Evidence map›Paper›PMID 42776959›Full record

ArticlePloS one2026

Prevalence and predictors of artificial intelligence chatbots use for caregiving: Evidence from the Understanding America Study.

Joan K Monin, Marco Angrisani, Tate Dunbar, Angeliki Karachaliou, Kira Birditt

Abstract read
In one paragraph

Article in PloS one, 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

5 authors.

Joan K MoninSocial and Behavioral Sciences, Yale School of Public Health, New Haven, Connecticut, United States of America.ORCID https://orcid.org/0000-0002-1729-0230
Marco AngrisaniCenter for Economic and Social Research, University of Southern California, Los Angeles, CaliforniaUnited States of America.ORCID https://orcid.org/0000-0002-6084-6388
Tate DunbarSocial and Behavioral Sciences, Yale School of Public Health, New Haven, Connecticut, United States of America.
Angeliki KarachaliouSocial and Behavioral Sciences, Yale School of Public Health, New Haven, Connecticut, United States of America.
Kira BirdittSurvey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Connecticut, United States of America.

Funding

UAS-CLEAR: A new nationally representative longitudinal study of caregiving experiences and well-being across the lifecourseR01AG083097 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Marco Angrisani, KIRA S BIRDITT · 2023 to 2026
$3.8M
NIA NIH HHS R01 AG083097
6 · The paper itself

Abstract

Support for the growing population of family caregivers in the United States remains a challenging public health issue. With a limited workforce to support families coping with chronic and degenerative conditions, caregivers are turning to individualized approaches to gain information and emotional support. One approach that may be gaining popularity in caregiving support is the use of Artificial Intelligence (AI) chatbots. This study examined caregivers' awareness and general use of AI chatbots, their use of AI chatbots for caregiving purposes, and variations in these outcomes by individual characteristics (e.g., the Big 5 personality traits, internet literacy, cognitive ability) and caregiving contextual factors (e.g., caregiving relationship, caregiving stress, the health condition of the care recipient). To this end, we used one of the waves of the Understanding America Study-Caregiving Lifecourse Experiences Assessed in Real-time (UAS-CLEAR), a longitudinal study of caregivers in the U.S. The sample consisted of 1,300 caregivers, with data collected between August and October 2025. We employed a cross-sectional, mixed-methods design, combining quantitative analysis of structured survey items with thematic coding of open-ended responses. Of the 487 respondents who reported being AI chatbot users, 25% used the technology for caregiving. Those who were more likely to use AI for caregiving were higher in openness, lower in internet literacy, lower in cognitive ability, helped with more caregiving tasks, and were more uncertain in their caregiving role. Over half (58%) used AI chatbots for information about caregiving, 34% for emotional support related to caregiving, and 37% reported other uses. Open-ended responses about individuals' experiences using AI chatbots for caregiving also revealed that the most common reason for use was information seeking. In addition, caregivers found the chatbot support to be useful, while acknowledging some limitations of their use. Future research should examine perceptions of AI chatbots among non-users, the mechanisms linking individual traits to caregiving-related chatbot use, and the longitudinal effects of such use on caregiver well-being.

Indexed as

Artificial IntelligenceCaregiversAdultAgedCross-Sectional StudiesFemaleHumansInternetLongitudinal StudiesMaleMiddle AgedPrevalenceSurveys and QuestionnairesUnited States

Identifiers

PMID42776959
PMCPMC13600484

What OpenQuestion holds

Textmetadata
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.