# 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

> **NIH NIH K76** · MASSACHUSETTS GENERAL HOSPITAL · 2022 · $242,726

## Abstract

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
ag...

## Key facts

- **NIH application ID:** 10461072
- **Project number:** 5K76AG064390-03
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Felipe A. Jain
- **Activity code:** K76 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $242,726
- **Award type:** 5
- **Project period:** 2020-09-15 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10461072

## Citation

> US National Institutes of Health, RePORTER application 10461072, 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 (5K76AG064390-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10461072. Licensed CC0.

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