Mobile app delivered Mentalizing Imagery Therapy to augment remote family dementia caregiver skills training: a pilot randomized, controlled trial with outcomes assessment using digital phenotyping

移动应用程序提供心理意象疗法，以增强远程家庭痴呆症护理人员技能培训：一项随机对照试验，使用数字表型进行结果评估

• 批准号: 10045735
• 负责人: Felipe A. Jain
• 金额: $ 24.19万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10045735
• 关键词: Accounting; Adult; Affect; Age; Age-Years; Aging; Alzheimer' s Disease; Alzheimer' s disease related dementia; American; Area; Attention; Award; Behavior; Behavioral; Brain; Caregiver Burden; Caregiver research; Caregivers; Caring; Cellular Phone; Chronic stress; Data; Data Science; Dementia; Dementia caregivers; Development; Elderly; Emotional; Emotional Stress; Empathy; Exhibits; Face; Family; Family Caregiver; Family member; Feasibility Studies; Friends; Guided imagery; Health Care Costs; Individual; Intervention; K-Series Research Career Programs; Lead; Link; Machine Learning; Measurement; Measures; Mediating; Mental Depression; Mental Health; Mentors; Methods; Mind; Mindfulness Training; Modeling; Monitor; Moods; National Institute on Aging; Nature; Outcome; Outcome Assessment; Participant; Patient Self-Report; Patients; Phenotype; Population; Prefrontal Cortex; Preparation; Psyche structure; Psychiatrist; Public Health; Publishing; Randomized Controlled Trials; Reporting; Research; Research Personnel; Research Support; Research Training; Risk; Role; Schizophrenia; Self Care; Sleep; Sleeplessness; Social Environment; Strategic Planning; Stress; Symptoms; Testing; Time; Training; Treatment Failure; Treatment outcome; Universities; Washington; Work; base; behavioral phenotyping; care giving burden; care recipients; caregiver depression; caregiving; clinical effect; depressive symptoms; digital; exhaustion; follow-up; high risk; improved; loved ones; medical schools; mental imagery; mindfulness; mobile application; multimodality; negative mood; novel therapeutics; perceived stress; physical conditioning; predict clinical outcome; primary caregiver; primary outcome; psychologic; psychological symptom; secondary outcome; sensor; skills; skills training; smartphone Application; social; stress reduction; tool

Over 15 million Americans serve as family caregivers of relatives with Alzheimer’s disease (AD) and AD- Related Dementias (ADRD), and this often subjects them to tremendous stress, resulting in poorer mental health and higher risk of physical illness. Due to emotional and physical exhaustion, lack of time, and immediate needs related to caring for their loved ones, caregivers often forego their own self-care. The National Institute on Aging Strategic Plan identifies the need to develop better interventions to improve the mental and physical health of caregivers as a crucial priority area. The purpose of this Paul B. Beeson K76 Emerging Leaders Career Development Award in Aging Research application is to support the research training of Dr. Felipe Jain, a psychiatrist at Harvard Medical School. Dr. Jain’s work aims to improve caregiver skills training delivered remotely by smartphone with guided imagery and mindfulness therapies that reduce stress and help the caregiver improve mentalizing (understanding the links between mind and behavior) of themselves, their loved one suffering from dementia and others in their social milieu. Further, Dr. Jain hopes to develop the skills in machine learning and data science necessary to estimate early changes in caregiver symptoms remotely and passively, without any additional effort on the part of the caregiver who is often already overwhelmed, using smartphone sensors that capture information about caregiver behaviors. In the conduct of this K76 award, Dr. Jain will lead a randomized, controlled trial for 120 AD/ADRD caregivers 60 years of age or older. Caregivers will be assigned to receive smartphone applications that either include a caregiver skills toolbox alone, or a caregiver skills toolbox combined with Mentalizing Imagery Therapy (MIT). MIT uses guided imagery and mindfulness to help caregivers improve stress, reduce negative mood and increase mentalizing. Theoretically, stress reduction resulting from MIT due both to mindfulness skills and better mentalizing of the care recipient and others should help caregivers better implement tools for caregiving within their unique social environment and accounting for the care recipient’s individual symptoms. The first aim of the study is to determine the clinical effects of App-delivered caregiver skills with or without MIT on caregivers’ perceived stress, caregiver burden, mastery, depression and insomnia. The second aim is to develop behavioral markers from smartphone sensors that are associated with outcomes. We will (1) test the hypothesis that smartphone estimated sleep is longitudinally associated with caregivers’ self-reported insomnia, stress and burden and (2) determine the feasibility of identifying behavioral features with machine learning to predict day-to-day sleep and stress. If successful, this research will help open a new avenue of AD/ADRD caregiver research and treatment focused on improving mentalizing. It will also inform the field of aging research regarding the feasibility of using smartphone sensors to detect changes in early behavioral markers that may be used by clinicians to intervene for older adults at risk of poor outcomes.

超过1500万美国人是阿尔茨海默氏病（AD）和AD-的亲戚的家庭护理人员 相关痴呆症（ADRD），这通常会使他们承受巨大的压力，导致精神较差 健康和较高的身体疾病风险。由于情绪和身体上的疲惫，缺乏时间以及 照顾者经常放弃自己的自我保健，这与照顾亲人有关的直接需求。这 国家老化战略计划研究所确定需要制定更好的干预措施以改善 护理人员作为关键优先领域的心理和身体健康。保罗·B·比森·K76的目的 新兴领导者的老龄化研究申请职业发展奖是为了支持研究 哈佛医学院的精神科医生Felipe Jain博士的培训。 Jain博士的工作旨在改善照顾者 智能手机远程提供的技能培训，以及带有指导的图像和正念疗法，以减少 压力并帮助照顾者改善心理（了解思想与行为之间的联系） 他们自己，他们所爱的人患有痴呆症和其他人在社会环境中。此外，Jain博士希望 发展机器学习和数据科学技能，以估计护理人员的早期变化 远程和被动地出现症状，没有任何额外的努力 已经不知所措，使用智能手机传感器捕获有关护理人员行为的信息。 在该K76奖的行为中，Jain博士将领导120 AD/ADRD的随机对照试验 照料者60岁或以上。护理人员将被分配接收智能手机应用程序 单独使用护理人员技能工具箱，或一个护理人员技能工具箱与心理图像相结合 治疗（麻省理工学院）。麻省理工学院使用指导的图像和正念来帮助护理人员改善压力，减轻负面 情绪并增加心理。理论上的，减轻压力是由于正念而导致的 技能和更好地对护理接收者和其他人进行心理化，应帮助护理人员更好地实施工具 在他们独特的社交环境中照料，并考虑护理人员的个体症状。 该研究的第一个目的是确定或没有或没有 MIT关于看护人的压力，护理人员烧伤，精通，抑郁和失眠。第二个目标是 从与结果相关的智能手机传感器中开发行为标记。我们将（1）测试 假设智能手机估计的睡眠与看护人的自我报告纵向相关 失眠，压力和伯宁以及（2）确定用机器识别行为特征的可行性 学习预测日常睡眠和压力。如果成功，这项研究将有助于开放新的途径 AD/ADRD护理人员的研究和治疗重点是改善心理化。它也将为领域提供信息 关于使用智能手机传感器检测早期行为变化的可行性的老化研究 临床医生可能会用来干预的标志物，以降低结果不佳的老年人。