Evidence map›Paper›PMID 41678797›Full record

ArticleJMIR research protocols2026

Evaluating an AI-Enabled Mobile Mental Health Monitoring Tool Among Family Caregivers of Adults Living With Cancer: Single-Arm Feasibility and Acceptability Trial Protocol.

Chiara Acquati, Michael Aratow, Tahmida Nazreen, Arunima Bhattacharjee, Isabella K Marra, Ashley S Alexander

Abstract readClinical Trial Protocol
In one paragraph

Article in JMIR research protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Chiara AcquatiGraduate College of Social Work, University of Houston, Houston, TX, United States.ORCID 0000-0002-6232-5834
Michael AratowEllipsis Health, San Francisco, CA, United States.ORCID 0000-0003-4749-6413
Tahmida NazreenEllipsis Health, San Francisco, CA, United States.ORCID 0009-0008-1292-1225
Arunima BhattacharjeeGraduate College of Social Work, University of Houston, Houston, TX, United States.ORCID 0009-0008-3101-6730
Isabella K MarraFertitta Family College of Medicine, University of Houston, Houston, TX, United States.ORCID 0009-0008-2725-9932
Ashley S AlexanderKelsey Research Foundation, Houston, TX, United States.ORCID 0009-0009-1889-1060

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPsychological distress, particularly symptoms of depression and anxiety (D&A), is highly prevalent among family caregivers of individuals living with cancer, who often assume central roles in care coordination, treatment adherence, symptom monitoring, and emotional support. Rates of distress among caregivers frequently equal or exceed those observed in patients themselves. Despite increased attention to caregivers' mental health needs, routine distress screening remains limited in oncology care settings. Advances in mobile health technology and artificial intelligence (AI) offer opportunities to address these needs by providing accessible and user-driven tools. The Ellipsis Caregiver Assessment Enhancement (eCARE; Ellipsis Health, Inc) is a speech-based, AI-enabled mobile app designed to screen and monitor symptoms of depression and anxiety. By collecting brief voice recordings and in-app survey data, eCARE offers a scalable approach for integrating caregiver distress monitoring into cancer care.

objectiveThis single-arm trial will evaluate the feasibility and acceptability of the eCARE app among family members who are the primary caregivers of patients diagnosed with cancer within the past 5 years. Specifically, the study aims to (1) determine feasibility based on platform completion rates, (2) assess acceptability using validated measures, and (3) identify barriers and facilitators influencing the uptake and sustained use of eCARE.

methodsIn Phase 1, a total of 60 United States-based family caregivers will be recruited from community health clinics, cancer and caregiving advocacy groups, and online postings. Screened and enrolled caregivers will complete 6 eCARE sessions over an 8-week period. Pre- and posttest surveys assess depression, anxiety, caregiving burden, and relational processes. Feasibility will be evaluated based on the proportion of participants who complete at least 66% of weekly assessments, and acceptability will be assessed using the acceptability of intervention measure (AIM). In Phase 2, a total of 20 caregivers will be invited to participate in semi-structured online interviews to explore user experience, including perceived benefits, barriers to use, and preferences for future implementation. Qualitative data will be analyzed thematically to inform tool refinement.

resultsThe study has received Institutional Review Board approval from the University of Houston. Participant recruitment and enrollment began in June 2024, with data collection expected to conclude by August 2025. Data analysis will begin in December 2025, with preliminary results anticipated by May 2026.

conclusionsThis study will generate preliminary evidence on the feasibility, acceptability, and utility of a speech-based, AI-enabled smartphone tool for monitoring D&A symptoms among family cancer caregivers. Findings will inform the design of a larger, fully powered trial and guide future implementation of remote psychological distress monitoring strategies in oncology care. By offering a low-burden, caregiver-centered approach, eCARE has the potential to expand access to psychosocial support and facilitate timely identification of needs and coordination of services across cancer care settings. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/83276.

Indexed as

Artificial IntelligenceCaregiversMobile ApplicationsNeoplasmsAdultAnxietyDepressionFeasibility StudiesFemaleHumansMental HealthPsychological DistressStress, PsychologicalTelemedicineacoustic analysisanxietyartificial intelligence (AI)cancercaregivingdepressionfeasibility & acceptability studymobile health (mHealth)psychological distresssemantic analysisspeech-based monitoring

Identifiers

PMID41678797
PMCPMC12946784

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.