e.g. mhealth
Search Results (1 to 10 of 30 Results)
Download search results: CSV END BibTex RIS
Skip search results from other journals and go to results- 6 Journal of Medical Internet Research
- 5 JMIR Formative Research
- 5 JMIR Medical Informatics
- 3 JMIR Mental Health
- 3 JMIR mHealth and uHealth
- 2 JMIR Human Factors
- 2 JMIR Research Protocols
- 1 JMIR Medical Education
- 1 JMIR Neurotechnology
- 1 JMIR Pediatrics and Parenting
- 1 JMIR Serious Games
- 0 Medicine 2.0
- 0 Interactive Journal of Medical Research
- 0 iProceedings
- 0 JMIR Public Health and Surveillance
- 0 JMIR Rehabilitation and Assistive Technologies
- 0 JMIR Preprints
- 0 JMIR Bioinformatics and Biotechnology
- 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 Aging
- 0 JMIR Perioperative Medicine
- 0 JMIR Nursing
- 0 JMIRx Med
- 0 JMIRx Bio
- 0 JMIR Infodemiology
- 0 Transfer Hub (manuscript eXchange)
- 0 JMIR AI
- 0 Asian/Pacific Island Nursing Journal
- 0 Online Journal of Public Health Informatics
- 0 JMIR XR and Spatial Computing (JMXR)
Adolescent Emoji Use in Text-Based Messaging: Focus Group Study
Adolescent communication is increasingly mediated by text-based communication platforms like SMS or social media messaging. About 95% of US adolescents aged 13‐17 years have a smartphone for personal use [1], and adolescents in this age group send and receive an average of 67 text messages each day [2]. Text messaging is now the dominant mode of communication between adolescents [2] and is increasingly important to adolescents’ relationships with parents and other adults [3].
JMIR Form Res 2025;9:e59640
Download Citation: END BibTex RIS
Reference 1: Inpatient communication networks: leveraging secure text-messaging platforms to gain insight Reference 2: Reducing interdisciplinary communication failures through secure text messaging: a quality Reference 4: Resident and nurse perspectives on the use of secure text messaging systems Reference 11: A matter of urgency: reducing clinical text message interruptions during educational sessionstext
JMIR Med Inform 2025;13:e66859
Download Citation: END BibTex RIS
message word count and weekly text messages) among users of a text-based app where users send text messages to therapists “24/7” ([17], page 3) who then review messages during “standard working hours” and respond at least “once a day, 5 days a week” ([17], page 3).
JMIR Form Res 2025;9:e66301
Download Citation: END BibTex RIS
Large language models (LLMs), like Open AI’s GPT models, can streamline the classification and coding of unstructured EHR text due to their massive training data sets and advanced text processing [11,12]. LLMs have been used to categorize unstructured text from EHR systems [13], assist with qualitative analysis [14,15], and perform deductive coding with and without context [16]. Preliminary evidence shows that LLMs outperform crowd workers in annotation of health texts [17,18].
JMIR Med Inform 2025;13:e65454
Download Citation: END BibTex RIS
Providing false information in the form of text is also possible but requires more effort than simply ticking a lower number on the questionnaire. In addition, natural language processing (NLP) with a sufficiently large database can be used to detect lying [10].
JMIR Serious Games 2024;12:e56663
Download Citation: END BibTex RIS
Go back to the top of the page Skip and go to footer section
Reassessing AI in Medicine: Exploring the Capabilities of AI in Academic Abstract Synthesis
We propose that employing Chat GPT in a “layered progressive” manner for text generation could address this issue. This method involves dividing an article into smaller sections, having Chat GPT summarize each section individually, and then compiling these summaries into a cohesive whole. Such an approach is likely to yield better results than generating a summary from the entire text.
J Med Internet Res 2024;26:e55920
Download Citation: END BibTex RIS
Except for SMS text messages, all study recruitment and communication materials in this study were branded with standard Kaiser Permanente institutional affiliations, including the study flyer, emails, and portal messages. Patients who did not sign up on the day of vaccination received an invitation by SMS text message, email, or a notification through their online Kaiser Permanente health care portal account.
JMIR Mhealth Uhealth 2024;12:e58991
Download Citation: END BibTex RIS
Search strategies used for general databases were as follows:
Population: Psychosis OR Schizophrenia OR Schizoaffective OR Schizophrenia Spectrum OR Psychotic Disorders OR First-Episode Psychosis OR Early-Episode Psychosis
Intervention: SMS OR Short Message Service OR SMS-Survey OR Texting OR Text Message OR SMS Based System OR SMS Reminder OR Text Message Reminder OR Digital Health OR Telehealth OR Mobile Apps OR Mobile Applications OR Mobile Health OR e Health OR m Health OR Wearable Technology
Search strategies
JMIR Ment Health 2024;11:e57150
Download Citation: END BibTex RIS
A common method for information extraction using NLP is to treat it as a text classification task specific to each document type. However, document-specific text fine-tuning requires that each model be fine-tuned individually for each specific document type, which does not fully demonstrate the strength of automated processing. Fine-tuning a model requires labeled data, and since such data are unlikely to be available beforehand, it requires manual annotation by health care professionals.
JMIR Med Inform 2024;12:e58977
Download Citation: END BibTex RIS