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Experiences Receiving and Delivering Virtual Health Care For Women: Qualitative Evidence Synthesis

Karen M Goldstein, Sharron Rushton, Allison A Lewinski, Abigail Shapiro, Tiera Lanford-Davey, Jessica N Coleman, Neetu Chawla, Dhara B Patel, Katherine Van Loon, Megan Shepherd-Banigan, Catherine Sims, Sarah Cantrell, Susan Alton Dailey, Jennifer M Gierisch

J Med Internet Res 2025;27:e68314

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A Deep Learning–Enabled Workflow to Estimate Real-World Progression-Free Survival in Patients With Metastatic Breast Cancer: Study Using Deidentified Electronic Health Records

Gowtham Varma, Rohit Kumar Yenukoti, Praveen Kumar M, Bandlamudi Sai Ashrit, K Purushotham, C Subash, Sunil Kumar Ravi, Verghese Kurien, Avinash Aman, Mithun Manoharan, Shashank Jaiswal, Akash Anand, Rakesh Barve, Viswanathan Thiagarajan, Patrick Lenehan, Scott A Soefje, Venky Soundararajan

JMIR Cancer 2025;11:e64697

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Mobile Therapeutic Attention for Treatment-Resistant Schizophrenia (m-RESIST) Solution for Improving Clinical and Functional Outcomes in Treatment-Resistant Schizophrenia: Prospective, Multicenter Efficacy Study

In this study, the intervention involved patients and their caregivers, and the main actors involved in the deployment of the m-RESIST solution were psychiatrists, psychologists, and case managers. The key aim of the m-RESIST solution was to engage patients with TRS, together with their caregivers, in therapeutic processes, and empower them to enable their self-management.

Jussi Seppälä, Eva Grasa, Anna Alonso-Solis, Alexandra Roldan-Bejarano, Marianne Haapea, Matti Isohanni, Jouko Miettunen, Johanna Caro Mendivelso, Cari Almazán, Katya Rubinstein, Asaf Caspi, Zolt Unoka, Kinga Farkas, Elisenda Reixach, Jesus Berdun, Judith Usall, Susana Ochoa, Iluminada Corripio, Erika Jääskeläinen, m-Resist Group

JMIR Hum Factors 2025;12:e67659

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Prevalence of Multiple Chronic Conditions Among Adults in the All of Us Research Program: Exploratory Analysis

Xintong Li, Caitlin Dreisbach, Carolina M Gustafson, Komal Patel Murali, Theresa A Koleck

JMIR Form Res 2025;9:e69138

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Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population–Based Study

To compare differences in patient characteristics based on CHD incidence (yes or no), we used various statistical tests including 2-tailed Student t tests, Mann‒Whitney U tests, or chi-square tests, as appropriate. Feature selection was conducted in a stepwise manner to ensure that only the most relevant variables were included in the predictive models. Initially, variables with more than 30% missing data were excluded to avoid potential bias from imputation.

Thien Vu, Yoshihiro Kokubo, Mai Inoue, Masaki Yamamoto, Attayeb Mohsen, Agustin Martin-Morales, Research Dawadi, Takao Inoue, Jie Ting Tay, Mari Yoshizaki, Naoki Watanabe, Yuki Kuriya, Chisa Matsumoto, Ahmed Arafa, Yoko M Nakao, Yuka Kato, Masayuki Teramoto, Michihiro Araki

JMIR Cardio 2025;9:e68066

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Digital Mental Health Interventions for Young People Aged 16-25 Years: Scoping Review

Courtney Potts, Carmen Kealy, Jamie M McNulty, Alba Madrid-Cagigal, Thomas Wilson, Maurice D Mulvenna, Siobhan O'Neill, Gary Donohoe, Margaret M Barry

J Med Internet Res 2025;27:e72892

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Cocreating the Visualization of Digital Mobility Outcomes: Delphi-Type Process With Patients

Jack Lumsdon, Cameron Wilson, Lisa Alcock, Clemens Becker, Francesco Benvenuti, Tecla Bonci, Koen van den Brande, Gavin Brittain, Philip Brown, Ellen Buckley, Marco Caruso, Brian Caulfield, Andrea Cereatti, Laura Delgado-Ortiz, Silvia Del Din, Jordi Evers, Judith Garcia-Aymerich, Heiko Gaßner, Tova Gur Arieh, Clint Hansen, Jeffrey M Hausdorff, Hugo Hiden, Emily Hume, Cameron Kirk, Walter Maetzler, Dimitrios Megaritis, Lynn Rochester, Kirsty Scott, Basil Sharrack, Norman Sutton, Beatrix Vereijken, Ioannis Vogiatzis, Alison Yarnall, Alison Keogh, Alma Cantu

JMIR Form Res 2025;9:e68782

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Machine Learning Clinical Decision Support for Interdisciplinary Multimodal Chronic Musculoskeletal Pain Treatment: Prospective Pilot Study of Patient Assessment and Prognostic Profile Validation

Profile accuracy: H=high, M=medium, L=low. AUC: area under the curve; M: mixed; N: negative; P: positive; TPR: true-positive rate; TNR: true-negative rate. The above summary (Figure 2) presents results for all pilot study patients to show performance and overall results. However, the individual prognostic patient profile as used in IMPT clinical assessment provides clearly presented summary results for each patient.

Fredrick Zmudzki, Rob J E M Smeets, Jan S Groenewegen, Erik van der Graaff

JMIR Rehabil Assist Technol 2025;12:e65890

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