Search Articles

View query in Help articles search

Search Results (1 to 10 of 289 Results)

Download search results: CSV END BibTex RIS


Implementation of a Quality Improvement and Clinical Decision Support Tool for Cancer Diagnosis in Primary Care: Process Evaluation

Implementation of a Quality Improvement and Clinical Decision Support Tool for Cancer Diagnosis in Primary Care: Process Evaluation

Complex interventions are used to assess the effectiveness and utility of such tools in general practice. Yet implementing complex interventions can be distinctly difficult, as they involve multiple interrelated components and there are often multiple levels where change is required [21]. Process evaluation can aid in the understanding of the factors that influence how or why a complex intervention succeeds or fails.

Sophie Chima, Barbara Hunter, Javiera Martinez-Gutierrez, Natalie Lumsden, Craig Nelson, Dougie Boyle, Kaleswari Somasundaram, Jo-Anne Manski-Nankervis, Jon Emery

JMIR Cancer 2025;11:e65461

Trajectory-Ordered Objectives for Self-Supervised Representation Learning of Temporal Healthcare Data Using Transformers: Model Development and Evaluation Study

Trajectory-Ordered Objectives for Self-Supervised Representation Learning of Temporal Healthcare Data Using Transformers: Model Development and Evaluation Study

Recent studies have underscored the effectiveness of using multihead transformer architecture and MLM self-supervised learning in the domain of EHR trajectory modeling. While these methods have exhibited superior performance in various contexts, we focus on investigating their limitations related to sequential order learning and propose enhancements to address this issue.

Ali Amirahmadi, Farzaneh Etminani, Jonas Björk, Olle Melander, Mattias Ohlsson

JMIR Med Inform 2025;13:e68138

Using Personalized Intervention Criteria in a Mobile Just-in-Time Adaptive Intervention for Increasing Physical Activity in University Students: Pilot Study

Using Personalized Intervention Criteria in a Mobile Just-in-Time Adaptive Intervention for Increasing Physical Activity in University Students: Pilot Study

We specified 2 different sets of models to examine the effectiveness of using JITAI implemented with PIC to increase physical activity. Model set A was specified to examine the effects of PIC or UIC on physical activity after the intervention. To examine the change in physical activity after 1 hour of intervention by different intervention criteria, the mean level of physical activity was estimated for the group using UIC and the group using PIC at the hour before and the hour after the intervention.

Mai Ikegaya, Jerome Clifford Foo, Taiga Murata, Kenta Oshima, Jinhyuk Kim

JMIR Hum Factors 2025;12:e66750

Effect of a Tailored eHealth Physical Activity Intervention on Physical Activity and Depression During Postpartum: Randomized Controlled Trial (The Postpartum Wellness Study)

Effect of a Tailored eHealth Physical Activity Intervention on Physical Activity and Depression During Postpartum: Randomized Controlled Trial (The Postpartum Wellness Study)

Technology-based electronic health (e Health) interventions may address common barriers to being physically active in the postpartum period, and as such are a promising approach to increasing PA levels in postpartum individuals at high risk for PPD. e Health PA interventions have demonstrated effectiveness in increasing PA in the general adult population [27,28].

Sylvia E Badon, Nina Oberman, Maya Ramsey, Charles P Quesenberry, Elaine Kurtovich, Lizeth Gomez Chavez, Susan D Brown, Cheryl L Albright, Mibhali Bhalala, Lyndsay A Avalos

JMIR Ment Health 2025;12:e64507

Effectiveness and Methodologies of Virtual Reality Dental Simulators for Veneer Tooth Preparation Training: Randomized Controlled Trial

Effectiveness and Methodologies of Virtual Reality Dental Simulators for Veneer Tooth Preparation Training: Randomized Controlled Trial

Flow chart for investigating the teaching effectiveness of 2 dental simulators. Flow chart for varying training patterns between the 2 simulators. The first component of the study compared the training efficacy of dental simulators and traditional phantom heads. The participants of groups 1, 2, and 3 used Unidental, Simodont, and traditional phantom heads, respectively, for skill training, and were then assessed using traditional phantom heads.

Yaning Li, Hongqiang Ye, Wenxiao Wu, Jiayi Li, Xiaohan Zhao, Yunsong Liu, Yongsheng Zhou

J Med Internet Res 2025;27:e63961

Assessing ChatGPT’s Capability as a New Age Standardized Patient: Qualitative Study

Assessing ChatGPT’s Capability as a New Age Standardized Patient: Qualitative Study

Guided by these aims, our investigation focused on the following research questions: (1) How do students perceive the effectiveness of Chat GPT compared with traditional SPs in medical training scenarios? (2) To what extent can Chat GPT function effectively as a virtual SP in medical education? By addressing these questions, our study seeks to inform the potential integration of AI-driven virtual SPs into medical curricula, particularly in settings where access to traditional SPs is limited.

Joseph Cross, Tarron Kayalackakom, Raymond E Robinson, Andrea Vaughans, Roopa Sebastian, Ricardo Hood, Courtney Lewis, Sumanth Devaraju, Prasanna Honnavar, Sheetal Naik, Jillwin Joseph, Nikhilesh Anand, Abdalla Mohammed, Asjah Johnson, Eliran Cohen, Teniola Adeniji, Aisling Nnenna Nnaji, Julia Elizabeth George

JMIR Med Educ 2025;11:e63353

Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis

Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis

The results showed that sample size significantly influenced heterogeneity among studies, with diagnostic accuracy tending to decrease as sample size increased (regression coefficient=–1.384, P This research aimed to evaluate the effectiveness of different ML algorithms in predicting ARDS. A total of 17 studies were reviewed, which identified 64 different ARDS prediction models.

Jinxi Yang, Siyao Zeng, Shanpeng Cui, Junbo Zheng, Hongliang Wang

J Med Internet Res 2025;27:e66615