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

• Published in: Frontiers in public health Vol. 11; p. 1178491
• Main Authors: Velmovitsky, Pedro Elkind, Lotto, Matheus, Alencar, Paulo, Leatherdale, Scott T, Cowan, Donald, Morita, Plinio Pelegrini
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 05-07-2023
• Subjects: Cardiovascular Diseases; ECG; Electrocardiography; heart rate; Heart Rate - physiology; Humans; Hypertension; mHealth; mobile; Public Health; stress; wearable; Wearable Electronic Devices; stress; heart rate; wearable; Apple Watch; ECG; mHealth; mobile
• ISSN: 2296-2565; 2296-2565
• DOI: 10.3389/fpubh.2023.1178491

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 (
 < 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.