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
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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.
