e.g. mhealth
Search Results (1 to 7 of 7 Results)
Download search results: CSV END BibTex RIS
Skip search results from other journals and go to results- 5 Journal of Medical Internet Research
- 1 JMIR Formative Research
- 1 JMIR Infodemiology
- 0 Medicine 2.0
- 0 Interactive Journal of Medical Research
- 0 iProceedings
- 0 JMIR Research Protocols
- 0 JMIR Human Factors
- 0 JMIR Medical Informatics
- 0 JMIR Public Health and Surveillance
- 0 JMIR mHealth and uHealth
- 0 JMIR Serious Games
- 0 JMIR Mental Health
- 0 JMIR Rehabilitation and Assistive Technologies
- 0 JMIR Preprints
- 0 JMIR Bioinformatics and Biotechnology
- 0 JMIR Medical Education
- 0 JMIR Cancer
- 0 JMIR Challenges
- 0 JMIR Diabetes
- 0 JMIR Biomedical Engineering
- 0 JMIR Data
- 0 JMIR Cardio
- 0 Journal of Participatory Medicine
- 0 JMIR Dermatology
- 0 JMIR Pediatrics and Parenting
- 0 JMIR Aging
- 0 JMIR Perioperative Medicine
- 0 JMIR Nursing
- 0 JMIRx Med
- 0 JMIRx Bio
- 0 Transfer Hub (manuscript eXchange)
- 0 JMIR AI
- 0 JMIR Neurotechnology
- 0 Asian/Pacific Island Nursing Journal
- 0 Online Journal of Public Health Informatics
- 0 JMIR XR and Spatial Computing (JMXR)
Main discussion themes were identified through the examination of all 15,364 posts from patients and caregivers regarding long COVID. This was performed using Biterm Topic Modeling (BTM) with the BTM R package [28]. BTM is a natural language processing–based text mining approach, which clusters similar texts on the basis of common discussion topics and provides lists of words to be interpreted for cluster labeling [29].
JMIR Infodemiology 2022;2(2):e39849
Download Citation: END BibTex RIS
Go back to the top of the page Skip and go to footer section
Telehealth Before and During the COVID-19 Pandemic: Analysis of Health Care Workers' Opinions
Examining the prevalence of discussion topics across time frames and commenters’ characteristics can shed light on the opinions or concerns about telehealth that are specific to these time frames and these individual characteristics. Structural topic modeling (STM) was used to identify the topics discussed in the comments. STM allows the analyst to incorporate information about the characteristics (metadata) and content of the documents into the modeling process [10].
J Med Internet Res 2022;24(2):e29519
Download Citation: END BibTex RIS
Reference 1: COVID-19 Vaccine-Related Discussion on Twitter: Topic Modeling and Sentiment AnalysisdiscussionComment on “COVID-19 Vaccine-Related Discussion on Twitter: Topic Modeling and Sentiment Analysis”
J Med Internet Res 2022;24(2):e31978
Download Citation: END BibTex RIS
Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis
We used Twitter as a proxy for public sentiment and were able to find the most important discussion topics that pertained to COVID-19 vaccines in the early days of the vaccine rollout. Additionally, we were able to classify public sentiment as it pertained to the vaccines and how this sentiment changed over time overall and in each topic as well. The goal of this research was to examine the discussion topics and public sentiment toward COVID-19 vaccines.
J Med Internet Res 2021;23(10):e30765
Download Citation: END BibTex RIS
COVID-19 Vaccine–Related Discussion on Twitter: Topic Modeling and Sentiment Analysis
Although social media data analysis has been widely performed for both health-related issues and emerging public health crises [10-14], analysis of big data of social media discussion on COVID-19 vaccines is limited [15,16]. To the best of our knowledge, in the most recently published work about COVID-19 vaccine–related social media discussion, the study period ended in November 2020 [16].
J Med Internet Res 2021;23(6):e24435
Download Citation: END BibTex RIS
The discussion flow was then directed to self-care and self-observation practices (ie, activities for self-tracking purposes, such as fitness apps, nutrition intake logs, or diaries with or without digital devices). The focus group guidelines are presented in Multimedia Appendix 1.
The first author (GM) moderated the focus groups, interfering only when the discussion lost its topic focus. The length of group discussions was between 76 and 82 minutes each.
J Med Internet Res 2020;22(11):e20528
Download Citation: END BibTex RIS