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Balance Improvement and Fall Risk Reduction in Stroke Survivors After Treatment With a Wearable Home-Use Gait Device: Single-Arm Longitudinal Study With 1-Year Follow-Up

Brianne Darcy, Lauren Rashford, David Huizenga, Kyle B Reed, Stacy J M Bamberg

JMIR Form Res 2025;9:e67297

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The Effectiveness of Online Exercise on Physical Activity, Motor Function, and Mental Health: Systematic Review and Meta-Analysis

Adelle Kemlall Bhundoo, Julian David Pillay, Jan Wilke

J Med Internet Res 2025;27:e64856

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Patient-Centered Risk Prediction, Prevention, and Intervention Platform (TIMELY) to Support the Continuum of Care in Coronary Artery Disease Using eHealth and Artificial Intelligence: Protocol for a Randomized Controlled Trial

Mirela Habibovic, Emma Douma, Hendrik Schäfer, Manuela Sestayo-Fernandez, Tom Roovers, Xin Sun, Henrik Schmidt, Mona Kotewitsch, Jos Widdershoven, David Cantarero-Prieto, Frank Mooren, Carlos Pena-Gil, José Rámon González Juanatey, Martin Schmidt, Hagen Malberg, Vassilis Tsakanikas, Dimitrios Fotiadis, Dimitris Gatsios, Jos Bosch, Willem Johan Kop, Boris Schmitz

JMIR Res Protoc 2025;14:e66283

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Comparison of Sleep Features Across Smartphone Sensors, Actigraphy, and Diaries Among Young Adults: Longitudinal Observational Study

Jaclyn S Kirshenbaum, Ryann N Crowley, Melissa D Latham, David Pagliaccio, Randy P Auerbach, Nicholas B Allen

JMIR Form Res 2025;9:e67455

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Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review

Lavanya Vasudevan, Mohammad Golam Kibria, Lauren M Kucirka, Karl Shieh, Mian Wei, Safoora Masoumi, Subha Balasubramanian, Ashley Victor, Jamie L Conklin, Metin Nafi Gurcan, Alison M Stuebe, David Page

J Med Internet Res 2025;27:e68225

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Experiences and Perceptions About Death Reporting and Notification Among Rural Communities on the Islands of Lake Victoria, Uganda: Qualitative Study

Steven Ndugwa Kabwama, Douglas Bulafu, Rawlance Ndejjo, Rose Nampeera, Christine Kihembo, Chukwuma David Umeokonkwo, Simon Antara, Sheba Nakacubo Gitta, Caroline Kyozira, Allan Muruta, Jean-Edgard Nguessan, Rhoda K Wanyenze

JMIR Public Health Surveill 2025;11:e77135

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Integrating Food Preference Profiling, Behavior Change Strategies, and Machine Learning for Cardiovascular Disease Prevention in a Personalized Nutrition Digital Health Intervention: Conceptual Pipeline Development and Proof-of-Principle Study

The analysis identified 14 food items with sufficient classification power for FPPs: tea with sugar, vegetables, roast chicken, ham, tomatoes, strawberries, green olives, banana, sweet coffee drinks, coffee with sugar, fruit, mushroom, spinach, and potatoes. Each box in the decision tree represents a node. The top number in each box is the node number. The label inside the box includes two rows: the predicted class for that node (first row) and the distribution of samples across the tree (second row).

Hana Fitria Navratilova, Anthony David Whetton, Nophar Geifman

J Med Internet Res 2025;27:e75106

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