Evidence map›Paper›PMID 39944856›Full record

ArticlemHealth2025

Association between mobile phone, self-efficacy and dependency among elderly people: a community-based study.

Qi-Qian Yao, Yin Chen, Xi-Wen Ding, Ayizuhere Aierken, Dong-Bin Hu, Ying Li

Abstract read
In one paragraph

Article in mHealth, 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
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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

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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

6 authors.

Qi-Qian YaoThe Sixth Affiliated Hospital, Harbin Medical University, Harbin, China.
Yin ChenDepartment of Social Medicine, School of Public Health, Zhejiang University, Hangzhou, China.
Xi-Wen DingDepartment of Social Medicine, School of Public Health, Zhejiang University, Hangzhou, China.
Ayizuhere AierkenDepartment of Social Medicine, School of Public Health, Zhejiang University, Hangzhou, China.
Dong-Bin HuThe Second Affiliated Hospital, Harbin Medical University, Harbin, China.
Ying LiDepartment of Social Medicine, School of Public Health, Zhejiang University, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The prevalence of dependency is high, and it is an urgent problem requiring immediate solutions for elderly people. This study aimed to explore the association between mobile phone use, self-efficacy and dependency among elderly people. Methods: This community-based study was conducted in 33 locations in China. A total of 2,195 participants aged ≥60 years were selected using a complex multistage sampling design. All data were collected using questionnaires by face-to-face interviews. Dependency was measured using the standardized Minnesota Multiphasic Personality Inventory-II. Self-efficacy was assessed using the Chinese version of the General Self-Efficacy Scale. Cumulative logistic regression models were used to evaluate the association between dependency and the use of mobile phones. Analysis of covariance (ANCOVA) was conducted to evaluate the association between the self-efficacy level and the frequency of mobile phone use. Results: More than 90% of elderly people reported that they used a mobile phone. The high frequency of mobile phone use was significantly associated with low level of dependency and high level of self-efficacy. The frequency of mobile phone use was negatively associated with the times of received community health services. Conclusions: Individuals who use mobile phones have a low level of dependency and a high level of self-efficacy. These findings suggest that mobile phone is an important mental health resource for improving dependency and increasing self-efficacy among elderly people.

Indexed as

community health servicesdependencyelderly peopleMobile phone useself-efficacy

Identifiers

PMID39944856
PMCPMC11811646

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Registered trials

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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.