Evidence map›Paper›PMID 38539176›Full record

ArticleBMC public health2024

Digital health literacy and associated factors among internet users from China: a cross-sectional study.

Bing-Yue Zhao, Long Huang, Xiao Cheng, Ting-Ting Chen, Si-Jia Li, Xiao-Juan Wang, Shui-Xiu Huang, Rong-Fang Hu, Hong Li

Open access · goldAbstract read
In one paragraph

Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 1 pooled it
44.2field-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

47 citing papers in PubMed, 1 synthesis or guideline pooled it, 52 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

9 authors at 2 institutions in 1 country.

Bing-Yue Zhao *School of Nursing, Fujian Medical University, 1 Xue Yuan Road, University Town, Fuzhou, 350122, China.
Long Huang *Department of Nursing, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Guangdong, Zhuhai, China.
Xiao ChengSchool of Nursing, Fujian Medical University, 1 Xue Yuan Road, University Town, Fuzhou, 350122, China.
Ting-Ting ChenSchool of Nursing, Fujian Medical University, 1 Xue Yuan Road, University Town, Fuzhou, 350122, China.
Si-Jia LiSchool of Nursing, Fujian Medical University, 1 Xue Yuan Road, University Town, Fuzhou, 350122, China.
Xiao-Juan WangSchool of Nursing, Fujian Medical University, 1 Xue Yuan Road, University Town, Fuzhou, 350122, China.
Shui-Xiu HuangSchool of Nursing, Fujian Medical University, 1 Xue Yuan Road, University Town, Fuzhou, 350122, China.
Rong-Fang HuSchool of Nursing, Fujian Medical University, 1 Xue Yuan Road, University Town, Fuzhou, 350122, China. hurongfang1234@sina.com.
Hong LiSchool of Nursing, Fujian Medical University, 1 Xue Yuan Road, University Town, Fuzhou, 350122, China. leehong99@126.com.
Fujian Medical University · CNJinan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs the internet develops and 5G technology becomes increasingly prominent, the internet has become a major source of health-related information. Increasingly, people use the internet to find health-related information, and digital health literacy is now a set of essential capabilities to improve their health in the digital era. However, little is known about the factors that influencing digital health literacy. This study aimed to assess digital health literacy scores and identify its influencing factors among internet users in China. Additionally, this study explored the participant's actual skills using an additional set of performance-based items from the Digital Health Literacy Instrument (DHLI).

methodsAn online cross-sectional study was conducted in August 2022. Participants aged ≥18 years were recruited to complete the survey. Data were collected using the Chinese revised version of the DHLI, the self-reported internet use questionnaire, and the sociodemographic questionnaire. We conducted multivariate linear regression analyses to explore the relationships among the sociodemographic variables, behavior of internet use, and the digital health literacy scores.

resultsIn total, 702 participants completed the survey. The mean DHLI score was 2.69 ± 0.61. Multivariate linear regression analyses showed that the age groups 35-49 (β = - 0.08, P = 0.033), 50-64 (β = - 0.161, P < 0.001), and ≥ 65 (β = - 0.138, P < 0.001) were negatively associated with DHL scores. However, education level, including bachelor's or associate degree (β = 0.255, P = 0.002) and master's degree and above (β = 0.256, P < 0.001), frequency of health-related Internet usage (β = 0.192, P < 0.001), the number of digital devices used (β = 0.129, P = 0.001), and OHISB (β = 0.103, P = 0.006) showed a positive relationship with DHL scores.

conclusionsThe study findings demonstrate that age, educational levels, number of technological devices used, and greater use of the web for health information were independently associated with DHL scores. Healthcare providers should consider providing training programs tailored to specific sociodemographic factors to improve the ability that find and use accurate information online to meet digital health services, which contributes to enhance their self-management and reduce health disparities.

Indexed as

Health LiteracyTelemedicineAdolescentAdultChinaCross-Sectional StudiesDigital HealthHumansInternetSurveys and QuestionnairesDigital health literacyeHealth literacyHealth-related informationInternet usePerformance-based

Identifiers

PMID38539176
PMCPMC10976739
OpenAlexW4393234307

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.