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Influence of Virtual Reality Illusions on Balance Performance and Immersive User Experience in Young Adults: A Within-Subject Experimental Study

Influence of Virtual Reality Illusions on Balance Performance and Immersive User Experience in Young Adults: A Within-Subject Experimental Study

(C) Absolute mean anteroposterior center of pressure displacement comparison in gameplay stages. (D) Absolute mean anteroposterior center of pressure displacement comparison across different types of virtual illusions ( NS: P > 0.05, *P< 0.05, **P < 0.01, ***P < 0.001). Figure 5 shows the comparisons of maximum absolute Co P displacement.

R Achintha M Abayasiri, Antonio Padilha Lanari Bo, Taylor J M Dick, Nilufar Baghaei

JMIR Serious Games 2025;13:e70376

Research Dissemination Strategies in Pediatric Emergency Care Using a Professional Twitter (X) Account: A Mixed Methods Developmental Study of a Logic Model Framework

Research Dissemination Strategies in Pediatric Emergency Care Using a Professional Twitter (X) Account: A Mixed Methods Developmental Study of a Logic Model Framework

development (R) Acquisition of external graphics designer (G) Selection of software and metrics (J) Learning to work with existing technologies for scheduling and publishing (AF) Sudden unexpected team member change (U) Outputs Extracting key teaching points from top PECARN articles into tweet (X) Tweet frequency established at the same time each week (3 times per week) (AH) Tweet visual structure: Intentional or strategic polls, emojis, bullet points, and multimedia such as images and videos (AA, AB, AC, C,

Gwendolyn C Hooley, Julia N Magana, Jason M Woods, Shyam Sivasankar, Lauren VonHoltz, Anita R Schmidt, Todd P Chang, Michelle Lin

JMIR Form Res 2025;9:e59481

The Rapid Online Cognitive Assessment for the Detection of Neurocognitive Disorder: Open-Label Study

The Rapid Online Cognitive Assessment for the Detection of Neurocognitive Disorder: Open-Label Study

We first evaluated how Ro CA evaluated patient drawings (Figure 3 A-C). Ro CA classified 97% (44/46) of the cubes correctly, 91% (42/46) of the infinities correctly, and 98% (45/46) of the clocks correctly. We next calculated the accuracy of Ro CA for each drawing individually (Figure 3 D). We compared the accuracy of each drawing to its statistical baseline by bootstrapping, resampling the accuracy, and counting the number of times it fell below the random classifier.

Calvin Howard, Amy Johnson, Joseph Peedicail, Marcus C Ng

J Med Internet Res 2025;27:e66735

Use, Usability, and Experience Testing of a Digital Health Intervention to Support Chronic Kidney Disease Self-Management: Mixed Methods Study

Use, Usability, and Experience Testing of a Digital Health Intervention to Support Chronic Kidney Disease Self-Management: Mixed Methods Study

Self-Management Intervention through Lifestyle Education for Kidney health (SMILE-K) trial intervention group participant characteristics for those assigned to the intervention group and those who activated their My Kidneys & Me (MK&M) account. ae GFR: estimated glomerular filtration rate. b ACR: albumin to creatinine ratio. c Hb A1c: hemoglobin A1c. d CRP: C-reactive protein. e PAM-13: Patient Activation Measure-13.

Courtney J Lightfoot, Thomas J Wilkinson, Roseanne E Billany, Gurneet K Sohansoha, Noemi Vadaszy, Ella C Ford, Melanie J Davies, Thomas Yates, Alice C Smith, Matthew P M Graham-Brown

J Med Internet Res 2025;27:e75845

Development of Machine Learning–Based Risk Prediction Models to Predict Rapid Weight Gain in Infants: Analysis of Seven Cohorts

Development of Machine Learning–Based Risk Prediction Models to Predict Rapid Weight Gain in Infants: Analysis of Seven Cohorts

(C) Model 3 included model 2 predictors plus solids introduction (yes or no) at age 6 months. AUC: area under the receiver operating characteristic curve; MLP: multilayer perceptron; SVC: support vector classifier. Precision recall curves (PRC) of 8 machine learning algorithms in predicting infants at risk of rapid weight gain by the age of 1 year in the training dataset (internal validation).

Miaobing Zheng, Yuxin Zhang, Rachel A Laws, Peter Vuillermin, Jodie Dodd, Li Ming Wen, Louise A Baur, Rachael Taylor, Rebecca Byrne, Anne-Louise Ponsonby, Kylie D Hesketh

JMIR Public Health Surveill 2025;11:e69220