Evidence map›Paper›PMID 42052028›Full record

ArticleFrontiers in public health2026

Functional experiences of a LINE-based chatbot and their associations with system use experience among older adults: a cross-sectional study.

Kuo-Mou Chung, Liang-Hsi Kung, Yu-Hua Yan

Abstract read
In one paragraph

Article in Frontiers in public health, 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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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

3 authors.

Kuo-Mou ChungTainan Municipal Hospital (Managed by Show Chwan Medical Care Corporation), Tainan, Taiwan.
Liang-Hsi KungTainan Municipal Hospital (Managed by Show Chwan Medical Care Corporation), Tainan, Taiwan.
Yu-Hua YanTainan Municipal Hospital (Managed by Show Chwan Medical Care Corporation), Tainan, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Population aging has heightened concerns regarding social participation and active aging among older adults. Chatbot-based interventions delivered through familiar messaging platforms have increasingly been adopted to support engagement with community activity programs; however, prior research has often treated chatbots as homogeneous interventions, with limited attention to how specific functional components shape user experiences among older adults. Objective: This study aimed to examine older adults' functional experiences with a LINE-based chatbot designed to support engagement with community activity programs and to investigate how different chatbot functions are associated with overall system use experience. Methods: A cross-sectional survey was conducted among 299 older adults who used a LINE-based chatbot implemented in community-based programs. Functional experiences were assessed across three chatbot modules: information inquiry, photo-based check-in interaction, and task achievement. Composite mean scores were calculated for each functional module and for the overall system use experience scale. Descriptive statistics, Pearson correlation analyses, and multiple linear regression analyses were performed using IBM SPSS Statistics. Results: Participants reported moderate to moderately high levels of functional experience across all chatbot modules. Pearson correlation analyses showed significant positive associations among all functional experience variables. Multiple linear regression analysis indicated that the overall model explained a large proportion of variance in overall system use experience ( Conclusion: The findings suggest that different chatbot functions contribute unequally to older adults' system use experience. Task-oriented and goal-focused functions may play a central role in shaping engagement with chatbot systems, whereas interactive features such as photo-based check-ins may serve a supplementary role. These results underscore the importance of function-specific design when developing chatbot-based interventions intended to support engagement with digital systems designed for community activity programs among older adults.

Indexed as

Social ParticipationAgedAged, 80 and overCross-Sectional StudiesFemaleHumansMaleSurveys and Questionnaireschatbotscommunity programsdigital engagementlineolder adultssystem use experience

Identifiers

PMID42052028
PMCPMC13110929

What OpenQuestion holds

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LicenceCC BY
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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.