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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

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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Correction: Use and Engagement With Low-Intensity Cognitive Behavioral Therapy Techniques Used Within an App to Support Worry Management: Content Analysis of Log Data

Correction: Use and Engagement With Low-Intensity Cognitive Behavioral Therapy Techniques Used Within an App to Support Worry Management: Content Analysis of Log Data

The authorship was previously published as: Paul Farrand1,2*, Ph D; Patrick J Raue3*, Ph D; Earlise Ward4*, Ph D; Dean Repper5*, MSc; Patricia Areán3*, Ph D The following author, equal contribution tag, ORCID, and associated affiliation have been added in the fifth position of the authorship: Jonathon Baker6*, MA (ORCID 0009 0000 9923 5042) Iona Mind Inc, Romford, United Kingdom The Authors' Contributions was revised from: PF, PA, and PJR conceptualized and designed the project with EW and DR providing theoretical

Paul Farrand, Patrick J Raue, Earlise Ward, Dean Repper, Jonathan Baker, Patricia Areán

JMIR Mhealth Uhealth 2025;13:e76573

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

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

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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