Evidence map›Paper›PMID 40080043›Full record

ArticleJournal of medical Internet research2025

Caregiving Artificial Intelligence Chatbot for Older Adults and Their Preferences, Well-Being, and Social Connectivity: Mixed-Method Study.

Brooke H Wolfe, Yoo Jung Oh, Hyesun Choung, Xiaoran Cui, Joshua Weinzapfel, R Amanda Cooper, Hae-Na Lee, Rebecca Lehto

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

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

  1. Pooled it
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  13. What patients want from healthcare chatbots: insights from a mixed-methods study.Journal of the American Medical Informatics Association : JAMIA · 2025
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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.

Brooke H WolfeDepartment of Communication, Michigan State University, East Lansing, MI, United States.ORCID https://orcid.org/0000-0002-8908-855X
Yoo Jung OhDepartment of Communication, Michigan State University, East Lansing, MI, United States.ORCID https://orcid.org/0000-0002-7829-8535
Hyesun ChoungBrian Lamb School of Communication, Purdue University, West Lafayette, IN, United States.ORCID https://orcid.org/0000-0001-9464-0399
Xiaoran CuiDepartment of Communication, Michigan State University, East Lansing, MI, United States.ORCID https://orcid.org/0009-0009-4009-4324
Joshua WeinzapfelDepartment of Communication, Michigan State University, East Lansing, MI, United States.ORCID https://orcid.org/0009-0000-3458-2254
R Amanda CooperDepartment of Communication, University of Connecticut, Storrs, CT, United States.ORCID https://orcid.org/0000-0001-8771-6842
Hae-Na LeeCollege of Engineering, Michigan State University, East Lansing, MI, United States.ORCID https://orcid.org/0000-0002-2183-1722
Rebecca LehtoCollege of Nursing, Michigan State University, East Lansing, MI, United States.ORCID https://orcid.org/0000-0001-5091-8408

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe increasing number of older adults who are living alone poses challenges for maintaining their well-being, as they often need support with daily tasks, health care services, and social connections. However, advancements in artificial intelligence (AI) technologies have revolutionized health care and caregiving through their capacity to monitor health, provide medication and appointment reminders, and provide companionship to older adults. Nevertheless, the adaptability of these technologies for older adults is stymied by usability issues. This study explores how older adults use and adapt to AI technologies, highlighting both the persistent barriers and opportunities for potential enhancements.

objectiveThis study aimed to provide deeper insights into older adults' engagement with technology and AI. The technologies currently used, potential technologies desired for daily life integration, personal technology concerns faced, and overall attitudes toward technology and AI are explored.

methodsUsing mixed methods, participants (N=28) completed both a semistructured interview and surveys consisting of health and well-being measures. Participants then participated in a research team-facilitated interaction with an AI chatbot, Amazon Alexa. Interview transcripts were analyzed using thematic analysis, and surveys were evaluated using descriptive statistics.

resultsParticipants' average age was 71 years (ranged from 65 years to 84 years). Most participants were familiar with technology use, especially using smartphones (26/28, 93%) and desktops and laptops (21/28, 75%). Participants rated appointment reminders (25/28, 89%), emergency assistance (22/28, 79%), and health monitoring (21/28, 75%). Participants rated appointment reminders (25/28, 89.3%), emergency assistance (22/28, 78.6%), and health monitoring (21/28, 75%) as the most desirable features of AI chatbots for adoption. Digital devices were commonly used for entertainment, health management, professional productivity, and social connectivity. Participants were most interested in integrating technology into their personal lives for scheduling reminders, chore assistance, and providing care to others. Challenges in using new technology included a commitment to learning new technologies, concerns about lack of privacy, and worries about future technology dependence. Overall, older adults' attitudes coalesced into 3 orientations, which we label as technology adapters, technologically wary, and technology resisters. These results illustrate that not all older adults were resistant to technology and AI. Instead, older adults are aligned with categories on a spectrum between willing, hesitant but willing, and unwilling to use technology and AI. Researchers can use these findings by asking older adults about their orientation toward technology to facilitate the integration of new technologies with each person's comfortability and preferences.

conclusionsTo ensure that AI technologies effectively support older adults, it is essential to foster an ongoing dialogue among developers, older adults, families, and their caregivers, focusing on inclusive designs to meet older adults' needs.

Indexed as

Artificial IntelligenceCaregiversAgedAged, 80 and overFemaleGenerative Artificial IntelligenceHumansMaleSurveys and QuestionnairesAI chatbotsartificial intelligencemobile phoneolder adultssocial connectednesstechnology usewell-being

Identifiers

PMID40080043
PMCPMC11950695

What OpenQuestion 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 OpenQuestion graph.