Social Cognitive Mechanisms Underlying Disclosure and Help Seeking Behavior in Late-Life Suicide

项目总结/摘要 本提案响应RFA-MH-22-135。在尝试之前不披露自杀意念是规范的 在以后的生活中更常见。同样，我们的初步数据表明， 在自杀安全计划中的作用。不幸的是，增加非正式帮助的干预措施 到目前为止，寻求是无效的。因此，在人际交往过程中存在着主要的障碍， 对于预防晚年自杀至关重要，需要新的目标来增加寻求帮助的行为。的 这个纵向观察项目的目的是了解为什么老年人不披露自杀 在自杀危机中产生意念或寻求他人帮助。我们专注于社会认知能力和偏见， 影响个体如何感知（或误解）他人，并且以前与 孤独、自杀意念和行为。这项研究的前提是，社会认知障碍 影响了关系中涉及的积极和消极的效价系统以及决定关系的社会结构， 寻求帮助自杀。为了促进这项研究，我们的小组开发并验证了一个二元社会 客观量化支持中涉及的RDoC积极和消极效价系统的隶属任务 寻找该任务能够跨多个分析单元评估效价系统，包括行为， 自我报告、面部情感和自然语言处理。测量这些内在过程将是 通过结构性社会网络评估，与外部求助机会的量化相结合。一 我们提出的社会网络分析的翻译方面是增加网络测量与 自杀安全计划的社会因素。在一项纵向研究中，我们将招募一个不同年龄的样本， 来自各种紧急、初级和精神卫生保健机构的成年人。招募将按以下因素分层： 由当前活跃的自杀意念、无意念或企图史的抑郁症状定义的组， 和健康的比较者。在研究的目标1中，我们将比较社会认知、积极和 负效价指标从我们的社会关系任务，沿着与社会网络结构，包括过去 寻求帮助和披露的亚组与当前的自杀意念。在目标2中，我们将管理移动的 情绪识别任务和社会联系在预测实时自杀披露通过生态 30天内的临时评估。然后，我们将评估社会认知和归属标记是否 通过一年以上收集的纵向数据预测求助和自杀意念的轨迹。 探索性分析将利用通过应用时间序列网络分析和自然 语言处理来模拟不公开的过程。我们的长期目标是将其转化为 研究新的个性化干预措施，改善人际交往过程嵌入在当前 和预防老年人自杀的新方法。该提案直接响应NIMH战略 计划，目标2.2和3.2，以及NIMH在数字心理健康方面的优先领域。

PROJECT SUMMARY/ABSTRACT This proposal responds to RFA-MH-22-135. Non-disclosure of suicide ideation prior to attempt is normative and even more common in later life. Similarly, our preliminary data indicates lower inclusion of social contacts in suicide safety plans among at-risk older adults. Unfortunately, interventions to increase informal help seeking are ineffective to date. Therefore, major roadblocks exist in the interpersonal processes seen as essential to late-life suicide prevention, and new targets are needed for increasing help-seeking behavior. The purpose of this longitudinal observational project is to understand why older adults do not disclose suicide ideation or seek help from others amidst suicide crises. We focus on social cognitive abilities and biases, which influence how individuals perceive (or misperceive) others and have previously been associated with loneliness, suicidal ideation and behavior. The premise of this research is that social cognitive impairments impact the positive and negative valence systems involved in affiliation and the social structures that determine help seeking in suicide. Facilitating this research, our group has developed and validated a dyadic social affiliation task that objectively quantifies RDoC positive and negative valence systems involved in support seeking. This task enables evaluation of valence systems across multiple units of analysis, including behavior, self-report, facial affect and natural language processing. Measurement of these intrinsic processes will be integrated with quantification of extrinsic help-seeking opportunity, via structural social network assessment. A translational aspect of our proposed social network analyses is to augment network measurement with the social elements of suicide safety planning. In a longitudinal study, we will recruit a sample of diverse older adults from a variety of urgent, primary, and mental health care settings. Recruitment will be stratified by groups defined by current active suicide ideation, depressive symptoms without ideation or attempt histories, and healthy comparators. In Aim 1 of the study, we will compare groups on social cognition, positive and negative valence indicators from our social affiliation task, along with social network structure, including past help seeking and disclosure in the subgroup with current suicide ideation. In Aim 2, we will administer mobile emotion recognition tasks and social affiliation in predicting in real-time suicide disclosure through ecological momentary assessment over 30 days. We will then evaluate whether social cognition and affiliation markers predict trajectories of help seeking and suicide ideation through longitudinal data gathered over one year. Exploratory analyses will leverage the data derived by applying time series network analyses and natural language processing to model processes underlying non-disclosure. Our long-term goal is to translate this research to novel personalized interventions that improve interpersonal processes embedded in both current and novel suicide prevention approaches for older adults. This proposal responds directly to NIMH Strategic Plan, Aim 2.2 and 3.2, and the NIMH’s Priority Area in Digital Mental Health.