# Adapting evidence-based behavioral interventions to address non-communicable diseases and common mental health conditions among people with dementia or mild cognitive impairment

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $290,502

## Abstract

Project Abstract for Supplement:
This project will adapt the BEhavioral Community-based COmbined Intervention for MEntal Health and
Noncommunicable Diseases (BECOME) intervention to fit the unique needs of people with dementia or mild
cognitive impairment and their caregivers to manage non-communicable diseases (NCDs), like diabetes and
hypertension, and common mental disorders (CMDs), like depression and anxiety. The comorbidities among
people with dementia or mild cognitive impairment with NCDs and CMDs negatively impact cognitive
functioning, exacerbate neuropsychiatric symptoms, increase caregiver burden, and reduce overall quality of
life. Although there is no definitive cure for Alzheimer's Disease and Related Dementias, managing co-
occurring CMDs and NCDs can decelerate disease progression and alleviate caregiver stress. Medications
side effects and concerns about polypharmacy to manage NCDs and CMDs make behavioral interventions
attractive, especially for people with dementia or mild cognitive impairment. There is considerable evidence
that supports the effectiveness of skills-based behavioral interventions in improving mental health benefiting
both patients and caregivers. Despite the significance of these interventions, challenges persist. Interventions
need to be adapted to accommodate the cognitive capacity of people with dementia or mild cognitive
impairment, and incorporation of caregiver’s preferences, capacities, and needs.
The parent project, BECOME, integrates three evidence-based behavioral skills, behavioral activation,
evidence-based stress reduction and motivational interviewing targeting depression, anxiety and behavioral
changes respectively. The interventions are delivered by community health workers (CHWs), who play a
pivotal role in extending access to care for in low-and-middle-income-countries. This supplement aims to adapt
BECOME into a dyadic intervention for both people with dementia or mild cognitive impairment and their
caregivers, tailored to their unique needs. The adaptation will be guided by feedback from an advisory board
consisting of people with dementia or mild cognitive disorder, caregivers, experts on dementia care, NCDs,
and CMDs, and community leader working to bring awareness about dementia, Alzheimer’s disease and mild
cognitive disorders in Nepal and advocate for needed services for the community. Using the ADAPT-ITT
framework, we will adapt the intervention content and delivery into a dyadic approach to match the cognitive
capacities of people with dementia or mild cognitive impairment and integrate caregiver preferences, capacities
and needs. We will pilot the intervention with 5 dyads of people with dementia or mild cognitive impairment and
their caregivers. Finally, using mixed methods, we will evaluate improvements in symptoms of NCDs and
CMDs, and assess facilitator and barriers based on feedback from the dyads and the CHWs with goals to
further refine the intervention to be tested under a f...

## Key facts

- **NIH application ID:** 10952237
- **Project number:** 3R01MH133231-02S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Bibhav Acharya
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $290,502
- **Award type:** 3
- **Project period:** 2023-07-01 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10952237

## Citation

> US National Institutes of Health, RePORTER application 10952237, Adapting evidence-based behavioral interventions to address non-communicable diseases and common mental health conditions among people with dementia or mild cognitive impairment (3R01MH133231-02S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10952237. Licensed CC0.

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