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

超过1500万美国人担任阿尔茨海默病（AD）和AD亲属的家庭照顾者。 相关痴呆症（ADRD），这往往使他们承受巨大的压力，导致精神状况较差， 健康和身体疾病的风险更高。由于情绪和身体疲惫，缺乏时间， 照顾者在满足与照顾亲人有关的迫切需要时，往往放弃自己的自我照顾。的 国家老龄问题研究所战略计划确定了制定更好的干预措施以改善 照顾者的身心健康是一个重要的优先领域。这个保罗B的目的。比森K76 新兴领导者职业发展奖在老龄化研究的应用是支持研究 费利佩·贾恩博士的培训，他是哈佛医学院的精神病学家。Jain博士的工作旨在改善护理人员 通过智能手机远程提供的技能培训，包括引导图像和正念疗法， 压力，并帮助照顾者改善心理化（理解心理和行为之间的联系）， 他们自己，他们所爱的人患有痴呆症和其他人在他们的社会环境。此外，贾恩博士希望 发展机器学习和数据科学方面的技能，以估计护理人员的早期变化。 症状远程和被动，没有任何额外的努力，对部分的照顾者谁往往是 已经不堪重负，使用智能手机传感器捕捉有关护理人员行为的信息。 在K76奖的实施过程中，Jain博士将领导一项随机对照试验，治疗120例AD/ADRD患者。 60岁或以上的照顾者。护理人员将被分配接收智能手机应用程序， 包括单独的护理人员技能工具箱，或护理人员技能工具箱与心智化想象相结合 治疗（MIT）。麻省理工学院使用引导意象和正念帮助护理人员改善压力，减少负面影响 情绪和增加心理化。从理论上讲，由于正念和冥想， 技能和更好的心理接受者和其他人应该帮助照顾者更好地实施工具， 在他们独特的社会环境中生存，并考虑到接受护理者的个人症状。 该研究的第一个目的是确定应用程序提供的护理人员技能的临床效果， 照顾者知觉压力、照顾者负担、掌握、忧郁与失眠之研究。第二个目标是 从智能手机传感器中开发与结果相关的行为标记。我们将（1）测试 智能手机估计睡眠与照顾者自我报告的纵向相关的假设 失眠、压力和负担;（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辟一条新的途径， AD/ADRD护理人员的研究和治疗侧重于改善心理化。它还将向外地通报 关于使用智能手机传感器检测早期行为变化的可行性的衰老研究 临床医生可以使用这些标记来干预有不良结局风险的老年人。

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.