Identifying Person-Specific Drivers of Adolescent Depression via Idiographic Network Modeling of Active and Passive Smartphone Data

项目概要/摘要 青少年患临床抑郁症的风险不断上升，这可能导致终身患病 and mortality.可能导致这种风险的神经、身体、认知和社会情绪变化也 这是一个进行高影响力干预机会。不幸的是，心理治疗试验显示， 对青年抑郁症的影响为了改善青少年的长期结果，这项研究将确定个人- 青少年抑郁症的特定驱动因素，可以指导治疗个性化。先前的研究与 抑郁或焦虑的成年人证明了这种驱动症状和相关过程的存在 有影响力的（即，预测其他症状的变化），可修改，并表现出个体差异。 针对这些驱动因素的认知行为治疗（CBT）模块的个性化选择和排序 与历史基准相比，早期成人产生了更大的治疗效果。识别人- 青春期的特定驱动因素可以为治疗提供信息， 差异改变抑郁症发作和维持的轨迹。调查特定人员的驱动因素 通常涉及通过基于智能手机的生态瞬间对自我报告的体验进行深入调查 评估（EMA）。新出现的证据表明，智能手机也可以通过被动监测情绪， 以最小的反应负担感知抑郁相关行为。然而，几乎所有这些研究都 尽管美国青少年几乎普遍拥有智能手机，但这项研究是在成年人中进行的。 因此，这项研究将利用抑郁青少年的日常智能手机使用来评估 移动的感测相对于已建立的流动方法（即，EMA和腕动记录仪），以识别个人特异性 青少年抑郁症的驱动因素50名抑郁症状加重的青少年（12 - 18岁）将 参加30天的：a）基于智能手机的抑郁症状，过程和影响的EMA（4x/天）， 睡眠日记（1x/天）;（B）在打字中移动性、身体活动、睡眠、自然语言使用的移动的感测 人际沟通，屏幕上的时间和呼叫频率/持续时间;以及（c）手腕活动记录物理 活动和睡眠。青少年和照顾者将完成诊断访谈和其他措施（例如， 开发、临床、研究领域标准），以及随访时的用户反馈访谈- 起来为了解决研究目的：1）将对EMA症状的具体受试者内网络进行建模， 识别每个青少年的驱动因素; 2）EMA、移动的传感器和活动记录仪测量之间的相关性， 睡眠，身体和社会活动;以及核心抑郁症状的机器学习预测（自我 报告的情绪和快感缺失）将被用于评估移动的感测用于识别人的有效性- 特定驱动因素; 3）将探索受试者间基线特征作为个体特异性 司机这些结果将为未来开发一种可扩展的、低负担的基于智能手机的工具提供信息， 指导抑郁症青少年的个性化治疗决策，具有潜在的公共卫生影响。

PROJECT SUMMARY/ABSTRACT Adolescents experience escalating risk for developing clinical depression, which can lead to lifelong morbidity and mortality. The neural, physical, cognitive, and socioemotional changes that may contribute to this risk also signal an opportunity for high impact intervention. Unfortunately, psychotherapy trials demonstrate modest effects on youth depression. To improve long-term outcomes for adolescents, this study will identify person- specific drivers of adolescent depression that can guide treatment personalization. Prior research with depressed or anxious adults demonstrates the existence of such drivers—symptoms and related processes that are influential (i.e., predict change in other symptoms), modifiable, and exhibit individual differences. Personalized selection and sequencing of cognitive behavioral therapy (CBT) modules to target these drivers early in adults have produced larger treatment effects compared to a historical benchmark. Identifying person- specific drivers during adolescence could inform treatments that account for both developmental and individual differences to shift the trajectory of depression onset and maintenance. Investigating person-specific drivers usually involves intensive surveying of self-reported experience via smartphone-based ecological momentary assessment (EMA). Emerging evidence suggests that smartphones can also monitor mood through passive sensing of depression-related behaviors with minimal response burden. However, nearly all such studies have been conducted with adults, despite near universal smartphone ownership among adolescents in the US. Thus, this study will leverage depressed adolescents' everyday smartphone use to assess the validity of mobile sensing against established ambulatory methods (i.e., EMA and actigraphy) to identify person-specific drivers of adolescent depression. Fifty adolescents (12–18 years old) with elevated depressive symptoms will participate in 30 days of: a) smartphone-based EMA of depressive symptoms, processes, and affect (4x/day), sleep diary (1x/day); (b) mobile sensing of mobility, physical activity, sleep, natural language use in typed interpersonal communication, screen-on time and call frequency/duration; and (c) wrist actigraphy of physical activity and sleep. Adolescents and caregivers will complete diagnostic interviews and other measures (e.g., developmental, clinical, Research Domain Criteria) at baseline, as well as user feedback interviews at follow- up. To address study aims: 1) idiographic, within-subject networks of EMA symptoms will be modeled to identify each adolescent's drivers; 2) correlations among EMA, mobile sensor, and actigraph measures of sleep, physical, and social activity; and machine learning prediction of core depressive symptoms (self- reported mood and anhedonia) will be used to assess the validity of mobile sensing for identifying person- specific drivers; 3) between-subject baseline characteristics will be explored as predictors of person-specific drivers. These results will inform future development of a scalable, low-burden smartphone-based tool that can guide personalized treatment decisions for depressed adolescents, with potential public health impact.