Evidence map›Paper›PMID 39425064›Full record

ArticleBMC public health2024

Evaluation of the information quality related to osteoporosis on TikTok.

Aoying Li, Qiqiang Xing, Yu Zhang, Wanda Zhan, Sibo Zhu, Linbing Lou, Chuan Jiang, Zhenzhen Guo, Hao Sun

Abstract read
PubMed Full text at PMC Publisher
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 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

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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.

Aoying Li *Orthopedics Department, The Yangzhou School of Clinical Medicine of Dalian Medical University, Yangzhou City, China.
Qiqiang Xing *Orthopedics Department, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou City, China.
Yu ZhangOrthopedics Department, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou City, China.
Wanda ZhanOrthopedics Department, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou City, China.
Sibo ZhuOrthopedics Department, The Yangzhou School of Clinical Medicine of Dalian Medical University, Yangzhou City, China.
Linbing LouOrthopedics Department, The Yangzhou School of Clinical Medicine of Dalian Medical University, Yangzhou City, China.
Chuan JiangOrthopedics Department, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou City, China.
Zhenzhen GuoHunan University of Humanities, Science and Technology, Loudi City, China. 1656253291@qq.com.
Hao SunOrthopedics Department, Northern Jiangsu People's Hospital of Jiangsu Province, Yangzhou City, China. s1361191533@163.com.

Funding

Hunan Provincal Natural Science Foundation of China 2023JJ50467Youth Foundation of Hunan Provincial Education Department 21B0786
6 · The paper itself

Abstract

backgroundOsteoporosis is currently considered the most common bone disease in the world and is characterized by low bone mass, deterioration of the bone tissue microstructure, and decreased bone strength. With the increasing popularity of smartphones and short videos, many patients search for various types of health information through social media, such as short videos. As one of China's short video giants, TikTok has played a significant role in spreading health information. We found that there are many videos about osteoporosis on TikTok; however, the quality of these short videos has not yet been evaluated.

objectiveThe purpose of this study was to evaluate the information quality of osteoporosis videos on the domestic TikTok platform.

methodsWe retrieved and screened 100 videos about osteoporosis from TikTok, extracted the basic information, encoded the video content, and recorded the source of each video. Two independent raters evaluated the information quality of each video via the DISCERN rating scale.

resultThe videos were divided into three groups according to their source: medical personnel, science communicators, and news media, with medical personnel posting the most videos. The content of the video is divided into 7 groups, namely, disease prevention, disease diagnosis, disease symptoms, disease overview, life-style, drug knowledge, and drug treatment, with the most videos related to disease overview. The average DISCERN score of the videos is 37.69 (SD = 6.78), mainly within the 'poor' (54/100, 54%) and 'appropriate' (43/100, 43%) rating ranges, with overall quality being low. Further analysis revealed a positive correlation between the number of shares, comments, likes, and favorites, and a positive correlation between the DISCERN score and the number of shares and favorites.

conclusionThe overall quality of videos concerning osteoporosis on TikTok is lower, but the quality of videos varies significantly across different sources. We should be selective and cautious when watching videos about osteoporosis on TikTok.

Indexed as

OsteoporosisSocial MediaVideo RecordingChinaConsumer Health InformationHumansInformation DisseminationInformation qualityInternetOsteoporosisSocial mediaTikTok

Identifiers

PMID39425064
PMCPMC11490150

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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.