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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

Prerequisites for Cost-Effective Home Blood Pressure Telemonitoring: Early Health Economic Analysis

Prerequisites for Cost-Effective Home Blood Pressure Telemonitoring: Early Health Economic Analysis

https://www.zorginstituutnederland.nl/publicaties/publicatie/2024/01/16/richtlijn-voor-het-uitvoeren-van-economische-evaluaties-in-de-gezondheidszorg ://www.zorginstituutnederland.nl/over-ons/werkwijzen-en-procedures/adviseren-over-en-verduidelijken-van-het-basispakket-aan-zorg /beoordeling-van-geneesmiddelen/richtlijn-voor-economische-evaluatie) dashboard-arbeidsmarkt/ontwikkeling-cao-lonen/uurloon#:~:text=Als%20we%20kijken%20naar%20gemiddelden,van /Verdiepingsmodule+QALY+en+kwaliteit-van-leven-metingen+%28versie+2024%29.pdf)

Job van Steenkiste, Pim van Dorst, Daan Dohmen, Cornelis Boersma

JMIR Cardio 2025;9:e64386

Use and Acceptance of Innovative Digital Health Solutions Among Patients and Professionals: Survey Study

Use and Acceptance of Innovative Digital Health Solutions Among Patients and Professionals: Survey Study

This study was approved by the ethics committee of Klinikum rechts der Isar, Technical University of Munich, before distribution. The approval number is 2023‐146-S-NP. By answering the mandatory questions of the survey and submitting them, the participants provided their consent to the anonymous study. In case they refrained from submitting the answers or refrained from answering specific mandatory questions, their consent was denied. In these cases, data were neither submitted nor recorded in any form.

Fritz Seidl, Florian Hinterwimmer, Ferdinand Vogt, Günther M Edenharter, Karl F Braun, Rüdiger von Eisenhart-Rothe, AG Digitalisierung der DGOU, Peter Biberthaler, Dominik Pförringer

JMIR Hum Factors 2025;12:e60779

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