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Portfolio Diet.app dashboard with key features highlighted, top to bottom: (A) learn tab that has recipes, tipsheets, and videos; (B) daily average Portfolio Diet Score per month; (C) star rewards, a form of reward for logging into the app and for completing the question of the week; (D) current day total Portfolio Diet Score; (E) specific daily messages related to goals; (F) personal favorite meals for easy tracking; (G) subcategory Portfolio Diet Scores with daily targets of 5 points; (H) progress-tracking
JMIR Cardio 2025;9:e58124
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Sample size calculation was undertaken using procedures provided by the software program G*Power 3 [37]. As per our statistical consultant, the calculation was undertaken to determine adequate power in the determination of the potential effects of the intervention (aim 3), in which a repeated-measures ANOVA with a within-between interaction would be the statistical test used.
JMIR Res Protoc 2025;14:e55475
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All models were trained with the mini-batch stochastic gradient descent algorithm using the Adam optimizer. A batch size of 8 was selected. The learning rate was 0.0001. The training was done for 200 epochs. The values of hyperparameters were empirically tuned for best performance. Categorical cross-entropy (CSE) was selected as the loss function for this study [26]. The CSE loss function minimizes the distance between 2 distributions (the predicted labels and the GT labels).
JMIR Aging 2025;8:e63686
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