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

通过主动和被动智能手机数据的具体网络建模来识别青少年抑郁症的特定个人驱动因素

• 批准号: 10196290
• 负责人: Mei Yi Ng
• 金额: $ 19.83万
• 依托单位: FLORIDA INTERNATIONAL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-14 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10196290
• 关键词: 18 year old; Address; Adolescence; Adolescent; Adult; Affect; Algorithms; Anhedonia; Behavior; Benchmarking; Caregivers; Cellular Phone; Characteristics; Clinical; Clinical Research; Cognitive; Cognitive Therapy; Data; Data Collection; Depressed mood; Development; Diagnostic; Disease; Ecological momentary assessment; Exhibits; Feedback; Frequencies; Future; Heterogeneity; Individual; Individual Differences; Influentials; Intervention; Interview; Irritable Mood; Lead; Machine Learning; Maintenance; Major Depressive Disorder; Measurement; Measures; Mental Depression; Methods; Modeling; Monitor; Moods; Morbidity - disease rate; Outcome; Ownership; Parents; Participant; Patient Self-Report; Personal Communication; Persons; Physical activity; Physiology; Process; Psychopathology; Psychotherapy; Public Health; Publishing; Research; Research Domain Criteria; Research Personnel; Risk; Signal Transduction; Sleep; Subgroup; Surveys; Symptoms; Teenagers; Testing; Therapeutic; Time; Validation; Work; Wrist; Youth; actigraphy; anxious; base; child depression; depressive symptoms; diaries; experience; follow up assessment; follow-up; improved; insight; mortality; natural language; network models; novel; personalized decision; personalized intervention; personalized medicine; prospective; psychologic; racial and ethnic; relating to nervous system; response; sensor; social; socioeconomics; tool; treatment effect

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.

项目摘要/摘要 青少年经历升级临床抑郁症的经验，这可能导致终生发病 和死亡率。 不幸的是，心理治疗试验表明了高影响力的机会。 对青少年抑郁症的影响。 青少年抑郁症的驱动因素可以指导treade治疗个性化。 depsed或焦虑的成年人证明了此类驱动因素的存在 - 症状和相关的processessssssssssssess 具有影响力的（即预测其他症状的变化），可修改和展示差异。 认知行为疗法（CBT）模块的个性化选择和测序以针对这些驱动程序 与历史基准相比，成年人的早期治疗效果比较较大。 在耗时青少年期间的细节可以告知治疗 剥夺和维护的轨迹的差异 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密集的测量 评估（EMA）。 感知与抑郁症相关的行为，但几乎所有此类研究都有 尽管在美国的青少年中拥有几乎通用的智能手机所有权，但还是与成年人一起进行的。 因此，这项研究将利用沮丧的青少年的日常智能手机使用 移动传感针对已建立的门诊方法（即EMA和Actraphy），以识别特定于人的 青少年抑郁症的驱动因素。 参加30天的参与：a）基于智能手机的抑郁症状，过程和影响（4倍/天）的EMA， 睡眠日记（1倍/天）； 人际关系，屏幕时间和呼叫频率/持续时间； 活动和睡眠。 基线的发展，临床，研究领域标准）以及以下方面的反馈访谈 向上解决研究目的：1）习惯，主体内症状的网络将建模为 确定每个青少年的驱动程序； 2） 睡眠，身体和社交活动； 报告的情绪和Anhedonia）将用于评估移动传感对识别人的有效性 - 特定的驱动因素； 3）受试者之间的基线特征将作为特定的预测指标 驱动程序。 指导沮丧的熟悉的个性化信任决策，并具有潜在的公共卫生影响。