  This paper explores the challenges in evaluating machine learning (ML) models
for continuous health monitoring using wearable devices beyond conventional
metrics. We state the complexities posed by real-world variability, disease
dynamics, user-specific characteristics, and the prevalence of false
notifications, necessitating novel evaluation strategies. Drawing insights from
large-scale heart studies, the paper offers a comprehensive guideline for
robust ML model evaluation on continuous health monitoring.
