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

基于智能手机的数字表型检测身体变形障碍的高风险情感状态

• 批准号: 10221508
• 负责人: Hilary Weingarden
• 金额: $ 18.51万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-13 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10221508
• 关键词: Accelerometer; Acute; Address; Adult; Advisory Committees; Affect; Alcohol consumption; Alcohols; American; Anxiety; Area; Assessment tool; Behavioral; Body Dysmorphic Disorder; Cannabis; Cellular Phone; Clinical; Clinical Research; Clinical assessments; Communication; Data; Detection; Devices; Ecological momentary assessment; Emotions; Ethics; Feeling suicidal; Foundations; General Hospitals; Goals; Gold; Health; Home; Human; In Situ; Individual; Intervention; Life; Massachusetts; Measurement; Measures; Mental Depression; Mental disorders; Mentors; Mentorship; Methods; Monitor; Moods; National Institute of Mental Health; Outcome; Participant; Patient Self-Report; Phenotype; Process; Public Health; Reporting; Reproducibility; Research; Risk; Risk Factors; Ritual compulsion; Sampling; Series; Severities; Shame; Statistical Models; Substance Use Disorder; Suicide; Suicide attempt; Surveys; Technology; Telephone; Testing; Text; Time; Training; Withdrawal; Work; Writing; base; career; career development; completed suicide; digital; emotional distress; experience; high risk; high risk population; indexing; insight; longitudinal analysis; marijuana use; medical schools; modifiable risk; negative affect; programs; reducing suicide; sensor; skills; social; statistical learning; statistics; substance misuse; substance use; suicidal risk; tool

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.

身体营养不良疾病（BDD）与自杀未遂的风险极高（22-28％）和 药物使用障碍（49％），强调了BDD风险检测的至关重要性。负面影响 状态 - 尤其是焦虑和羞耻 - 是有据可查的自杀和使用物质的危险因素 BDD，为风险检测和干预提供明确的目标。这个K23旨在开发和验证 焦虑，冲击和一般负面影响的毫不显着，时间敏感和生态有效的措施 BDD中的状态，使用基于智能手机的数字表型。被动（即不引人注目的）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 检测焦虑的行为特征（避免，仪式），震惊（社交戒断，孤立）和一般 负面影响（总回避，仪式，戒断和隔离特征）。我们将收集被动和 在85名BDD成年人中，主动（即生态瞬时评估[EMA]）智能手机数据，将使用 EMA负面影响作为结果的评分，从被动构建和验证预测统计模型 数据。我们还将检验的假设是，消极影响的被动智能手机可以 显着预测BDD中的次日自杀构想和物质使用，超过常见的临床 风险指数。该项目综合了候选人在BDD中自杀的基于情感的风险的专业知识 她进行智能手机研究的经验。从这个基础建造，该K23将提供关键 在关键领域启动候选人独立研究职业的新培训：（1）数字表型， 包括统计学习和纵向分析； （2）EMA方法； （3）自杀和 使用物质； （4）职业发展，包括R01写作； （5）基于技术的自杀和 药物使用研究。培训目标将通过出色的心态和机构支持来实现 马萨诸塞州综合医院和哈佛医学院。 BDD的领导者Sabine Wilhelm博士和 临床研究将成为主要导师。 Jukka-Pekka Onnela博士，数字表型专家 及其统计方法，以及EMA情绪和自杀研究专家Michael Armey博士将 担任联合官员。咨询将提供EMA和物质使用的互补指南 团队：博士。 Bettina Hoeppner和A. Eden Evins。与NIMH战略目标2一致，该K23将产生 可扩展的，不引人注目的工具，可检测高危急性，可修改的危险因素，以自杀和物质使用 人口。此外，负面影响状态是经诊断的风险因素。作为此证明的下一步 概念K23，候选人将申请R01，以进一步验证被动移动检测负面影响 国家及其对经诊断风险进行预测的能力。该研究计划可以启用（1）个性化 针对高危状态的即时干预措施，以减少自杀和毒品的使用； （2）不引人注目 监视风险变化； （3）风险过程的大规模，生态播种的纵向研究。