Evidence mapPaperPMID 39778201Full record

ArticleJMIR aging2025

Designing a Multimodal and Culturally Relevant Alzheimer Disease and Related Dementia Generative Artificial Intelligence Tool for Black American Informal Caregivers: Cognitive Walk-Through Usability Study.

Cristina Bosco, Ege Otenen, John Osorio Torres, Vivian Nguyen, Darshil Chheda, Xinran Peng, Nenette M Jessup, Anna K Himes, Bianca Cureton, Yvonne Lu and 4 more

Abstract read
In one paragraph

Article in JMIR aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

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

14 authors.

Cristina BoscoLuddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States.ORCID https://orcid.org/0000-0002-1674-1861
Ege OtenenLuddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States.ORCID https://orcid.org/0000-0001-9887-5603
John Osorio TorresLuddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States.ORCID https://orcid.org/0009-0003-6778-8453
Vivian NguyenLuddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States.ORCID https://orcid.org/0009-0003-4495-9045
Darshil ChhedaLuddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States.ORCID https://orcid.org/0009-0007-7338-1819
Xinran PengLuddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States.ORCID https://orcid.org/0009-0007-3153-9008
Nenette M JessupSchool of Nursing, Indiana University, Indianapolis, IN, United States.ORCID https://orcid.org/0000-0001-6026-7899
Anna K HimesSchool of Nursing, Indiana University, Indianapolis, IN, United States.ORCID https://orcid.org/0009-0002-7768-1556
Bianca CuretonSchool of Nursing, Indiana University, Indianapolis, IN, United States.ORCID https://orcid.org/0009-0000-0858-0909
Yvonne LuSchool of Nursing, Indiana University, Indianapolis, IN, United States.ORCID https://orcid.org/0000-0002-1345-4541
Carl V HillAlzheimer's Association, Chicago, IL, United States.ORCID https://orcid.org/0000-0002-7917-1341
Hugh C HendrieSchool of Medicine, Indiana University, Indianapolis, IN, United States.ORCID https://orcid.org/0000-0002-7591-3176
Priscilla A BarnesSchool of Public Health, Indiana University, Bloomington, IN, United States.ORCID https://orcid.org/0000-0002-4010-2672
Patrick C ShihLuddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States.ORCID https://orcid.org/0000-0003-2460-0468

Funding

NIA NIH HHS R24 AG071471
6 · The paper itself

Abstract

backgroundMany members of Black American communities, faced with the high prevalence of Alzheimer disease and related dementias (ADRD) within their demographic, find themselves taking on the role of informal caregivers. Despite being the primary individuals responsible for the care of individuals with ADRD, these caregivers often lack sufficient knowledge about ADRD-related health literacy and feel ill-prepared for their caregiving responsibilities. Generative AI has become a new promising technological innovation in the health care domain, particularly for improving health literacy; however, some generative AI developments might lead to increased bias and potential harm toward Black American communities. Therefore, rigorous development of generative AI tools to support the Black American community is needed.

objectiveThe goal of this study is to test Lola, a multimodal mobile app, which, by relying on generative AI, facilitates access to ADRD-related health information by enabling speech and text as inputs and providing auditory, textual, and visual outputs.

methodsTo test our mobile app, we used the cognitive walk-through methodology, and we recruited 15 informal ADRD caregivers who were older than 50 years and part of the Black American community living within the region. We asked them to perform 3 tasks on the mobile app (ie, searching for an article on brain health, searching for local events, and finally, searching for opportunities to participate in scientific research in their area), then we recorded their opinions and impressions. The main aspects to be evaluated were the mobile app's usability, accessibility, cultural relevance, and adoption.

resultsOur findings highlight the users' need for a system that enables interaction with different modalities, the need for a system that can provide personalized and culturally and contextually relevant information, and the role of community and physical spaces in increasing the use of Lola.

conclusionsOur study shows that, when designing for Black American older adults, a multimodal interaction with the generative AI system can allow individuals to choose their own interaction way and style based upon their interaction preferences and external constraints. This flexibility of interaction modes can guarantee an inclusive and engaging generative AI experience.

Indexed as

Alzheimer DiseaseArtificial IntelligenceBlack or African AmericanCaregiversMobile ApplicationsAgedDementiaFemaleHealth LiteracyHumansMaleMiddle AgedAfrican AmericanagingAIAlzheimer'sartificial intelligenceblackcaregiverscognitionculturaldementiadigital healthgenerative AIgeriatricsinteractionmHealthmobile appmultimodalitysmartphoneusabilityuser opinion

Identifiers

PMID39778201
PMCPMC11754989

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.