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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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Telenursing Health Education and Lifestyle Modification Among Patients With Diabetes in Bangladesh: Protocol for a Pilot Study With a Quasi-experimental Pre- and Postintervention Design

Telenursing Health Education and Lifestyle Modification Among Patients With Diabetes in Bangladesh: Protocol for a Pilot Study With a Quasi-experimental Pre- and Postintervention Design

We calculated the sample size using G*Power software (version 3.1.9.7; Heinrich-Heine-University). As this is a pilot study, we set an effect size of 0.39 considering the differences between 2 dependent matched pair means of Hb A1c from a previous study [19], with a level of significance of 5%, and 80% power, the total sample size is calculated at 54.

Michiko Moriyama, K A T M Ehsanul Huq, Lucy Mondol, Akhi Roy Mita, Niru Shamsun Nahar

JMIR Res Protoc 2025;14:e71849

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Real-World Effectiveness of Glucose-Guided Eating Using the Data-Driven Fasting App Among Adults Interested in Weight and Glucose Management: Observational Study

Real-World Effectiveness of Glucose-Guided Eating Using the Data-Driven Fasting App Among Adults Interested in Weight and Glucose Management: Observational Study

Those with only 1 day of entries (n=322) were excluded from the analysis and were similar to the analytical sample in terms of baseline weight (P=.08) and fasting glucose (P=.41) but had a lower BMI (24.5kg/m², P=.01) and included significantly more men and nonbinary individuals (P The app was used by the analytical sample for a median of 19 (IQR 9-28) days, with 7 (IQR 3-13) weight entries and 52 (IQR 25-82) glucose entries, which were primarily preprandial glucose entries (Table 2).

Michelle R Jospe, Martin Kendall, Susan M Schembre, Melyssa Roy

JMIR Form Res 2025;9:e65368

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