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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

Nonadherence to Diabetes Complications Screening in a Multiethnic Asian Population: Protocol for a Mixed Methods Prospective Study

Nonadherence to Diabetes Complications Screening in a Multiethnic Asian Population: Protocol for a Mixed Methods Prospective Study

Each study participant will receive a reimbursement of S $20 (US $15.29) for each screening type for which they are recruited as part of the study. For example, a participant who completes baseline assessments for attending DR and DN screening will receive a total of S $40 (US $30.57) worth of vouchers. Similarly, the participant will receive a total of S $40 (US $30.57) worth of vouchers upon completing the respective follow-up assessments.

Amudha Aravindhan, Eva Fenwick, Aurora Wing Dan Chan, Ryan Eyn Kidd Man, Wern Ee Tang, Ngiap Chuan Tan, Charumathi Sabanayagam, Junxing Chay, Lok Pui Ng, Wei Teen Wong, Wern Fern Soo, Shin Wei Lim, Ecosse L Lamoureux

JMIR Res Protoc 2025;14:e63253

Co-Designing, Developing, and Testing a Mental Health Platform for Young People Using a Participatory Design Methodology in Colombia: Mixed Methods Study

Co-Designing, Developing, and Testing a Mental Health Platform for Young People Using a Participatory Design Methodology in Colombia: Mixed Methods Study

Engagement remains a key area for future R&D to address the substantial drop-off after initial use. While our user engagement rates were better (longer periods of engagement) than those reported in previous research, possibly due to our thorough co-design process tailored to users’ needs and preferences, achieving long-term engagement requires sustained efforts and innovative strategies to keep users motivated and invested over time.

Laura Ospina-Pinillos, Débora L Shambo-Rodríguez, Mónica Natalí Sánchez-Nítola, Alexandra Morales, Laura C Gallego-Sanchez, María Isabel Riaño-Fonseca, Andrea Carolina Bello-Tocancipá, Alvaro Navarro-Mancilla, Jaime A Pavlich-Mariscal, Alexandra Pomares-Quimbaya, Carlos Gómez-Restrepo, Ian B Hickie, Jo-An Occhipinti

JMIR Hum Factors 2025;12:e66558