗30-65％无法做出反应，表明需要更多潜在的可访问干预措施。一个 挑战有限的治疗效力是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的需求。单独，低 治疗可及性源于现有干预措施的结构。大多数星期跨越了 专为受训练有素的临床医生提供的交付而设计，使其难以扩展。该建议旨在解决 通过整合以前的方法和发现，需要进行可访问，潜在的青年MD干预措施 单独的领域：单会干预（SSI）研究和网络科学。在50的荟萃分析中 随机试验，研究人员发现SSIS可以减少潜水员青年精神病问题， 包括MD。研究人员还发现，基于网络的SSI教学增长心态（相信 在9个月的高症状青年中，个人特征可延长）减少抑郁症和动画。 这是针对性的SSIS可以产生持久的好处，但是鉴于MD的异质性，需要工具 可以将青年与针对个人症状结构进行优化的SSI相匹配。拟议的项目安全 从网络方法到精神病理学的计算进步，该方法将精神疾病视为 症状之间的因果相互作用，以评估这种工具。第一个目标是建立一种新方法 表征MD符号结构；第二个是从这些结构中测试参数作为预测指标 对两个靶向不同MD特征的SSI（行为与认知症​​状）的响应。具体来说，目标1是 使用经验抽样方法建立计算个性化符号网络的准则 （ESM）来自MD的青年的数据收集了7倍/天3周（n = 50，年龄11-16；每个时间点147个时间点）。这 将包括对计算网络参数的两种领先方法的比较，例如向外 中心（症状前瞻性预测其他症状的程度）。目标2是测试网络 参数作为MD青年的SSI结果预测因子（n = 180）。年轻人将被随机地 行为激活（BA）SSI（改编自证据基于证据的BA SSI）；上面指出的心态SSI；或a 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 行为症状的中心（例如，退出令人愉悦的活动）可能对 BA SSI和对心态SSI的认知症状（例如绝望）的中心性更强的青年。 结果可能会确定一种新颖的方法，即通过个人需求将青年与有针对性的MD SSI相匹配。该项目将 还包括第一个比较两个青年MD SSI的RCT，以及迄今为止对任何SSI试验的随访最长（2个） 多年），衡量他们对减少青年医学博士的相对承诺。