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

项目摘要/摘要 青少年罹患临床抑郁症的风险不断上升，这可能导致终生发病率。 和死亡率。可能导致这种风险的神经、身体、认知和社会情绪的变化也 暗示有机会进行高影响力的干预。不幸的是，心理治疗试验显示 对青少年抑郁症的影响。为了改善青少年的长期结果，这项研究将确定- 可以指导个性化治疗的青少年抑郁症的特定驱动因素。之前的研究与 抑郁或焦虑的成年人表明这种司机的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)、可修改的、并表现出个体差异的。 针对这些司机的认知行为治疗(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。识别以下人士- 青春期的特定驱动因素可能会影响同时考虑发育和个体因素的治疗 差异转移了抑郁症的发病和维持轨迹。调查特定于个人的司机 通常包括通过基于智能手机的生态瞬间对自我报告体验进行密集调查 评估(EMA)。越来越多的证据表明，智能手机也可以通过被动的方式监控情绪 以最小的反应负担感知抑郁相关行为。然而，几乎所有这样的研究都有 这项调查是在成年人中进行的，尽管在美国青少年中几乎普遍拥有智能手机。 因此，这项研究将利用抑郁青少年的日常智能手机使用情况来评估 针对已建立的动态方法(即EMA和活动记录法)进行移动感知，以识别特定的人 青少年抑郁症的驱动因素。有抑郁症状的50名青少年(12-18岁)将 参与为期30天的：a)基于智能手机的抑郁症状、过程和影响的EMA(每天4次)， 睡眠日记(每天1次)；(B)活动、体力活动、睡眠、自然语言使用的移动感知，以打字形式 人际沟通、上屏时间和通话频率/持续时间；以及(C)手腕动作图 活动和睡眠。青少年和照顾者将完成诊断性访谈和其他措施(例如， 开发、临床、研究领域标准)，以及后续的用户反馈访谈- 向上。为了解决研究目标：1)EMA症状的具体的、受试者内的网络将被建模为 确定每个青少年的司机；2)EMA、移动传感器和活动图测量之间的相关性 睡眠、体力和社交活动；以及对核心抑郁症状的机器学习预测(自我 报告的情绪和快感缺乏)将用于评估移动传感识别个人的有效性- 特定的驱动因素；3)将研究对象间的基线特征作为特定个人的预测因素 司机。这些结果将为基于智能手机的可扩展、低负担工具的未来开发提供参考，该工具可以 指导抑郁青少年的个人化治疗决定，对公共卫生有潜在影响。

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.