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The Efficacy of Digital Interventions on Adherence to Oral Systemic Anticancer Therapy Among Patients With Cancer: Systematic Review and Meta-Analysis

The Efficacy of Digital Interventions on Adherence to Oral Systemic Anticancer Therapy Among Patients With Cancer: Systematic Review and Meta-Analysis

A meta-analysis involving 11 studies across various diseases demonstrated that reminder-based interventions, including text messages, phone calls, and video calls, significantly improved adherence, with 65.94% of prescribed doses taken in the reminder groups compared with 54.71% in control groups (P=.04) [19].

Wan-Chuen Liao, Fiona Angus, Jane Conley, Li-Chia Chen

JMIR Cancer 2025;11:e64208

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A Digital Photo Activity Intervention for Nursing Home Residents With Dementia and Their Carers: Mixed Methods Process Evaluation

A Digital Photo Activity Intervention for Nursing Home Residents With Dementia and Their Carers: Mixed Methods Process Evaluation

Background characteristics of nursing home residents in both the experimental and control groups who completed the semistructured interviews after the 4-week intervention. a Significance level set at P b Chi-square test with Yates continuity correction. c Not applicable. d GDS: Global Deterioration Scale.

Josephine Rose Orejana Tan, David P Neal, Maria Vilmen, Petra Boersma, Teake P Ettema, Robbert J J Gobbens, Sietske A M Sikkes, Rose-Marie Dröes

JMIR Form Res 2025;9:e56586

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A Web-Based Tool to Perform a Values Clarification for Stroke Prevention in Patients With Atrial Fibrillation: Design and Preliminary Testing Study

A Web-Based Tool to Perform a Values Clarification for Stroke Prevention in Patients With Atrial Fibrillation: Design and Preliminary Testing Study

The overall SURE test, saying “yes” to all 4 components, was 61.2% (156/255) for the standard group, 66.5% (145/218) for the visual group, and 67% (134/200) for the visual+VC group (visual vs standard, odds ratio [OR] 1.26, 95% CI 0.86‐1.84; P=.23; visual+VC vs standard, OR 1.29, 95% CI 0.87‐1.90; P=.20).

Michael P Dorsch, Allen J Flynn, Kaitlyn M Greer, Sabah Ganai, Geoffrey D Barnes, Brian Zikmund-Fisher

JMIR Cardio 2025;9:e67956

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Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study

Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study

All reported P values were corrected for multiple tests using the Bonferroni correction. The overview of the study is shown in Figure 1. Overview of the study. H-PEACE: Health and Prevention Enhancement, Ko GES: Korean Genome and Epidemiology Study, GENIE: gene-environmental interaction and phenotype, CT: computed tomography, MRI: magnetic resonance imaging, MRA: magnetic resonance angiography.

Chang-Uk Jeong, Jacob S Leiby, Dokyoon Kim, Eun Kyung Choe

JMIR Aging 2025;8:e64473

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A Novel Just-in-Time Intervention for Promoting Safer Drinking Among College Students: App Testing Across 2 Independent Pre-Post Trials

A Novel Just-in-Time Intervention for Promoting Safer Drinking Among College Students: App Testing Across 2 Independent Pre-Post Trials

There was no significant effect of the study phase or incentives on any of the self-reported drinking outcomes, for the average number of days per week in the last month involving alcohol consumption (F2, 232=0.294, P=.75, η2=.003), typical weekend evening drink consumption in the last month (F2, 165=0.662, P=.52, η2=.008), the maximum number of drinks consumed in the last month (F2, 175=0.005, P=.99, η2=.00), or protective behavioral strategies (F2, 232=1.469, P=.23, η2=.013).

Philip I Chow, Jessica Smith, Ravjot Saini, Christina Frederick, Connie Clark, Maxwell Ritterband, Jennifer P Halbert, Kathryn Cheney, Katharine E Daniel, Karen S Ingersoll

JMIR Hum Factors 2025;12:e69873

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Comparison of Deep Learning Approaches Using Chest Radiographs for Predicting Clinical Deterioration: Retrospective Observational Study

Comparison of Deep Learning Approaches Using Chest Radiographs for Predicting Clinical Deterioration: Retrospective Observational Study

The p-values of the AUROC scores are presented in Multimedia Appendix 1. As P Data cleaning and cohort selection with descriptive analysis were conducted using Stata version 16.1 (Stata Corp). We used Python version 3.8.10, along with the Monai framework version 1.2.0 (NVIDIA) and Pytorch version 2.0.0 (Facebook) to develop the deep learning models. Additionally, the AUROC score and its 95% CI were calculated using Fast De Long implementation from VMAF (Video Multimethod Assessment Fusion; Netflix) [43].

Mahmudur Rahman, Jifan Gao, Kyle A Carey, Dana P Edelson, Askar Afshar, John W Garrett, Guanhua Chen, Majid Afshar, Matthew M Churpek

JMIR AI 2025;4:e67144

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