<p>There is growing motivation to exploit computational biomechanical modeling of the heart as a predictive tool to support clinical diagnoses and therapies. Existing patient-specific cardiac models often rely on data collected under highly standardized conditions in hospitals. However, disease progression and therapy responses often depend on stressors, encountered in daily life, that cannot be captured in a traditional clinical setting. To achieve clinical translation, existing modeling frameworks must be refined and extended to include such influences. The “digital twin” concept, in which models of specific systems are continually updated with new data, is a promising avenue for integrating and interpreting these data streams. However, this endeavor calls for novel approaches to model development and data acquisition and integration. We review modeling approaches addressing specific stressor types (caffeine, exercise, sex-dependent factors, sleep, the environment) to identify knowledge gaps, assess emerging technical challenges, and suggest potential model developments to extend the scope and reach of biomedical cardiac simulations.</p> Graphical abstract <p></p>