Effect of Urban Environment on Cardiovascular Health - A Feasibility Pilot Study Using Machine Learning to Predict Heart Rate Variability in Patients With Heart Failure

Aug 5, 2026

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Urbanization is associated with non-communicable diseases, including congestive heart failure, and may shape autonomic function measurable through heart rate variability (HRV). This feasibility pilot study evaluates whether machine learning models can predict HRV metrics from environmental attributes in real-world settings. Twenty participants wore smartwatches for three weeks while environmental factors were derived from Google Street View imagery. Models were assessed with correlation, error metrics, prediction intervals, and Bland–Altman agreement, showing stronger performance for vagal-related HRV than for overall autonomic activity.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Aims

Methods and results

Participants and data collection

Environmental attributes and model evaluation

Conclusion

Effect of urban environment on cardiovascular health Citation for published version (APA): van Es, V. A. A. , de Lathauwer, I. L. J. , Lopata, R. G. P. , Kemperman, A. , van Dongen, R. P. , Brouwers, R. W. M. , Funk, M. , & Kemps, H. M. C. (2024) . Effect of urban environment on cardiovascular health: a feasibility pilot study using machine learning to predict heart rate variability in patients with heart failure. European Heart Journal-Digital Health, 5(5), 551-562 . Article ztae050 . [https://doi.org/10.1093/ehjdh/ztae050](https://doi.org/10.1093/ehjdh/ztae050) Document license: CC BY DOI: 10.1093/ehjdh/ztae050 Document status and date: Published: 01/09/2024 Document Version: Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement: [www.tue.nl/taverne](www.tue.nl/taverne) Take down policy If you believe that this document breaches copyright please contact us at: [openaccess@tue.nl](openaccess@tue.nl) providing details and we will investigate your claim. Download date: 13. Mar. 2025 European Heart Journal-Digital Health (2024) 5, 551–562 ORIGINAL ARTICLE [https://doi.org/10.1093/ehjdh/ztae050](https://doi.org/10.1093/ehjdh/ztae050 Artificial intelligence)[ Artificial intelligence](https://doi.org/10.1093/ehjdh/ztae050 Artificial intelligence) (machine learning, deep learning) Effect of urban environment on cardiovascular health: a feasibility pilot study using machine learning to predict heart rate variability inpatients with heart failure Valerie A.A. van Es1,2, Ignace L.J. De Lathauwer 3,4,*, Richard G. P. Lopata1, Astrid D.A. M. Kemperman2, Robert P. van Dongen2, Rutger W. M. Brouwers4, Mathias Funk4, and Hareld M.C. Kemps3,4 1Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; 2Department of Built Environment, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; 3Department of Cardiology, Máxima Medical Centre, 5504 DB Veldhoven, The Netherlands; and 4Department of Industrial Design, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands Received 15 February 2024; revised 26 April 2024; accepted 30 June 2024; online publish-ahead-of-print 12 July 2024 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 ca

FAQs

What is the main aim of this feasibility pilot study?

To validate whether machine learning can ascertain and quantify the connection between environmental attributes and cardiac autonomic response, operationalized through HRV, in patients with heart failure.

How were data and environmental attributes collected?

Participants wore smartwatches for three weeks to record activities, locations, and heart rate, while environmental attributes were extracted from Google Street View images.

How did the machine learning models perform on different HRV metrics?

They predicted vagal activity-related HRV metrics well, while metrics reflecting overall autonomic activity were more challenging due to the complex balance between sympathetic and parasympathetic modulation.

Aug 5, 2026

Upload by Riley