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6. Status of health communication of medical institutions using new media platforms in Shanghai

Author

JIANG Zongmin, LIU Huilin, JIN Wei, ZHOU Jingfeng, WANG Quqing, WANG Ting, WANG Jiwei, and WEI Xiaomin

Subjects
new media, medical institution, health communication, Medicine
Abstract

ObjectiveTo investigate the current situation of health communication carried out by medical institutions in Shanghai through new media platforms such as WeChat， Weibo， Toutiao， Douyin， Kuaishou， Bilibili and WeChat Videos， and to propose targeted measures.MethodsBased on the systematic collection of new media accounts of medical institutions in Shanghai， and through the combination of keyword screening and manual audit， health communication data of medical institutions on new media platforms were determined.ResultsData from 1 117 new media accounts of 162 medical institutions in Shanghai were collected， including 610 WeChat official accounts， 105 WeChat video accounts， 89 Weibo accounts， 18 Bilibili accounts， 198 Douyin accounts， 37 Toutiao accounts， and 60 Kuaishou accounts， totaling 111 853 posts. After keyword sorting and manual screening， a total of 66 761 health science posts were collected， with WeChat Official Accounts， Douyin， and Weibo having the top three highest number of posts. Video-based new media such as Douyin， WeChat Videos， Kuaishou， and Bilibili had a better communication impact than text and image-based new media like WeChat Official Accounts， Weibo， and Toutiao. Among them， Douyin and Toutiao were the best platforms for video and text-image-based new media， respectively.ConclusionMedical institutions in Shanghai recognize the importance of new media in health communication and have made full use of various media platforms to carry out health communication，having a certain impact on health education. In the future， medical institutions should choose appropriate platforms based on target audiences and content characteristics， fully leverage the advantages of various platforms， explore innovative communication strategies， promote the dissemination of health knowledge， and enhance the health literacy of the public.

Published
2024
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30. Association between health insurance cost-sharing and choice of hospital tier for cardiovascular diseases in China: a prospective cohort study

Author

Chen, Junshi, Chen, Zhengming, Clarke, Robert, Collins, Rory, Li, Liming, Wang, Chen, Lv, Jun, Peto, Richard, Walters, Robin, Avery, Daniel, Barnard, Maxim, Bennett, Derrick, Boxall, Ruth, Chan, Kahung, Chen, Yiping, Clarke, Johnathan, Du, Huaidong, Mohamed, Ahmed Edris, Fry, Hannah, Gilbert, Simon, Im, Pek Kei, Iona, Andri, Kakkoura, Maria, Kartsonaki, Christiana, Lam, Hubert, Lin, Kuang, Liu, James, Mazidi, Mohsen, Millwood, Iona, Morris, Sam, Nie, Qunhua, Pozaricki, Alfred, Ryder, Paul, Said, Saredo, Schmidt, Dan, Stevens, Becky, Turnbull, Iain, Wang, Baihan, Wang, Lin, Wright, Neil, Yang, Ling, Yang, Xiaoming, Yao, Pang, Han, Xiao, Hou, Can, Xia, Qingmei, Liu, Chao, Pei, Pei, Sun, Dianjianyi, Yu, Canqing, Chen, Naying, Liu, Duo, Tang, Zhenzhu, Chen, Ningyu, Jiang, Qilian, Lan, Jian, Li, Mingqiang, Liu, Yun, Meng, Fanwen, Meng, Jinhuai, Pan, Rong, Qin, Yulu, Wang, Ping, Wang, Sisi, Wei, Liuping, Zhou, Liyuan, Dong, Caixia, Ge, Pengfei, Ren, Xiaolan, Li, Zhongxiao, Mao, Enke, Wang, Tao, Zhang, Hui, Zhang, Xi, Chen, Jinyan, Hu, Ximin, Wang, Xiaohuan, Guo, Zhendong, Li, Huimei, Li, Yilei, Weng, Min, Wu, Shukuan, Yan, Shichun, Zou, Mingyuan, Zhou, Xue, Guo, Ziyan, Kang, Quan, Li, Yanjie, Yu, Bo, Xu, Qinai, Chang, Liang, Fan, Lei, Feng, Shixian, Zhang, Ding, Zhou, Gang, Gao, Yulian, He, Tianyou, He, Pan, Hu, Chen, Sun, Huarong, Zhang, Xukui, Chen, Biyun, Fu, Zhongxi, Huang, Yuelong, Liu, Huilin, Xu, Qiaohua, Yin, Li, Long, Huajun, Xu, Xin, Zhang, Hao, Zhang, Libo, Su, Jian, Tao, Ran, Wu, Ming, Yang, Jie, Zhou, Jinyi, Zhou, Yonglin, Hu, Yihe, Hua, Yujie, Jin, Jianrong, Liu, Fang, Liu, Jingchao, Lu, Yan, Ma, Liangcai, Tang, Aiyu, Zhang, Jun, Cheng, Liang, Du, Ranran, Gao, Ruqin, Li, Feifei, Li, Shanpeng, Liu, Yongmei, Ning, Feng, Pang, Zengchang, Sun, Xiaohui, Tian, Xiaocao, Wang, Shaojie, Zhai, Yaoming, Zhang, Hua, Hou, Wei, Lv, Silu, Wang, Junzheng, Chen, Xiaofang, Wu, Xianping, Zhang, Ningmei, Chang, Xiaoyu, Li, Jianguo, Liu, Jiaqiu, Luo, Guojin, Sun, Qiang, Zhong, Xunfu, Gong, Weiwei, Hu, Ruying, Wang, Hao, Wang, Meng, Yu, Min, Chen, Lingli, Gu, Qijun, Pan, Dongxia, Wang, Chunmei, Xie, Kaixu, Zhang, Xiaoyi, Chen, Hongyuan, Liu, Liyang, Gou, Haiyan, Wang, Xun, Ding, Jing, Zhang, Ning, Mao, Yueshi, Zhou, Shanshan, Jin, Lirong, Cheng, Xin, Lu, Yun, Chen, Li, Hao, Zilong, Xing, Xiaona, Wang, Lei, Ju, Naixin, Mao, Yiting, Li, Shuya, Du, Peng, Wang, Deren, Sun, Xiaojia, You, Shihao, Wang, Weizhi, Zhu, Yanmei, Li, Xiaojiu, Dong, Yi, Levy, Muriel, Buckell, John, Wu, Nina, Yip, Winnie, and Mihaylova, Borislava

Published
2024
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34. Causal association between snoring and stroke: a Mendelian randomization study in a Chinese population

Author

Chen, Junshi, Chen, Zhengming, Clarke, Robert, Collins, Rory, Guo, Yu, Li, Liming, Lv, Jun, Peto, Richard, Walter, Robin, Avery, Daniel, Bennett, Derrick, Boxall, Ruth, Burgess, Sue, Chan, Ka Hung, Chang, Yumei, Chen, Yiping, Clarke, Johnathan, Du, Huaidong, Mohamed, Ahmed Edris, Fairhurst-Hunter, Zammy, Fry, Hannah, Hill, Mike, Holmes, Michael, Im, Pek Kei, Iona, Andri, Kakkoura, Maria, Kartsonaki, Christiana, Kerosi, Rene, Lin, Kuang, Mazidi, Mohsen, Millwood, Iona, Morris, Sam, Nie, Qunhua, Pozarickij, Alfred, Ryder, Paul, Said, Saredo, Schmidt, Dan, Sherliker, Paul, Stevens, Becky, Turnbull, Iain, Walters, Robin, Wang, Lin, Wright, Neil, Yang, Ling, Yang, Xiaoming, Yao, Pang, Han, Xiao, Hou, Can, Pei, Pei, Liu, Chao, Yu, Canqing, Xia, Qingmei, Pang, Zengchang, Gao, Ruqin, Li, Shanpeng, Duan, Haiping, Wang, Shaojie, Liu, Yongmei, Du, Ranran, Zang, Yajing, Cheng, Liang, Tian, Xiaocao, Zhang, Hua, Zhai, Yaoming, Ning, Feng, Sun, Xiaohui, Li, Feifei, Lv, Silu, Wang, Junzheng, Hou, Wei, Sun, Wei, Yan, Shichun, Cui, Xiaoming, Wang, Chi, Wu, Zhenyuan, Li, Yanjie, Kang, Quan, Luo, Huiming, Ou, Tingting, Zheng, Xiangyang, Guo, Zhendong, Wu, Shukuan, Li, Yilei, Li, Huimei, Wu, Ming, Zhou, Yonglin, Zhou, Jinyi, Tao, Ran, Yang, Jie, Su, Jian, Liu, Fang, Zhang, Jun, Hu, Yihe, Lu, Yan, Ma, Liangcai, Tang, Aiyu, Zhang, Shuo, Jin, Jianrong, Liu, Jingchao, Lin, Mei, Lu, Zhenzhen, Zhou, Lifang, Xie, Changping, Lan, Jian, Zhu, Tingping, Liu, Yun, Wei, Liuping, Zhou, Liyuan, Chen, Ningyu, Qin, Yulu, Wang, Sisi, Wu, Xianping, Zhang, Ningmei, Chen, Xiaofang, Chang, Xiaoyu, Yuan, Mingqiang, Wu, Xia, Jiang, Wei, Liu, Jiaqiu, Sun, Qiang, Chen, Faqing, Ren, Xiaolan, Dong, Caixia, Zhang, Hui, Mao, Enke, Wang, Xiaoping, Wang, Tao, zhang, Xi, Kang, Kai, Feng, Shixian, Tian, Huizi, Fan, Lei, Li, XiaoLin, Sun, Huarong, He, Pan, Zhang, Xukui, Yu, Min, Hu, Ruying, Wang, Hao, Zhang, Xiaoyi, Cao, Yuan, Xie, Kaixu, Chen, Lingli, Shen, Dun, Li, Xiaojun, Jin, Donghui, Yin, Li, Liu, Huilin, Fu, Zhongxi, Xu, Xin, Zhang, Hao, Chen, Jianwei, Peng, Yuan, Zhang, Libo, Qu, Chan, Zhu, Yunqing, Zhuang, Zhenhuang, Sun, Dianjianyi, Millwood, Iona Y., and Walters, Robin G.

Published
2024
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37. Habits of Using Online Health Information and eHealth Literacy in Middle-aged and Elderly Residents

Author

YUAN Cheng, WEI Xiaomin, WU Xiaoyu, LIU Huilin, JIANG Zongmin

Subjects
middle-aged and elderly, ehealth literacy, health information, usage habits, internet, community health services, shanghai, Medicine
Abstract

Background Since the Internet dependence of the supply and demand sides of health popularization is increasing, eHealth literacy has become a primary skill that chronic disease patients need to master. Objective To analyze the current situation and existing problems of online health information usage habits and eHealth literacy in middle-aged and elderly residents, and discuss the relationship between online health information usage habits and eHealth literacy, providing a reference for improving the level of eHealth literacy and developing appropriate Internet-based health popularization services for these populations. Methods From June to September 2021, a questionnaire survey was conducted among 1 061 middle-aged and elderly residents in Shanghai communities selected by multistage sampling using the General Information Questionnaire and the Chinese version of the eHealth Literacy Scale (eHEALS-C) to understand their habits of using online health information and levels of eHealth literacy. Stepwise multiple linear regression was used to assess the association of habit of using online health information and other potential candidate factors with the level of eHealth literacy. Results Altogether, 1 019 cases (96.04%) who effectively responded to the survey were included for analysis. Following health care accounts〔411 cases (40.33%) 〕was a major channel used for seeking online health information, followed by the search functionality or engines〔336 cases (32.97%) 〕, while the health apps or mini programs〔254 cases (24.93%) 〕, and online health communities〔65 cases (6.38%) 〕were less used. The online health information was less sourced from medical institutions〔397 cases (38.96%) 〕and medical workers〔187 cases (18.35%) 〕. The average total score of the eHEALS-C for the respondents was (27.62±8.57). The respondents scored lower in item 2〔 (3.36±1.27) points〕and item 6〔 (3.38±1.23) points〕of the eHEALS-C, indicating that they had low awareness of applying online health information to health problems, and low perceived skills at evaluating the online health information. Multiple linear regression analysis showed that age, participation in both basic and commercial medical insurance, using at least two channels for seeking online health information, having at least two sources of online health information, the habit of forwarding and sharing online health information, participation in collective online learning, and having an attitude of acceptance towards online health information were influencing factors of the level of eHealth literacy. Conclusion The eHealth literacy of these middle-aged and elderly residents is at a medium level, which is affected by online health information usage habits and other factors. To help them make better use of online health information and to improve their eHealth literacy, it is suggested to spread integrated authoritative information and retrieval platforms among these populations, give priority to supervision and elderly-oriented transformation of online health information platforms, and mobilize community, family, health associations, and commercial medical insurance institutions to co-deliver health-promotion services targeting these people.

Published
2023
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42. A wide landscape of morbidity and mortality risk associated with marital status in 0.5 million Chinese men and women: a prospective cohort study

Author

Chen, Junshi, Chen (PI, Zhengming, Clarke, Robert, Collins, Rory, Guo, Yu, Li (PI, Liming, Lv, Jun, Peto, Richard, Walters, Robin, Avery, Daniel, Bennett, Derrick, Boxall, Ruth, Burgess, Sue, Chan, Ka Hung, Chang, Yumei, Chen, Yiping, Chen, Zhengming, Clarke, Johnathan, Du, Huaidong, Mohamed, Ahmed Edris, Fairhurst-Hunter, Zammy, Fry, Hannah, Hill, Mike, Holmes, Michael, Im, Pek Kei, Iona, Andri, Kakkoura, Maria, Kartsonaki, Christiana, Kerosi, Rene, Lin, Kuang, Mazidi, Mohsen, Millwood, Iona, Morris, Sam, Nie, Qunhua, Pozarickij, Alfred, Ryder, Paul, Said, Saredo, Schmidt, Dan, Sherliker, Paul, Stevens, Becky, Turnbull, Iain, Wang, Lin, Wright, Neil, Yang, Ling, Yang, Xiaoming, Yao, Pang, Han, Xiao, Hou, Can, Pei, Pei, Liu, Chao, Yu, Canqing, Xia, Qingmei, Pang, Zengchang, Gao, Ruqin, Li, Shanpeng, Duan, Haiping, Wang, Shaojie, Liu, Yongmei, Du, Ranran, Zang, Yajing, Cheng, Liang, Tian, Xiaocao, Zhang, Hua, Zhai, Yaoming, Ning, Feng, Sun, Xiaohui, Li, Feifei, Lv, Silu, Wang, Junzheng, Hou, Wei, Sun, Wei, Yan, Shichun, Cui, Xiaoming, Wang, Chi, Wu, Zhenyuan, Li, Yanjie, Kang, Quan, Luo, Huiming, Ou, Tingting, Zheng, Xiangyang, Guo, Zhendong, Wu, Shukuan, Li, Yilei, Li, Huimei, Wu, Ming, Zhou, Yonglin, Zhou, Jinyi, Tao, Ran, Yang, Jie, Su, Jian, Liu, Fang, Zhang, Jun, Hu, Yihe, Lu, Yan, Ma, Liangcai, Tang, Aiyu, Zhang, Shuo, Jin, Jianrong, Liu, Jingchao, Lin, Mei, Lu, Zhenzhen, Zhou, Lifang, Xie, Changping, Lan, Jian, Zhu, Tingping, Liu, Yun, Wei, Liuping, Zhou, Liyuan, Chen, Ningyu, Qin, Yulu, Wang, Sisi, Wu, Xianping, Zhang, Ningmei, Chen, Xiaofang, Chang, Xiaoyu, Yuan, Mingqiang, Wu, Xia, Jiang, Wei, Liu, Jiaqiu, Sun, Qiang, Chen, Faqing, Ren, Xiaolan, Dong, Caixia, Zhang, Hui, Mao, Enke, Wang, Xiaoping, Wang, Tao, zhang, Xi, Kang, Kai, Feng, Shixian, Tian, Huizi, Fan, Lei, Li, XiaoLin, Sun, Huarong, He, Pan, Zhang, Xukui, Yu, Min, Hu, Ruying, Wang, Hao, Zhang, Xiaoyi, Cao, Yuan, Xie, Kaixu, Chen, Lingli, Shen, Dun, Li, Xiaojun, Jin, Donghui, Yin, Li, Liu, Huilin, Fu, Zhongxi, Xu, Xin, Zhang, Hao, Chen, Jianwei, Peng, Yuan, Zhang, Libo, Qu, Chan, Xiao, Meng, Li, Aolin, Wang, Yueqing, Pang, Yuanjie, Li, Liming, and Sun, Dianjianyi

Published
2024
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47. Review of Deep Learning in Classification of Tongue Image

Author

WU Xin, XU Hong, LIN Zhuosheng, LI Shengke, LIU Huilin, FENG Yue

Subjects
traditional chinese medicine tongue diagnosis, deep learning, tongue image analysis, tongue image classification, Electronic computers. Computer science, QA75.5-76.95
Abstract

With the rapid development of technology and the improvement of computing power, deep learning will be widely used in the field of tongue classification. The classification of tongue image is an important part of tongue diagnosis in traditional Chinese medicine (TCM). Traditional tongue diagnosis is dependent on understanding and judgment skills gained from personal experience under the guidance of basic theory, which leads to ambiguity and variability, affecting diagnostic reproducibility. In order to reduce the error of subjective judgment, many researchers have devoted themselves to realizing the objectification, quantification and automation of tongue diagnosis in TCM through deep learning. This paper analyzes and summarizes the research status of tongue image classification methods based on deep learning. In the study of tongue image classification, various deep learning methods are used as the research objects. The research objects are divided into categories based on early neural networks, convolutional neural networks, regional convolutional neural networks, transfer learning and other methods for summary analysis. TCM syndromes/diseases in tongue diagnosis and classification of physical constitution are discussed. A 5-fold cross-validation experiment is conducted with the public tongue diagnosis dataset on Kaggle. The dataset is a small sample of the dentate tongue, and the classification methods based on deep learning and transfer learning are evaluated. The research development of single-label and multi-label classification is discussed and prospected.

Published
2023
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