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患者亲属的家庭照顾者- 关联性痴呆症(ADRD)，这经常使他们承受巨大的压力，导致精神状况变差 健康和更高的身体疾病风险。由于情绪和身体疲惫，缺乏时间，以及 由于与照顾亲人有关的直接需要，照顾者往往放弃自己的自我照顾。这个 国家老龄研究所战略计划确定有必要制定更好的干预措施，以改善 将照料者的心理和身体健康作为一个关键的优先领域。保罗·B·比森K76的目的 新兴领导者职业发展奖在老龄化研究中的应用是对研究的支持 对哈佛医学院精神病学家Felipe Jain博士的培训。Jain博士的工作旨在改善照顾者 技能培训通过智能手机远程提供，并带有引导图像和正念疗法，可减少 压力和帮助照顾者改善心理化(了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，以及他们社会环境中的其他人。此外，贾恩博士希望 发展必要的机器学习和数据科学技能，以评估照顾者的早期变化 远程和被动的症状，而不需要照顾者做出任何额外的努力 已经不堪重负，使用智能手机传感器捕捉照顾者行为的信息。 在K76奖项的实施过程中，Jain博士将领导一项针对120 AD/ADRD的随机对照试验 照顾者年龄在60岁或以上。照顾者将被指派接受符合以下条件的智能手机应用程序 包括单独护理者技能工具箱，或结合了意象化图像的护理者技能工具箱 治疗(麻省理工学院)麻省理工学院使用引导意象和正念来帮助照顾者减轻压力，减少负面影响 情绪和增加意志力。从理论上讲，麻省理工学院的压力减轻既是由于正念 护理接受者和其他人的技能和更好的指导应该有助于护理人员更好地实施工具 在他们独特的社会环境中进行照顾，并解释受照顾者的个别症状。 这项研究的第一个目的是确定App提供的照顾者技能在有或没有的情况下的临床效果 麻省理工学院对照顾者感受到的压力、照顾者的负担、掌握情况、抑郁和失眠进行了调查。第二个目标是 从智能手机传感器中开发出与结果相关的行为标记。我们将(1)测试 假设智能手机估计的睡眠时间与照顾者的自我报告存在纵向关联 失眠、压力和负担，以及(2)确定机器识别行为特征的可行性 学习预测每天的睡眠和压力。如果成功，这项研究将有助于开辟一条新的途径 AD/ADRD照顾者的研究和治疗侧重于改善精神化。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老化研究 临床医生可用于对有不良预后风险的老年人进行干预的标志物。

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.