Search Results (1 to 10 of 395 Results)
Download search results: CSV END BibTex RIS
Skip search results from other journals and go to results- 157 Journal of Medical Internet Research
- 53 JMIR mHealth and uHealth
- 46 JMIR Medical Informatics
- 42 JMIR Public Health and Surveillance
- 29 JMIR Research Protocols
- 20 JMIR Formative Research
- 8 JMIR Human Factors
- 7 JMIR Serious Games
- 5 JMIR Aging
- 5 JMIR Mental Health
- 4 Online Journal of Public Health Informatics
- 2 Interactive Journal of Medical Research
- 2 JMIR Diabetes
- 2 JMIR Infodemiology
- 2 JMIR Medical Education
- 2 JMIR Nursing
- 2 JMIR Pediatrics and Parenting
- 1 Iproceedings
- 1 JMIR AI
- 1 JMIR Biomedical Engineering
- 1 JMIR Cancer
- 1 JMIR Perioperative Medicine
- 1 JMIR Rehabilitation and Assistive Technologies
- 1 JMIR XR and Spatial Computing (JMXR)
- 0 Medicine 2.0
- 0 iProceedings
- 0 JMIR Preprints
- 0 JMIR Bioinformatics and Biotechnology
- 0 JMIR Challenges
- 0 JMIR Data
- 0 JMIR Cardio
- 0 Journal of Participatory Medicine
- 0 JMIR Dermatology
- 0 JMIRx Med
- 0 JMIRx Bio
- 0 Transfer Hub (manuscript eXchange)
- 0 JMIR Neurotechnology
- 0 Asian/Pacific Island Nursing Journal
Go back to the top of the page Skip and go to footer section
Go back to the top of the page Skip and go to footer section

E-Cigarette Narratives of User-Generated Posts on Xiaohongshu in China: Content Analysis
J Med Internet Res 2025;27:e71173
Download Citation: END BibTex RIS
Go back to the top of the page Skip and go to footer section

After that, the DN had a brief follow-up discussion (approximately 5 min) with participants to provide ongoing support and complete data collection.
Textbox 1 details the discussion topics that guided the study visits between the DN and participants, and Multimedia Appendix 1 collates the most common questions asked by the DN.
JMIR Ment Health 2025;12:e70154
Download Citation: END BibTex RIS

For survey data preprocessing, we standardized continuous variables using a Min-Max scaler and applied one-hot encoding to categorical variables. For actigraphy data, temporal pattern characteristics were extracted using an autoencoder. An autoencoder is a neural network composed of an encoder and a decoder, which enables automatic feature learning from unlabeled data [58]. Eight actigraphy features were extracted across the 4 designated periods.
J Med Internet Res 2025;27:e69379
Download Citation: END BibTex RIS

On average, trained clinicians spent 36.7% (219.6 min) less time using the Epic system per day than controls at 3 months postcourse (Figure 2). Time spent in the Notes module per day was 56.6% (29 min) lower for trained clinicians than controls at 3 months post-course (Figure 3).
JMIR Form Res 2025;9:e68491
Download Citation: END BibTex RIS
Go back to the top of the page Skip and go to footer section
Go back to the top of the page Skip and go to footer section
Go back to the top of the page Skip and go to footer section