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Accuracy of Smartphone-Mediated Snore Detection in a Simulated Real-World Setting: Algorithm Development and Validation

Accuracy of Smartphone-Mediated Snore Detection in a Simulated Real-World Setting: Algorithm Development and Validation

Analytical validation reference table showing the schematic and equationsa,b,c,d,e used to report snore detection performance of the Sleep Watch app in this study. a Accuracy = (true positive + true negative)/(true positive + true negative + false positive + false negative). b Sensitivity = true positive/(true positive + false negative). c Specificity = true negative/(false positive + true negative). d Positive predictive value = true positive/(true positive + false positive). e Negative predictive value = true negative

Jeffrey Brown, Zachary Mitchell, Yu Albert Jiang, Ryan Archdeacon

JMIR Form Res 2025;9:e67861

Acceptability of a Web-Based Health App (PortfolioDiet.app) to Translate a Nutrition Therapy for Cardiovascular Disease in High-Risk Adults: Mixed Methods Randomized Ancillary Pilot Study

Acceptability of a Web-Based Health App (PortfolioDiet.app) to Translate a Nutrition Therapy for Cardiovascular Disease in High-Risk Adults: Mixed Methods Randomized Ancillary Pilot Study

Several recent Canadian population-based studies have shown that many patients at high CVD risk continue to have low-density lipoprotein cholesterol (LDL-C) levels well above the guideline targets [2,3]. LDL-C has been extensively studied and described as a causal factor for CVD [4]. LDL-C levels above the target can result from multiple factors such as insufficient LDL-C lowering with statins, statin-related side effects, suboptimal medication adherence, and treatment inertia [5].

Meaghan E Kavanagh, Laura Chiavaroli, Selina M Quibrantar, Gabrielle Viscardi, Kimberly Ramboanga, Natalie Amlin, Melanie Paquette, Sandhya Sahye-Pudaruth, Darshna Patel, Shannan M Grant, Andrea J Glenn, Sabrina Ayoub-Charette, Andreea Zurbau, Robert G Josse, Vasanti S Malik, Cyril W C Kendall, David J A Jenkins, John L Sievenpiper

JMIR Cardio 2025;9:e58124

Biases in Race and Ethnicity Introduced by Filtering Electronic Health Records for “Complete Data”: Observational Clinical Data Analysis

Biases in Race and Ethnicity Introduced by Filtering Electronic Health Records for “Complete Data”: Observational Clinical Data Analysis

Available percentage of patients’ data upon individually applying all 19 filters in different ethnic subgroups in (A) the Cedars-Sinai dataset, (B) the CUIMC dataset, and (C) the Ao U dataset. The filters are in descending order following the available percentage of the category, all. The points are connected to ease the visualization, but the filters are not cumulative. Stacked bar plots show the ethnicity distribution of the datasets in percentages.

Jose Miguel Acitores Cortina, Yasaman Fatapour, Kathleen LaRow Brown, Undina Gisladottir, Michael Zietz, Oliver John Bear Don't Walk IV, Danner Peter, Jacob S Berkowitz, Nadine A Friedrich, Sophia Kivelson, Aditi Kuchi, Hongyu Liu, Apoorva Srinivasan, Kevin K Tsang, Nicholas P Tatonetti

JMIR Med Inform 2025;13:e67591