Abstract:

Cardiovascular diseases (CVDs) remain a leading cause of morbidity and mortality worldwide. Early detection of heart rhythm abnormalities and stress levels can significantly improve patient outcomes. This study leverages continuous monitoring via smartwatches and an AI-powered mobile application to facilitate early detection of both cardiovascular anomalies and stress levels. We developed advanced data science and forecasting algorithms utilizing machine learning and statistical analysis to identify potential deviations from normative physiological patterns. Over a 28-day period, continuous data were collected from wearable devices, analyzing heart rate (HR), stress levels, and additional physiological parameters in a participant cohort. Our findings demonstrate significant correlations (the correlation coefficient is greater than 0.83) between heart rate and stress levels, with Poincaré plot analysis providing valuable insights into heart rhythm disturbances (SD1/SD2 ratios). Furthermore, we present an innovative application of this technology for predicting opioid cravings, highlighting its broader health monitoring capabilities. The Behaivior AI Recovery™ application, compatible with various sensor devices, including smartwatches, offers a practical solution for real-world health monitoring in clinical and home settings, enabling early intervention and improved patient outcomes. This research underscores the transformative potential of integrating AI and wearable technology in healthcare, emphasizing the importance of continuous and remote patient monitoring.