A Speed-Dependent Assessment of E-Textile-Based Sensor Technology: Validity of the Prevayl Wearable Heart Rate Monitor.

Authors: Burgess, L.C., Armstrong, M., Beresford, L., Callaway, A.J.

Journal: Sensors (Basel)

Publication Date: 26/05/2026

Volume: 26

Issue: 11

eISSN: 1424-8220

DOI: 10.3390/s26113378

Abstract:

BACKGROUND: The use of wearable sensors to measure and monitor heart rate has exponentially grown in recent years, representing an inexpensive, time-efficient, and non-invasive method to assess the status of cardiovascular fitness and the autonomic nervous system. Validating new devices against a criterion standard, such as electrocardiography (ECG), is essential to ensure their accuracy and reliability. This study examined the accuracy and validity of the Prevayl heart rate monitor against 3-lead ECG. METHODS: Twenty-six healthy adults (15 female, mean age 32.0 ± 10.4 years) completed a 16-min, incremental running test on a treadmill. Heart rate data were recorded simultaneously throughout the test via ECG and the Prevayl wearable and compared retrospectively. Beat count error (%), mean heart rate absolute error (beats per minute (bpm)), and percentage error (bpm) were calculated. In addition, a Bland-Altman analysis and Pearson's correlation coefficient were conducted to assess agreement and correlation, respectively. RESULTS: The Prevayl device demonstrated a median beat count agreement of 100.5% with ECG (range: 98.6-104.4%; Npart = 26). Strong correlations were observed between ECG and Prevayl for both raw beat count (r = 0.94, p < 0.01) and heart rate (beats per minute (bpm)) from ECG and the Prevayl algorithm (r = 0.96, p < 0.01). Across running speeds (0-12 kph), a strong correlation was found between raw beat count from ECG and Prevayl (r = 0.82-0.89, p < 0.01) and between bpm from ECG and Prevayl (r = 0.86-0.93, p < 0.01). Bland-Altman plots demonstrated negligible systematic bias. CONCLUSIONS: The Prevayl system provides valid measurements when compared to ECG during incremental running. This is demonstrated through strong correlations to ECG heart rate data at different speeds and with different analysis methods, supporting its use for monitoring cardiovascular responses during exercise.

https://eprints.bournemouth.ac.uk/42044/

Source: PubMed