Mechanisms and Predictors of Change in App-Based Mindfulness Training for Adolescents

基于应用程序的青少年正念训练变化的机制和预测因素

• 批准号: 10651776
• 负责人: Christian Anthony Webb
• 金额: $ 62.76万
• 依托单位: MCLEAN HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10651776
• 关键词: Accounting; Address; Adolescent; Adverse effects; Adverse event; Affective; Age; Algorithms; American; Anxiety; Attention; Brain; Cellular Phone; Characteristics; Clinical; Clinical Trials; Cognitive; Communication; Control Groups; Coupled; Development; Diagnostic; Ecological momentary assessment; Elasticity; Emotions; Goals; Grain; Individual; Intervention; Link; Machine Learning; Measures; Mediating; Mental Depression; Mindfulness Training; Modeling; National Center for Complementary and Integrative Health; Neurobiology; Online Systems; Outcome; Panic; Patient Self-Report; Pattern; Persons; Predictive Value; Prevalence; Probability; Prognosis; Protocols documentation; Randomized; Recommendation; Research; Research Activity; Rest; Risk Factors; Risk-Benefit Assessment; Sensitivity and Specificity; Strategic Planning; Surveys; Symptoms; Teenagers; Testing; Thinking; Work; Youth; active control; adverse outcome; algorithm development; anxiety symptoms; attentional control; cognitive task; coping; cost; cost effective; depressive symptoms; efficacy evaluation; experience; functional MRI scan; improved; kernel methods; machine learning algorithm; machine learning model; mindfulness; mindfulness meditation; neural; neuromechanism; outcome prediction; personalized medicine; personalized predictions; post intervention; predicting response; programs; psychosocial; rumination; skills; smartphone application

Project Summary/Abstract Mindfulness-based smartphone apps have surged in popularity in recent years. Headspace – among the most popular of these platforms – has over 42 million users. Recent surveys indicate that 11% of U.S. adolescents have used mindfulness apps as a means of coping with anxiety or depressive symptoms, which increase substantially during the adolescent years. A growing body of research implicates rumination as being a transdiagnostic risk factor involved in the development of depression and anxiety in youth. Critically, mindfulness meditation has shown significant promise in targeting rumination, and ultimately improving depressive and anxiety symptoms. Mindfulness apps offer a convenient and cost-effective means for accessing mindfulness training, while being interactive and engaging for youth. Despite their growing popularity among teens, strikingly little research has been conducted on these apps. Two critical questions have yet to be addressed, which are strongly aligned with the NCCIH Strategic Plan: (1) what are the underlying neural and cognitive mechanisms that account for the beneficial effects of these apps and (2) for whom is app-based mindfulness well-suited. To address these gaps, adolescents (ages 13-18) will be randomly assigned to an app-delivered mindfulness course vs. an active control condition and will complete pre- and post-intervention resting state functional magnetic resonance imaging (fMRI) scans to probe static and dynamic functional connectivity within – and between – brain networks strongly implicated in mindfulness training and rumination (i.e., Default Mode Network and Salience Network). In addition, cognitive tasks will be administered at pre- and post-intervention to assess attentional control abilities putatively enhanced by mindfulness training. Finally, mindfulness skills and changes in rumination will be assessed via a smartphone-based ecological momentary assessment (EMA) protocol developed in the PI’s lab. First, we will test whether changes in (1) brain functional connectivity, (2) attentional control and (3) acquisition and use of mindfulness skills mediate between-group (i.e., app vs. control) differences in the reduction of rumination. Second, we will test whether a machine learning model incorporating baseline clinical, demographic, and psychosocial characteristics can be used to identify which adolescents are predicted to benefit from app-based mindfulness training. Recent advances in machine learning allow for the development of algorithms predicting outcome at the individual level, as well as the integration of numerous predictors rather than relying on single variables that may, in isolation, have limited clinically-useful predictive value. Ultimately, such an algorithm may inform individual risk-benefit assessments that could be used to objectively communicate the probability of experiencing positive vs. adverse outcomes to users prior to engaging with a mindfulness app. Collectively, results are expected to advance (1) our understanding of the underlying mechanisms that account for the beneficial effects of app-based mindfulness training and (2) our ability to predict which adolescents are well-suited to these increasingly popular apps.

项目摘要/摘要 近年来，基于正念的智能手机应用程序越来越受欢迎。顶空 - 最重要的 这些平台很受欢迎 - 拥有超过4200万用户。最近的调查表明，11％的美国青少年 使用正念应用程序作为应对焦虑或抑郁症状的一种手段，这会增加 在青少年期间基本上。越来越多的研究意味着反思是 经诊断危险因素参与青年人的抑郁和焦虑的发展。批判性， 正念冥想在靶向反省方面表现出了巨大的希望，并最终改善了 抑郁和焦虑症状。正念应用程序为 接受正念训练，同时互动和吸引青年。尽管他们越来越受欢迎 在青少年中，对这些应用程序的研究很少。尚未有两个关键问题 解决的问题，与NCCIH战略计划密切相符：（1）基础神经和什么是什么 考虑这些应用程序的有益效果的认知机制和（2）基于应用程序 正念非常适合。为了解决这些差距，青少年（13-18岁）将随机分配给 应用的正念课程与主动控制条件，并将完成干预前后 静止状态功能磁共振成像（fMRI）扫描以探测静态和动态功能 在正念训练和反省的大脑网络内部以及之间的连接性 （即默认模式网络和显着性网络）。此外，认知任务将在前和 干预后评估注意力控制能力，可以通过正念训练提高。最后， 正念技巧和反省的变化将通过基于智能手机的生态瞬间评估 评估（EMA）协议在PI实验室中开发了。首先，我们将测试（1）大脑功能的变化 连通性，（2）注意力控制和（3）获得和使用正念技能会导致组间 （即应用程序与控制）的减少差​​异。其次，我们将测试是否机器 编码基线临床，人群和社会心理特征的学习模型可用于 确定哪些青少年可以从基于应用的正念培训中受益。最近的进步 机器学习允许开发算法，以预测个人层面的结果，以及 众多预测变量的集成，而不是依赖于可能有限的单个变量 临床上使用的预测价值。最终，这种算法可能会为个人风险效益评估提供信息 可以用来客观地传达经历正面与不利结果的可能性 在使用正念应用程序之前，用户。总的来说，结果有望进步（1）我们的 理解基于应用程序的正念的有益影响的基本机制 培训和（2）我们预测哪些青少年非常适合这些越来越受欢迎的应用程序的能力。