Evidence map›Paper›PMID 41473731›Full record

ArticleFrontiers in public health2025

The adoption of assistive smart home technology and health-related quality of life among older adults: the moderating role of depressive symptoms.

Yuwei Qiao, Longtao Wang, Yanli Liu, Weichao Chen

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. 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

4 authors.

Yuwei QiaoSchool of Journalism and Communication, Hunan Normal University, Changsha, China.
Longtao WangSchool of Journalism and Communication, Hunan Normal University, Changsha, China.
Yanli LiuSchool of Journalism and Communication, Hunan Normal University, Changsha, China.
Weichao ChenSchool of Journalism and Communication, Hunan Normal University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: This study aims to assess the impact of assistive smart home technology use on the health-related quality of life (HRQoL) and to examine the moderating effect of depressive symptoms. Methods: Using data from the 2021 Psychology and Behavior Investigation of Chinese Residents (PBICR), a total of 1,147 participants (aged ≥ 60 years) were included after excluding samples with missing core values. An Ordinary Least Squares (OLS) regression model was employed to examine the association between assistive smart home technology use and HRQoL among older adults. Results: Assistive smart home technology use was significantly and positively associated with HRQoL among older adults. Moderation analysis further revealed that depressive symptoms strengthened this association, with individuals experiencing higher depressive symptoms showing a greater increase in HRQoL when using assistive smart home technology. Conclusion: The findings suggest that assistive smart home technology may contribute to improving HRQoL among older adults, particularly for those with higher depressive symptoms. Promoting age-friendly smart home devices, community-based training, and integration with healthcare services may enhance the benefits of technology adoption and support aging in place in China.

Indexed as

DepressionQuality of LifeSelf-Help DevicesAgedAged, 80 and overChinaFemaleHumansIndependent LivingMaleMiddle AgedSurveys and Questionnairesaging in placeassistive smart home technologydepressive symptomshealth-related quality of life (HRQoL)older adults

Identifiers

PMID41473731
PMCPMC12746494

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.