Evidence map›Paper›PMID 32576159›Full record

ArticleBMC public health2020

Health information needs regarding diabetes mellitus in China: an internet-based analysis.

Tian-Hao Wang, Xiao-Feng Zhou, Yuan Ni, Zhi-Gang Pan

Abstract read
In one paragraph

Article in BMC public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Assessing patient information needs for new antidiabetic medications to inform shared decision-making: A best-worst scaling experiment in China.Health expectations : an international journal of public participation in health care and health policy · 2024
    Article
  4. 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

4 authors.

Tian-Hao WangDepartment of General Practice, Zhongshan Hospital of Fudan University, Shanghai, 200032, China.
Xiao-Feng ZhouPing An Healthcare and Technology Co., Ltd., Shanghai, 200001, China.
Yuan NiPing An Healthcare and Technology Co., Ltd., Shanghai, 200001, China.
Zhi-Gang PanDepartment of General Practice, Zhongshan Hospital of Fudan University, Shanghai, 200032, China. pan.zhigang@zs-hospital.sh.cn.ORCID http://orcid.org/0000-0001-6916-0203

Funding

Shanghai Municipal Population and Family Planning Commission 2015ZB0601
6 · The paper itself

Abstract

backgroundToday,. most people use the Internet to seek online health-related information from general public health-related websites and discussion groups. However, there are no Internet-based analyses of health information needs pertaining to diabetes in China until now. With the development of artificial intelligence,we can analyzed these online health-related information and provide references for health providers to improve their health service.

methodsWe have done a study of statistically analyzing the questions about diabetes collected from 39 health website, the number of which is 151,589. We have divided these questions into 9 categories using a convolutional neural network.

resultsThe diabetes problems of consumer are presented as follows, diagnosis: 34.95%, treatment: 25.17%, lifestyle: 21.09%, complication: 8.00%, maternity-related:5.00%, prognosis: 2.59%, health provider choosing: 1.40%, prevention: 1.23%, others: 0.58%, The elderly are more concerned about the treatment and complications of diabetes, while the young are more concerned about the maternity-related and prognosis of diabetes. The diabetes drugs most frequently mentioned by consumers are insulin, metformin and Xiaoke pills, The most concerned complication is caidiovascular disease and diabetic eye disease.

conclusionDiabetes health education should focus on how to prevent diabetes and the contents of health education should be different for differernt age groups;on diabetes treatment, the use of insulin and oral hypoglycemic drugs education should be strengthened.

Indexed as

Diabetes MellitusAdultAgedArtificial IntelligenceChinaConsumer Health InformationFemaleHumansInternetMaleMiddle AgedNeeds AssessmentNeural Networks, ComputerPregnancyYoung AdultConsumer health informationDiabetes mellitusHealth problemMachine learningQuestion classification

Identifiers

PMID32576159
PMCPMC7310401

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.