• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

使用潜在狄利克雷分配主题模型和情感推理分析的价态感知词典对Reddit上2019年冠状病毒病大流行期间2型糖尿病管理数据进行调查:内容分析

Investigating Reddit Data on Type 2 Diabetes Management During the COVID-19 Pandemic Using Latent Dirichlet Allocation Topic Modeling and Valence Aware Dictionary for Sentiment Reasoning Analysis: Content Analysis.

作者信息

Nagpal Meghan, Jalali Niloofar, Sherifali Diana, Morita Plinio, Cafazzo Joseph A

机构信息

Institute of Health Policy, Management, & Evaluation, Dalla Lana School of Public Health, University of Toronto, Health Sciences Building, 4th Floor, Toronto, ON, M5T 3M6, Canada, 1 416 978 4326, 1 416 978 7350.

Centre for Digital Therapeutics, Techna Institute, University Health Network, Toronto, ON, Canada.

出版信息

JMIR Form Res. 2025 Feb 21;9:e51154. doi: 10.2196/51154.

DOI:10.2196/51154
PMID:39983050
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11870598/
Abstract

BACKGROUND

Type 2 diabetes (T2D) is a chronic disease that can be partially managed through healthy behaviors. However, the COVID-19 pandemic impacted how people managed T2D due to work and school closures and social isolation. Moreover, individuals with T2D were at increased risk of complications from COVID-19 and experienced worsened mental health due to stress and anxiety.

OBJECTIVE

This study aims to synthesize emerging themes related to the health behaviors of people living with T2D, and how they were affected during the early stages of the COVID-19 pandemic by examining Reddit forums dedicated to people living with T2D.

METHODS

Data from Reddit forums related to T2D, from January 2018 to early March 2021, were downloaded using the Pushshift API; support vector machines were used to classify whether a post was made in the context of the pandemic. Latent Dirichlet allocation topic modelling was performed to identify topics of discussion across the entire dataset and a subsequent iteration was performed to identify topics specific to the COVID-19 pandemic. Sentiment analysis using the VADER (Valence Aware Dictionary for Sentiment Reasoning) algorithm was performed to assess attitudes towards the pandemic.

RESULTS

From all posts, the identified topics of discussion were classified into the following themes: managing lifestyle (sentiment score 0.25, 95% CI 0.25-0.26), managing blood glucose (sentiment score 0.19, 95% CI 0.18-0.19), obtaining diabetes care (sentiment score 0.19, 95% CI 0.18-0.20), and coping and receiving support (sentiment score 0.34, 95% CI 0.33-0.35). Among the COVID-19-specific posts, the topics of discussion were coping with poor mental health (sentiment score 0.04, 95% CI -0.01 to0.11), accessing doctor and medications and controlling blood glucose (sentiment score 0.14, 95% CI 0.09-0.20), changing food habits during the pandemic (sentiment score 0.25, 95% CI 0.20-0.31), impact of stress on blood glucose levels (sentiment score 0.03, 95% CI -0.03 to 0.08), changing status of employment and insurance (sentiment score 0.17, 95% CI 0.13-0.22), and risk of COVID-19 complications (sentiment score 0.09, 95% CI 0.03-0.14). Overall, posts classified as COVID-19-related (0.12, 95% CI 0.01-0.15) were associated with a lower sentiment score than those classified as nonCOVID (0.25, 95% CI 0.24-0.25). This study was limited due to the lack of a method for assessing the demographics of users and verifying whether users had T2D.

CONCLUSIONS

Themes identified from Reddit data suggested that the COVID-19 pandemic significantly influenced how people with T2D managed their disease, particularly in terms of accessing care and dealing with the complications of the virus. Overall, the early stages of the pandemic negatively impacted the attitudes of people living with T2D. This study demonstrates that social media data can be a qualitative data source for understanding patient perspectives.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9637/11870598/a8a5dabe93ae/formative-v9-e51154-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9637/11870598/5b244b109451/formative-v9-e51154-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9637/11870598/a8a5dabe93ae/formative-v9-e51154-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9637/11870598/5b244b109451/formative-v9-e51154-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9637/11870598/a8a5dabe93ae/formative-v9-e51154-g002.jpg
摘要

背景

2型糖尿病(T2D)是一种慢性病,可通过健康行为进行部分管理。然而,由于工作和学校关闭以及社会隔离,新冠疫情影响了人们管理T2D的方式。此外,T2D患者感染新冠病毒后出现并发症的风险增加,且由于压力和焦虑,其心理健康状况恶化。

目的

本研究旨在通过研究Reddit上专门针对T2D患者的论坛,综合与T2D患者健康行为相关的新出现的主题,以及他们在新冠疫情早期阶段是如何受到影响的。

方法

使用Pushshift API下载2018年1月至2021年3月初Reddit上与T2D相关论坛的数据;使用支持向量机对帖子是否在疫情背景下发布进行分类。进行潜在狄利克雷分配主题建模以识别整个数据集中的讨论主题,并进行后续迭代以识别特定于新冠疫情的主题。使用VADER(用于情感推理的价态感知词典)算法进行情感分析,以评估对疫情的态度。

结果

从所有帖子中,识别出的讨论主题分为以下几类:管理生活方式(情感得分0.25,95%置信区间0.25 - 0.26)、管理血糖(情感得分0.19,95%置信区间0.18 - 0.19)、获得糖尿病护理(情感得分0.19,95%置信区间0.18 - 0.20)以及应对和获得支持(情感得分0.34,95%置信区间0.33 - 0.35)。在与新冠疫情相关的特定帖子中,讨论主题包括应对不良心理健康(情感得分0.04,95%置信区间 - 0.01至0.11)、看医生和获取药物以及控制血糖(情感得分0.14,95%置信区间0.09 - 0.20)、疫情期间改变饮食习惯(情感得分0.25,95%置信区间0.20 - 0.31)、压力对血糖水平的影响(情感得分0.03,95%置信区间 - 0.03至0.08)、就业和保险状况的变化(情感得分0.17,95%置信区间0.13 - 0.22)以及新冠病毒并发症的风险(情感得分0.09,95%置信区间0.03 - 0.14)。总体而言,被归类为与新冠疫情相关的帖子(0.12,95%置信区间0.01 - 0.15)的情感得分低于被归类为非新冠相关的帖子(0.25,95%置信区间0.24 - 0.25)。由于缺乏评估用户人口统计学特征和核实用户是否患有T2D的方法,本研究存在局限性。

结论

从Reddit数据中识别出的主题表明,新冠疫情显著影响了T2D患者管理疾病的方式,特别是在获得护理和应对病毒并发症方面。总体而言,疫情早期对T2D患者的态度产生了负面影响。本研究表明社交媒体数据可以成为了解患者观点的定性数据来源。

相似文献

1
Investigating Reddit Data on Type 2 Diabetes Management During the COVID-19 Pandemic Using Latent Dirichlet Allocation Topic Modeling and Valence Aware Dictionary for Sentiment Reasoning Analysis: Content Analysis.使用潜在狄利克雷分配主题模型和情感推理分析的价态感知词典对Reddit上2019年冠状病毒病大流行期间2型糖尿病管理数据进行调查:内容分析
JMIR Form Res. 2025 Feb 21;9:e51154. doi: 10.2196/51154.
2
Managing Type 2 Diabetes During the COVID-19 Pandemic: Scoping Review and Qualitative Study Using Systematic Literature Review and Reddit.2019冠状病毒病大流行期间2型糖尿病的管理:使用系统文献综述和Reddit进行的范围综述和定性研究
Interact J Med Res. 2024 Aug 8;13:e49073. doi: 10.2196/49073.
3
Social Media Insights Into US Mental Health During the COVID-19 Pandemic: Longitudinal Analysis of Twitter Data.社交媒体洞察美国在 COVID-19 大流行期间的心理健康状况:对 Twitter 数据的纵向分析。
J Med Internet Res. 2020 Dec 14;22(12):e21418. doi: 10.2196/21418.
4
Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study.关于新冠疫情的推文主题、趋势和情绪:时间信息监测研究
J Med Internet Res. 2020 Oct 23;22(10):e22624. doi: 10.2196/22624.
5
Sexually Transmitted Disease-Related Reddit Posts During the COVID-19 Pandemic: Latent Dirichlet Allocation Analysis.COVID-19 大流行期间与性传播疾病相关的 Reddit 帖子:潜在狄利克雷分配分析。
J Med Internet Res. 2022 Oct 31;24(10):e37258. doi: 10.2196/37258.
6
Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study.自然语言处理揭示了新冠疫情期间Reddit上脆弱的心理健康支持小组以及加剧的健康焦虑:一项观察性研究。
J Med Internet Res. 2020 Oct 12;22(10):e22635. doi: 10.2196/22635.
7
Analyzing Themes, Sentiments, and Coping Strategies Regarding Online News Coverage of Depression in Hong Kong: Mixed Methods Study.分析香港抑郁症在线新闻报道的主题、情感及应对策略:混合方法研究
J Med Internet Res. 2025 Feb 13;27:e66696. doi: 10.2196/66696.
8
Concerns Expressed by Chinese Social Media Users During the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data.新冠疫情期间中国社交媒体用户表达的担忧:对新浪微博数据的内容分析
J Med Internet Res. 2020 Nov 26;22(11):e22152. doi: 10.2196/22152.
9
Emergency Physician Twitter Use in the COVID-19 Pandemic as a Potential Predictor of Impending Surge: Retrospective Observational Study.《COVID-19 大流行期间急诊医师在 Twitter 上的使用情况可能预示着即将出现的疫情高峰:回顾性观察研究》
J Med Internet Res. 2021 Jul 14;23(7):e28615. doi: 10.2196/28615.
10
Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling Study.使用自然语言处理技术探索社交媒体对食品安全的看法:情感分析和主题建模研究。
J Med Internet Res. 2024 Mar 21;26:e47826. doi: 10.2196/47826.

本文引用的文献

1
Managing Type 2 Diabetes During the COVID-19 Pandemic: Scoping Review and Qualitative Study Using Systematic Literature Review and Reddit.2019冠状病毒病大流行期间2型糖尿病的管理:使用系统文献综述和Reddit进行的范围综述和定性研究
Interact J Med Res. 2024 Aug 8;13:e49073. doi: 10.2196/49073.
2
Impact of COVID-19 Pandemic on Healthcare Utilization among Patients with Type 2 Diabetes Mellitus: A Systematic Review.COVID-19 大流行对 2 型糖尿病患者医疗利用的影响:系统评价。
Int J Environ Res Public Health. 2023 Mar 4;20(5):4577. doi: 10.3390/ijerph20054577.
3
Patient-Generated Data Analytics of Health Behaviors of People Living With Type 2 Diabetes: Scoping Review.
2型糖尿病患者健康行为的患者生成数据分析:范围综述
JMIR Diabetes. 2021 Dec 20;6(4):e29027. doi: 10.2196/29027.
4
Impact of COVID-19 lockdown on glycemic control in patients with type 1 and type 2 diabetes mellitus: a systematic review.2019冠状病毒病封锁措施对1型和2型糖尿病患者血糖控制的影响:一项系统评价
Diabetol Metab Syndr. 2021 Sep 7;13(1):95. doi: 10.1186/s13098-021-00705-9.
5
Type 2 Diabetes Mellitus and COVID-19: A Narrative Review.2 型糖尿病与 COVID-19:叙事性综述。
Front Endocrinol (Lausanne). 2021 Mar 31;12:609470. doi: 10.3389/fendo.2021.609470. eCollection 2021.
6
Determinants of mental health outcomes among people with and without diabetes during the COVID-19 outbreak in the Arab Gulf Region.在阿拉伯海湾地区 COVID-19 爆发期间,有和没有糖尿病的人群的心理健康结果的决定因素。
J Diabetes. 2021 Apr;13(4):339-352. doi: 10.1111/1753-0407.13149. Epub 2021 Jan 17.
7
A cross sectional study reveals severe disruption in glycemic control in people with diabetes during and after lockdown in India.一项横断面研究显示,在印度封锁期间和封锁解除后,糖尿病患者的血糖控制严重紊乱。
Diabetes Metab Syndr. 2020 Nov-Dec;14(6):1579-1584. doi: 10.1016/j.dsx.2020.08.011. Epub 2020 Aug 18.
8
Factors leading to high morbidity and mortality of COVID-19 in patients with type 2 diabetes.导致 2 型糖尿病患者 COVID-19 发病率和死亡率高的因素。
J Diabetes. 2020 Dec;12(12):895-908. doi: 10.1111/1753-0407.13085. Epub 2020 Sep 2.
9
Using Social Media to Track Geographic Variability in Language About Diabetes: Analysis of Diabetes-Related Tweets Across the United States.利用社交媒体追踪糖尿病相关语言的地理变异性:对美国各地与糖尿病相关推文的分析
JMIR Diabetes. 2020 Jan 26;5(1):e14431. doi: 10.2196/14431.
10
Evaluating the Effect of a Diabetes Health Coach in Individuals with Type 2 Diabetes.评估糖尿病健康教练在 2 型糖尿病患者中的效果。
Can J Diabetes. 2016 Feb;40(1):84-94. doi: 10.1016/j.jcjd.2015.10.006.