Can heart rate variability data from the Apple Watch electrocardiogram quantify stress?

Frontiers in Public Health

Public Health

Donald Cowan¹  Paulo Alencar¹  Scott T. Leatherdale²  Pedro Elkind Velmovitsky²  Matheus Lotto³  Plinio Pelegrini Morita⁴ 

[1] David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada;School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada;School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada;Department of Pediatric Dentistry, Orthodontics and Public Health, Bauru School of Dentistry, University of São, Paulo, Bauru, Brazil;School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada;Research Institute for Aging, University of Waterloo, Waterloo, ON, Canada;Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada;Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada;Centre for Digital Therapeutics, Techna Institute, University Health Network, Toronto, ON, Canada;

关键词: mHealth;    stress;    heart rate;    mobile;    wearable;    ECG;    Apple Watch;   

DOI  :  10.3389/fpubh.2023.1178491

 received in 2023-03-02, accepted in 2023-06-09,  发布年份 2023

来源: Frontiers

【 摘 要 】

Chronic stress has become an epidemic with negative health risks including cardiovascular disease, hypertension, and diabetes. Traditional methods of stress measurement and monitoring typically relies on self-reporting. However, wearable smart technologies offer a novel strategy to continuously and non-invasively collect objective health data in the real-world. A novel electrocardiogram (ECG) feature has recently been introduced to the Apple Watch device. Interestingly, ECG data can be used to derive Heart Rate Variability (HRV) features commonly used in the identification of stress, suggesting that the Apple Watch ECG app could potentially be utilized as a simple, cost-effective, and minimally invasive tool to monitor individual stress levels. Here we collected ECG data using the Apple Watch from 36 health participants during their daily routines. Heart rate variability (HRV) features from the ECG were extracted and analyzed against self-reported stress questionnaires based on the DASS-21 questionnaire and a single-item LIKERT-type scale. Repeated measures ANOVA tests did not find any statistical significance. Spearman correlation found very weak correlations (p < 0.05) between several HRV features and each questionnaire. The results indicate that the Apple Watch ECG cannot be used for quantifying stress with traditional statistical methods, although future directions of research (e.g., use of additional parameters and Machine Learning) could potentially improve stress quantification with the device.

【 授权许可】

Unknown   
Copyright © 2023 Velmovitsky, Lotto, Alencar, Leatherdale, Cowan and Morita.