Effect of urban environment on cardiovascular health: a feasibility pilot study using machine learning to predict heart rate variability in patients with heart failure

Abstract

Aims Urbanization is related to non-communicable diseases like congestive heart failure (CHF). Understanding the influence of diverse living environments on physiological variables such as heart rate variability (HRV) in patients with chronic cardiac disease may contribute to more effective lifestyle advice and telerehabilitation strategies. This study explores how machine learning (ML) models can predict HRV metrics, which measure autonomic nervous system (ANS) responses to environmental attributes in uncontrolled real-world settings. The goal is to validate if this approach can ascertain and quantify the connection between environmental attributes and cardiac autonomic response in CHF patients. Methods 20 participants (10 healthy, 10 CHF) wore smartwatches for 3 weeks, recording activities, locations, and HR. Environmental attributes were extracted from Google Street view images. ML models were trained and tested on the data to predict HRV metrics. The models were evaluated using Spearman’s correlation, RMSE, prediction intervals, and Bland-Altman analysis. Results ML models predicted HRV metrics related to vagal activity well (R > 0.8 for HR; 0.8 > R > 0.5 for RMSSD and SD1; 0.5 > R > 0.4 for HF and LF/HF) induced by environmental attributes. However, they struggled with metrics related to overall autonomic activity, due to the complex balance between sympathetic and parasympathetic modulation. Conclusion This study highlights the potential of ML-based models to discern vagal dynamics influenced by living environments in healthy individuals and patients diagnosed with CHF. Ultimately, this strategy could offer rehabilitation and tailored lifestyle advice, leading to improved prognosis and enhanced overall patient well-being in CHF.