Evidence map›Paper›PMID 36563026›Full record

ArticleJMIR public health and surveillance2022

Factors Influencing Adoption and Use of Telemedicine Services in Rural Areas of China: Mixed Methods Study.

Yumeng Du, Qiru Zhou, Weibin Cheng, Zhang Zhang, Samantha Hoelzer, Yizhi Liang, Hao Xue, Xiaochen Ma, Sean Sylvia, Junzhang Tian and 1 more

Open access · goldAbstract read
In one paragraph

Article in JMIR public health and surveillance, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 3 pooled it
9.4field-weighted citation impact, top 1% of its field
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

32 citing papers in PubMed, 3 syntheses or guidelines pooled it, 55 citations in OpenAlex.

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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

11 authors at 5 institutions in 3 countries.

Yumeng Du *Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China.ORCID 0000-0002-6430-9440
Qiru Zhou *Internet Hospital, Guangdong Second Provincial General Hospital, Guangzhou, China.ORCID 0000-0001-5915-271X
Weibin Cheng *Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China.ORCID 0000-0002-9845-6676
Zhang ZhangGillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.ORCID 0000-0001-9344-6578
Samantha HoelzerGillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.ORCID 0000-0002-5945-4109
Yizhi LiangUniversity of North Carolina at Chapel Hill Project-China, Guangzhou, China.ORCID 0000-0002-7475-496X
Hao XueStanford Center for China's Economy and Institutions, Stanford University, Stanford, CA, United States.ORCID 0000-0002-9683-5541
Xiaochen MaChina Center for Health Development Studies, Peking University, Beijing, China.ORCID 0000-0002-8251-3373
Sean SylviaGillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.ORCID 0000-0002-9508-247X
Junzhang TianInstitute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China.ORCID 0000-0002-4832-3648
Weiming TangInstitute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China.ORCID 0000-0002-9026-707X
University of North Carolina at Chapel Hill · USCity University of Hong Kong · HKGuangdong Provincial People's Hospital · CNPeking University · CNStanford University · US

Funding

Research Services CoreP2CHD050924 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KAREN B GUZZO · 2015 to 2026
$9.6M
6 · The paper itself

Abstract

backgroundThe shortage of medical resources in rural China reflects the health inequity in resource-limited settings, whereas telemedicine could provide opportunities to fill this gap. However, evidence of patient acceptance of telemedicine services from low- and middle-income countries is still lacking.

objectiveWe aimed to understand the profile of patient end-user telemedicine use and identify factors influencing telemedicine service use in rural China.

methodsOur study followed a mixed methods approach, with a quantitative cross-sectional survey followed by in-depth semistructured interviews to describe telemedicine use and its associated factors among rural residents in Guangdong Province, China. In the quantitative analysis, explanatory variables included environmental and context factors, household-level factors, individual sociodemographic factors, access to digital health care, and health needs and demand factors. We conducted univariate and multivariate analyses using Firth logistic regression to examine the correlations of telemedicine uptake. A thematic approach was used, guided by the Social Cognitive Theory for the qualitative analysis.

resultsA total of 2101 households were recruited for the quantitative survey. With a mean age of 61.4 (SD 14.41) years, >70% (1364/2101, 72.94%) of the household respondents were male. Less than 1% (14/2101, 0.67%) of the respondents reported experience of using telemedicine. The quantitative results supported that villagers living with family members who had a fever in the past 2 weeks (adjusted odds ratio 6.96, 95% CI 2.20-21.98; P=.001) or having smartphones or computers (adjusted odds ratio 3.71, 95% CI 0.64-21.32; P=.14) had marginally higher telemedicine uptake, whereas the qualitative results endorse these findings. The results of qualitative interviews (n=27) also supplemented the potential barriers to telemedicine use from the lack of knowledge, trust, demand, low self-efficacy, and sufficient physical and social support.

conclusionsThis study found extremely low use of telemedicine in rural China and identified potential factors affecting telemedicine uptake. The main barriers to telemedicine adoption among rural residents were found, including lack of knowledge, trust, demand as well as low self-efficacy, and insufficient physical and social support. Our study also suggests strategies to facilitate telemedicine engagement in low-resource settings: improving digital literacy and self-efficacy, building trust, and strengthening telemedicine infrastructure support.

Indexed as

TelemedicineChinaCross-Sectional StudiesDelivery of Health CareFemaleHumansMaleMiddle AgedSmartphoneChinamixed methodsmobile phonerural residentstelehealthtelemedicine

Identifiers

PMID36563026
PMCPMC9823570
OpenAlexW4312178001

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.