Evidence map›Paper›PMID 42398038›Full record

ArticleJMIR formative research2026

Exploring Informal Caregivers' Perception of the Olera Digital Caregiving Assistance Platform for Dementia Care: Mixed Methods Evaluation Study.

Minh-Nguyet Hoang, Laura Kim, Louis Fisher, Logan DuBose, Marcia G Ory, Shinduk Lee, Tokunbo Falohun, Qiping Fan

Abstract read
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Minh-Nguyet HoangNaresh K. Vashisht College of Medicine, Texas A&M University, 8447 John Sharp Pkwy, Bryan, TX, 77807, United States, 1 214-985-7035.ORCID 0009-0006-6513-0852
Laura KimNaresh K. Vashisht College of Medicine, Texas A&M University, 8447 John Sharp Pkwy, Bryan, TX, 77807, United States, 1 214-985-7035.ORCID 0009-0001-6968-7360
Louis FisherDepartment of Public Health, College of Behavioral, Social, and Health Sciences, Clemson University, Clemson, SC, United States.ORCID 0009-0000-8099-0371
Logan DuBoseDepartment of Environmental and Occupational Health, School of Public Health, Texas A&M University, College Station, TX, United States.ORCID 0009-0005-2740-9062
Marcia G OryDepartment of Environmental and Occupational Health, School of Public Health, Texas A&M University, College Station, TX, United States.ORCID 0000-0001-8036-2383
Shinduk LeeDivision of Health Systems and Community-Based Care, College of Nursing, University of Utah, Salt Lake City, UT, United States.ORCID 0000-0003-1336-819X
Tokunbo FalohunDepartment of Biomedical Engineering, College of Engineering, Texas A&M University, College Station, TX, United States.ORCID 0000-0003-2723-2760
Qiping FanDepartment of Public Health, College of Behavioral, Social, and Health Sciences, Clemson University, Clemson, SC, United States.ORCID 0000-0001-9168-7845

Funding

Olera - Online Platform to Increase Access to Personalized Educational and Professional Assistance for AD/ADRD CaregiversR44AG074116 · NIA · OLERA INC. · PI Tokunbo Falohun · 2021 to 2026
$5.3M
NIA NIH HHS R44 AG074116
6 · The paper itself

Abstract

Background: Informal caregivers of people living with dementia often experience high rates of caregiver burnout while providing care. Although there are many websites and mobile apps available to help caregivers, many do not use digital tools. The Olera platform was developed to be an easily adoptable web-based support tool, connecting caregivers with long-term services and supports, financial assistance, and educational resources. The platform was developed based on the Build-Measure-Learn framework with input from caregiver needs assessments and usability studies. Objective: This study aims to evaluate the quantitative and qualitative feedback of informal caregivers of people living with dementia on the second iteration of the Olera platform. The primary objective was to assess caregivers' acceptance of this caregiving platform. The secondary objective was to use qualitative methods to explore (1) the study cohort's challenges in daily caregiving to determine and compare them with prior literature, (2) their experience when using the Olera platform, and (3) their attitudes toward integrating artificial intelligence in caregiver services for future studies and platform development. Methods: Caregivers were recruited through various sources and screened for eligibility through an initial survey. Participants used the platform for 4 weeks and completed a survey with an adapted Technology Acceptance Survey (TAS) and qualitative open-ended questions at the end of the testing period. TAS responses were summarized with descriptive statistics, while ANOVAs, t tests, and linear regressions were used to compare the differences in the overall TAS scores by caregiver characteristics. Qualitative feedback data on the platform's usefulness were analyzed via a thematic analysis framework approach. Results: A total of 65 caregivers in the United States completed the study, with a mean age of 59.9 (SD 9.8) years. The majority were female (61/65, 95.3%), non-Hispanic or Latino White (45/65, 69.2%), and the adult child of their care recipient (42/65, 64.6%). Evaluation of the Olera platform showed a high acceptance rate, with each TAS item scoring above 5.0 and an overall TAS score of 5.83 (SD 0.85) out of 7. Higher platform use frequency was associated with higher TAS ratings in technology acceptance (F3,61=7.88, P<.001). Thematic analyses elicited the caregiving challenges, evaluation of the Olera platform, and feedback on artificial intelligence-assisted support. Conclusions: The Olera platform is an example of a beneficial web-based tool, though key features were requested to be included in the next iteration. Additionally, data supported prior findings regarding informal caregiver challenges and the insufficiency of conventional support mechanisms, indicating a need for more innovative digital solutions. Future research and development efforts using the Build-Measure-Learn approach are necessary to further iterate the platform's key features, enhance the tool, involve more informal caregivers in its improvements, and serve as a model for customizable, person-centered online care support.

Indexed as

CaregiversDementiaPerceptionAdultAgedAged, 80 and overDigital HealthDigital MediaFemaleHumansInternetMaleMiddle AgedMobile ApplicationsQualitative ResearchSurveys and QuestionnairesAlzheimer diseaseartificial intelligencecaregiving challengesdementiaease of usefamily caregivingTechnology Acceptance Modelusability

Identifiers

PMID42398038
PMCPMC13331331

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.