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

Autor:	van Es VAA; Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.; Department of Built Environment, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands., De Lathauwer ILJ; Department of Cardiology, Máxima Medical Centre, 5504 DB Veldhoven, The Netherlands.; Department of Industrial Design, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands., Lopata RGP; Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands., Kemperman ADAM; Department of Built Environment, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands., van Dongen RP; Department of Built Environment, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands., Brouwers RWM; Department of Industrial Design, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands., Funk M; Department of Industrial Design, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands., Kemps HMC; Department of Cardiology, Máxima Medical Centre, 5504 DB Veldhoven, The Netherlands.; Department of Industrial Design, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
Jazyk:	angličtina
Zdroj:	European heart journal. Digital health [Eur Heart J Digit Health] 2024 Jul 12; Vol. 5 (5), pp. 551-562. Date of Electronic Publication: 2024 Jul 12 (Print Publication: 2024).
DOI:	10.1093/ehjdh/ztae050
Abstrakt:	Aims: Urbanization is related to non-communicable diseases such as 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 responses to environmental attributes in uncontrolled real-world settings. The goal is to validate whether this approach can ascertain and quantify the connection between environmental attributes and cardiac autonomic response in patients with CHF. Methods and Results: A total of 20 participants (10 healthy individuals and 10 patients with CHF) wore smartwatches for 3 weeks, recording activities, locations, and heart rate (HR). Environmental attributes were extracted from Google Street View images. Machine learning models were trained and tested on the data to predict HRV metrics. The models were evaluated using Spearman's correlation, root mean square error, prediction intervals, and Bland-Altman analysis. Machine learning models predicted HRV metrics related to vagal activity well ( R > 0.8 for HR; 0.8 > R > 0.5 for the root mean square of successive interbeat interval differences and the Poincaré plot standard deviation perpendicular to the line of identity; 0.5 > R > 0.4 for the high frequency power and the ratio of the absolute low- and high frequency power 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. Competing Interests: Conflict of interest: none declared. (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)
Databáze:	MEDLINE
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