The impact of social media use on precursors of adolescent suicide risk: Aprospective study

社交媒体使用对青少年自杀风险前兆的影响：前瞻性研究

• 批准号: 10212954
• 负责人: Jessica Leigh Hamilton
• 金额: $ 15.78万
• 依托单位: RUTGERS, THE STATE UNIV OF N.J.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10212954
• 关键词: Address; Adolescent; Adolescent Development; Affect; Affective; Age; Arousal; Attenuated; Beds; Behavior; Cause of Death; Cellular Phone; Clinical; Cognitive; Data; Depressed mood; Depression and Suicide; Development; Ecological momentary assessment; Environment; Feeling suicidal; Foundations; Future; Goals; Growth; Health; Interview; Link; Longitudinal Studies; Measures; Mediating; Mental Depression; Mental Health; Mentors; Mentorship; Methodology; Methods; Modeling; Monitor; Moods; Nature; Outcome; Participant; Pathway interactions; Patient Self-Report; Pattern; Persons; Population; Positioning Attribute; Prevention; Prevention program; Prospective Studies; Psychiatry; Public Health; Research; Research Personnel; Risk; Risk Factors; Sampling; Scientist; Silk; Sleep; Sleep disturbances; Social Environment; Suicide; Suicide prevention; Techniques; Technology; Telephone; Testing; Time; Training; Universities; Visit; Youth; actigraphy; adolescent suicide; boys; career; child depression; clinically actionable; depressive symptoms; design; digital media; girls; high dimensionality; high risk; high-risk adolescents; ideation; innovation; machine learning algorithm; mobile computing; modifiable risk; mortality; novel; personalized intervention; prevent; programs; prospective; rapid growth; recruit; secondary analysis; sex; skills; sleep onset; social; social media; statistical and machine learning; statistics; success; suicidal behavior; suicidal risk; supervised learning; therapy development

PROJECT SUMMARY/ABSTRACT Suicide is the second leading cause of adolescent mortality in the US. Rates of adolescent suicide have increased in recent years, along with the advent and growth of social media use (SMU) in this population. SMU has been linked with sleep disruption, depressed mood, and suicidal ideation (SI), all of which are precursors of suicidal behavior. Existing research on the impact of SMU on sleep, mood, and suicidality is limited by cross- sectional designs and self-reported measures of SMU and sleep. To advance the field, prospective designs are needed that include objective measures and intensive monitoring of high-risk samples to rigorously examine the temporal relationships between SMU and suicide risk, defined as depressed mood and SI. This K01 proposal uses precisely such a design to test a conceptual model in which SMU predicts sleep disruption, which, in turn, contributes to depressed mood and SI among adolescents. The proposed study will harness smartphone technology to assess SMU using real-time passive data capture, actigraphy, and ecological momentary assessment (EMA) in high-risk adolescents. These methods, combined with in-person study visits, will examine the temporal and unique within-person associations between SMU and sleep disruption (Aim 1), SMU and suicide risk (Aim 2), and test whether sleep disruption mediates the relationship between SMU and suicide risk (Aim 3). Data-driven approaches of supervised machine learning will leverage the high-dimensional, intensively- monitored data to identify key SMU features predictive of suicide risk in adolescents (Exploratory Aim). The candidate will build on her strong foundation in adolescent depression and sleep research by acquiring new conceptual and methodological training in: 1) adolescent suicide risk, 2) technology and mental health, including social media use and mobile technology for real-time assessment; and 3) advanced computational skills. The candidate has assembled an interdisciplinary mentorship team to achieve her training goals within the exceptional environment of the Department of Psychiatry at the University of Pittsburgh. Mentors (Brent and Moreno) are experts in adolescent suicide and social media, respectively, and both have had success in mentoring early career scientists. The candidate’s consultants have extensive expertise in adolescent development and EMA (Silk), adolescent sleep and actigraphy (Franzen), smartphone sensing (Ferreira) applied to clinical health (Low), and advanced statistical and machine learning approaches (Wallace). The proposed training and research will inform future R01 studies that use these innovative methods to identify and modify clinically-actionable risk factors, including SMU and sleep disruption, to attenuate near-term risk for adolescent suicide. This program of research has the potential to yield high-impact results that inform the development of suicide prevention programs that are personalized, scalable, and delivered in real time. The proposed research along with this training plan will uniquely position the candidate to be an independent investigator and leading scholar in the important public health problem of adolescent suicide.

项目总结/摘要 自杀是美国青少年死亡的第二大原因。青少年自杀率 近年来，沿着社交媒体使用（SMU）在这一人群中的出现和增长，这种情况有所增加。SMU 与睡眠中断，抑郁情绪和自杀意念（SI）有关，所有这些都是 自杀行为关于SMU对睡眠、情绪和自杀倾向影响的现有研究受到交叉研究的限制。 分段设计和自我报告的SMU和睡眠测量。为了推进这一领域， 需要包括客观措施和对高风险样本的密集监测， SMU和自杀风险之间的时间关系，定义为抑郁情绪和SI。K 01提案 正是使用这样的设计来测试一个概念模型，其中SMU预测睡眠中断，这反过来， 有助于青少年的抑郁情绪和SI。这项研究将利用智能手机 技术评估 SMU使用 实时被动数据采集、活动记录和生态瞬时 在高危青少年中进行EMA评估。这些方法，结合亲自考察，将检查 SMU和睡眠中断之间的时间和独特的人内关联（目标1），SMU和 自杀风险（目标2），并测试睡眠中断是否介导SMU和自杀风险之间的关系 (Aim 3）。数据驱动的监督机器学习方法将利用高维、密集的 监测数据，以确定预测青少年自杀风险的关键SMU特征（探索性目的）。的 候选人将建立在她在青少年抑郁症和睡眠研究的坚实基础上，通过获得新的 概念和方法培训：1）青少年自杀风险，2）技术和心理健康，包括 利用社交媒体和移动的技术进行实时评估;以及3）高级计算技能。的 候选人已经组建了一个跨学科的导师团队，以实现她在 匹兹堡大学精神病学系的特殊环境。导师（布伦特和 Moreno）分别是青少年自杀和社交媒体方面的专家，两人都成功地 指导早期职业科学家。候选人的顾问在青少年问题上有广泛的专业知识 应用了发育和EMA（Silk）、青少年睡眠和体动记录（Franzen）、智能手机感知（Ferreira） 临床健康（低），先进的统计和机器学习方法（华莱士）。拟议 培训和研究将为未来的R 01研究提供信息，这些研究将使用这些创新方法来识别和修改 临床可行的风险因素，包括SMU和睡眠中断，以减轻青少年的近期风险 自杀这项研究计划有可能产生高影响力的结果，为发展提供信息。 自杀预防计划是个性化的，可扩展的，并在真实的时间交付。拟议研究 沿着本培训计划将使候选人成为一名独立的调查员， 这是一个重要的公共卫生问题，青少年自杀。