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A Resource-Efficient, High-Dose, Gamified Neurorehabilitation Program for Chronic Stroke at Home: Retrospective Real-World Analysis

A Resource-Efficient, High-Dose, Gamified Neurorehabilitation Program for Chronic Stroke at Home: Retrospective Real-World Analysis

The target dose was consistent with a previous large telerehabilitation study that showed significant improvements in upper-limb impairment (42 h [29]). In contrast, training in this program was not limited to one body area [30-32] and could be achieved with multiple effectors (ie, upper-limb, hand, trunk, and lower-limb). Training dose was delivered via synchronous telerehabilitation and asynchronous training (Table 2).

Spencer A Arbuckle, Anna Sophie Knill, Michelle H Chan-Cortés, Gabriela Rozanski, Anastasia Elena Ford, Louis T Derungs, John W Krakauer, Naveed Ejaz, David Putrino, Jenna Tosto-Mancuso, Meret Branscheidt

JMIR Serious Games 2025;13:e69335

Effect of Minimal Individual or Group Enhancement in an eHealth Program on Mental Health, Health Behavior, and Work Ability in Employees With Obesity: Randomized Controlled Trial

Effect of Minimal Individual or Group Enhancement in an eHealth Program on Mental Health, Health Behavior, and Work Ability in Employees With Obesity: Randomized Controlled Trial

Participants received the HWC e Health program (as detailed in item 1) and participated in 3 remotely facilitated group meetings via video (each lasting 2 h). Due to the COVID-19 pandemic, these meetings were held remotely and included brief introductions to topics, such as goal setting, stress management, and self-compassion, followed by small-group discussions (Table S2 in Multimedia Appendix 1). Group meetings were held at 1, 6, and 10 months during the 12-month treatment period.

Siniriikka A Männistö, Joona Muotka, Laura-Unnukka Suojanen, Raimo Lappalainen, Kirsi H Pietiläinen, Riitta Korpela

JMIR Ment Health 2025;12:e66518

The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review

The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review

RF AUROCh=0.691 (95% CI 0.671-0.711) DNN AUROC=0.691 (95% CI 0.671-0.712) Ada Boost AUROC=0.653 (95% CI 0.632-0.674) AUROC=0.824 (95% CI 0.814-0.834) AUROC=0.720 C-index: 0.788 (compared to 0.730 for German Vasc Score) Sensitivity=50% Specificity=90% AUROC=0.88-0.90 AUROC of 0.88, 0.84, and 0.83 for sepsis onset and 24 and 48 h before onset, respectively AUROC=0.74 Specificity=98.7% RF AUROC=0.742 SVM AUROC=0.675 XGBoost AUROC=0.745 LR AUROC=0.669 AUC of 0.67, 0.65, 0.78, and 0.73 for per‐patient, LADt, LCxu,

Norah Hamad Alhumaidi, Doni Dermawan, Hanin Farhana Kamaruzaman, Nasser Alotaiq

JMIR Med Inform 2025;13:e68898