Search Articles

View query in Help articles search

Search Results (1 to 10 of 5583 Results)

Download search results: CSV END BibTex RIS


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

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

Effect size dz = 0.39 α err prob = 0.05 Input: Tail(s) = 2 Power (1-β err prob) = 0.80 Output: Noncentrality parameter δ = 2.8659030 Critical t= 2.0057460 Df = 53 Total sample size = 54 Actual power = 0.8033525 The principal investigator (PI) and co-investigators (telenurses) received training in telenursing such as communication and behavior modification techniques, and self-management skills on diabetes including blood sugar control and foot care.

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

JMIR Res Protoc 2025;14:e71849

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

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. The participants of the survey received no compensation 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