Evidence map›Paper›PMID 37845659›Full record

ArticleBMC medical informatics and decision making2023

The willingness to continue using wearable devices among the elderly: SEM and FsQCA analysis.

Ying Wang, Liyan Lu, Rui Zhang, Yiming Ma, Shuping Zhao, Changyong Liang

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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  3. How Digital Health Technology Is Extending Independent Living.Journal of medical Internet research · 2026
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  8. Video-estimated peak jump power using deep learning is associated with sarcopenia and low physical performance in adults.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025
    Article
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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.

Ying WangAnhui University of Chinese Medicine, Hefei, 230012, China.
Liyan LuHefei University of Technology, Hefei, 230009, China. ly18255173836@163.com.
Rui ZhangHefei University of Technology, Hefei, 230009, China.
Yiming MaHefei University of Technology, Hefei, 230009, China.
Shuping ZhaoHefei University of Technology, Hefei, 230009, China.
Changyong LiangHefei University of Technology, Hefei, 230009, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith population aging and the scarcity of resources for elderly individuals, wearable devices pose opportunities and challenges for elderly care institutions. However, few studies have examined the effects of technical characteristics, personal characteristics, and health promotion on the willingness of elderly individuals to continue using wearable devices.

objectiveThis study explored the effects of technical characteristics and personal characteristics on the willingness of elderly individuals to continue using wearable devices through health promotion, drawing on the technology acceptance model and the value attitude behaviour model.

methodsWe obtained 265 valid samples through questionnaire surveys and used structural equation modelling (SEM) and fuzzy set qualitative comparative analysis (FsQCA) to clarify the complex causal patterns of elderly people's willingness to continue using wearable devices.

resultsThe SEM results showed that perceived usefulness, perceived reliability, self-perceived ageing, and health promotion affected willingness to continue using wearable devices. However, perceived ease of use had no effect. FsQCA showed that elderly individuals are highly willing to continue using wearable devices, yielding five solutions. Perceived ageing was essential in four of these solutions. The impact of perceived ease of use on continued use intention was dynamic and complex.

conclusionsThis study used two methods to provide insight into the willingness of elderly individuals to continue using wearable devices. In addition, this study discussed associated implications, limitations, and future research directions.

Indexed as

Wearable Electronic DevicesAgedAttitudeHumansIntentionLatent Class AnalysisReproducibility of ResultsElderlyFsQCAHealth promotionPersonal characteristicTechnical characteristicWearble devices

Identifiers

PMID37845659
PMCPMC10577990

What OpenQuestion holds

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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.