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Scaling a Brief Digital Well-Being Intervention (the Big Joy Project) and Sociodemographic Moderators: Single-Group Pre-Post Study

Scaling a Brief Digital Well-Being Intervention (the Big Joy Project) and Sociodemographic Moderators: Single-Group Pre-Post Study

Before and immediately after each activity, participants were asked how much they felt pleasant emotions such as delight, pride, and hope and unpleasant emotions such as distress, sadness, and anger. After each activity, they were also asked how difficult the activity was and if they thought it was a good fit for them. Each activity was designed to be brief and took between 5 and 10 minutes. For all activities, we operationalized and estimated the duration of the prompt and how long it took to finish it.

Darwin A Guevarra, Yoobin Park, Xuhai Xu, Jin Liou, Jolene Smith, Peggy Callahan, Emiliana Simon-Thomas, Elissa S Epel

J Med Internet Res 2025;27:e72053

A Comprehensive Drift-Adaptive Framework for Sustaining Model Performance in COVID-19 Detection From Dynamic Cough Audio Data: Model Development and Validation

A Comprehensive Drift-Adaptive Framework for Sustaining Model Performance in COVID-19 Detection From Dynamic Cough Audio Data: Model Development and Validation

The width of the window used was 0.96 seconds, while the window stride length was equal to half of the window’s width (0.48 s). This setting resulted in a mel spectrogram segment with a size of 64 mel bins × 96 frames. To facilitate model training, the COVID-19 Sounds and Coswara datasets were partitioned based on chronological order into a development set and a postdevelopment set by applying a 70:30 ratio.

Theofanis Ganitidis, Maria Athanasiou, Konstantinos Mitsis, Konstantia Zarkogianni, Konstantina S Nikita

J Med Internet Res 2025;27:e66919

Artificial Intelligence in Patch Testing: Comprehensive Review of Current Applications and Future Prospects in Dermatology

Artificial Intelligence in Patch Testing: Comprehensive Review of Current Applications and Future Prospects in Dermatology

By synthesizing the available literature, we hope to provide a comprehensive overview of the state of AI in patch testing and how AI can be leveraged to improve patch testing practices and diagnostic accuracy of ACD in the future. A comprehensive literature search was performed in August 2024 using the Pub Med database. The search was conducted without date restrictions to capture the full scope of research in this emerging field.

Hilary S Tang, Joseph Ebriani, Matthew J Yan, Shannon Wongvibulsin, Mehdi Farshchian

JMIR Dermatol 2025;8:e67154