Smartphone-based digital phenotyping to detect high-risk affect states in body dysmorphic disorder

身体畸形恐惧症（BDD）与极高的自杀企图风险相关（22-28%）， 物质使用障碍（49%），强调了BDD风险检测的重要性。消极情感 状态-特别是焦虑和羞耻-是自杀和物质使用的风险因素， BDD，为风险检测和干预提供明确的目标。K23旨在开发和验证 对焦虑、羞耻和一般负面情绪进行不引人注目、时间敏感和生态有效的测量 国家在BDD，使用智能手机为基础的数字表型。被动（即，不显眼）智能手机 负面影响状态的测量将基于GPS、加速度计和通信日志，用于 检测焦虑（回避，仪式），羞耻（社会退缩，孤立）和一般的行为特征 负面影响（聚集的回避，仪式，退缩和孤立特征）。我们将收集被动和 活动的（即，生态瞬时评估[EMA]）85名BDD成年人的智能手机数据，并将使用 EMA将负面影响评级作为结果，以建立和验证被动预测统计模型 数据我们还将测试假设，即消极情绪状态的被动智能手机测量可以 显著预测BDD患者次日自杀意念和物质使用， 风险指数。该项目综合了候选人在BDD中基于情感的自杀风险方面的专业知识， 她进行智能手机研究的经验。在此基础上，K23将提供关键的 在关键领域的新培训，以启动候选人的独立研究生涯：（1）数字表型， 包括统计学习和纵向分析;（2）EMA方法;（3）自杀评估， 物质使用;（4）职业发展，包括R 01写作;（5）基于技术的自杀和 物质使用研究。培训目标将在一流的指导和机构支持下完成， 马萨诸塞州总医院和哈佛医学院。Sabine Wilhelm博士，BDD的领导者， 临床研究，将担任主要导师。Jukka-Pekka Onnela博士，数字表型专家 和它的统计方法，以及EMA情绪和自杀研究专家Michael Armey博士，将 担任共同导师。EMA和物质使用的补充指南将由咨询机构提供。 团队：Bettina Hoeppner和A.伊登·埃文斯根据NIMH战略目标2，该K23将产生 可扩展的，不显眼的工具，以检测急性，可改变的自杀和物质使用的风险因素，在一个高风险的 人口此外，负面情绪状态是转诊断的风险因素。作为这个证明的下一步- 概念K23，候选人将申请R 01，以进一步验证消极影响的被动移动的检测 国家和他们的能力，以预测风险transdiagnotic。这项研究计划可以使（1）个性化 针对高风险受影响国家的及时干预措施，以减少自杀和药物使用;（2）不引人注目 监测风险的变化;（3）大规模的，生态有效的风险过程的纵向研究。

Body dysmorphic disorder (BDD) is associated with extremely high risk for suicide attempts (22-28%) and substance use disorders (49%), underscoring the critical importance of risk detection in BDD. Negative affect states - particularly anxiety and shame - are well-documented risk factors for suicide and substance use in BDD, offering clear targets for risk detection and intervention. This K23 aims to develop and validate unobtrusive, time-sensitive, and ecologically valid measures of anxiety, shame, and general negative affect states in BDD, using smartphone-based digital phenotyping. Passive (i.e., unobtrusive) smartphone measurement of negative affect states will be based on GPS, accelerometer, and communication logs, used to detect behavioral features of anxiety (avoidance, rituals), shame (social withdrawal, isolation), and general negative affect (aggregated avoidance, rituals, withdrawal, and isolation features). We will collect passive and active (i.e., ecological momentary assessment [EMA]) smartphone data in 85 adults with BDD and will use EMA ratings of negative affect as outcomes, to build and validate predictive statistical models from passive data. We will also test the hypotheses that passive smartphone measures of negative affect states can significantly predict next-day suicidal ideation and substance use in BDD, above and beyond common clinical indices of risk. This project synthesizes the Candidate’s expertise in emotion-based risk for suicide in BDD with her experience conducting smartphone research. Building from this foundation, this K23 will provide critical new training in key areas to launch the Candidate’s independent research career: (1) digital phenotyping, including statistical learning and longitudinal analysis; (2) EMA methods; (3) assessment of suicide and substance use; (4) career development, including R01 writing; and (5) ethics of technology-based suicide and substance use research. Training goals will be accomplished with stellar mentorship and institutional support at Massachusetts General Hospital and Harvard Medical School. Dr. Sabine Wilhelm, a leader in BDD and clinical research, will serve as the primary mentor. Dr. Jukka-Pekka Onnela, an expert in digital phenotyping and its statistical approaches, and Dr. Michael Armey, an expert in EMA research of emotions and suicide, will serve as co-mentors. Complementary guidance in EMA and substance use will be provided by the advisory team: Drs. Bettina Hoeppner and A. Eden Evins. In line with NIMH Strategic Objective 2, this K23 will yield scalable, unobtrusive tools to detect acute, modifiable risk factors for suicide and substance use in a high-risk population. Moreover, negative affect states are transdiagnostic risk factors. As a next step to this proof-of- concept K23, the Candidate will apply for an R01 to further validate passive mobile detection of negative affect states and their ability to predict risk transdiagnostically. This program of research can enable (1) personalized just-in-time interventions targeting high-risk affect states, to reduce suicide and substance use; (2) unobtrusive monitoring of changes in risk; and (3) large-scale, ecologically-valid longitudinal research of risk processes.