Evidence map›Paper›PMID 41566462›Full record

ArticleBMC public health2026

How smart companions reshape psychological well-being in later life: the role of artificial intelligence technology in mental health during senior years.

Bin Luan, Yongchuan Li

Abstract read
In one paragraph

Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Bin LuanSchool of Architecture and Landscape Architecture, ShandongUniversity of Art & Design, Jinan, China.
Yongchuan LiSchool of media & art design, Wenzhou Business College, Wenzhou, China. liyongchuan@ldy.edu.rs.

Funding

Research on Aged-friendly Interaction Design Strategies and Practical Paths of Smart Products under the Perspective of Active Aging RC202443
6 · The paper itself

Abstract

Against the dual backdrop of global population ageing and digital transformation, how to leverage smart eldercare technologies to bridge the “digital divide” and enhance older adults’ psychological well-being has become a pressing issue. Grounded in the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), this study innovatively develops a deep hybrid analytical framework that integrates Structural Equation Modeling (SEM) with Artificial Neural Networks (ANN). SEM is employed to validate the linear causal pathways among variables, while ANN is introduced to uncover nonlinear relationships and predictive weights in the data. The SEM results confirm a clear transmission pathway of “technology perception → demand satisfaction → enhancement of well-being,” in which “usage demand” plays a key mediating role. The nonlinear sensitivity analysis based on ANN further reveals that perceived usefulness and usage demand are the primary driving factors for predicting older adults’ well-being, indicating that their value orientation toward technology has shifted from “usable” to “truly useful.” The findings suggest that future product design should move away from mere functional stacking toward precise value alignment and zero–cognitive-load interaction, thereby providing scientific evidence for achieving high-quality, proactive healthy ageing.

Indexed as

Artificial IntelligenceMental HealthPsychological Well-BeingAgedAged, 80 and overDigital HealthFemaleHumansMaleNeural Networks, ComputerMental healthOlder adultsSmart eldercareSmart technologiesWell-being

Identifiers

PMID41566462
PMCPMC12908296

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.