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. 2023 Mar 23;4(3):155-164.
doi: 10.1093/ehjdh/ztad022. eCollection 2023 May.

Smartwatch-derived heart rate variability: a head-to-head comparison with the gold standard in cardiovascular disease

Fabian Theurl  1 , Michael Schreinlechner  1 , Nikolay Sappler  1 , Michael Toifl  1 , Theresa Dolejsi  1 , Florian Hofer  1 , Celine Massmann  1 , Christian Steinbring  1 , Silvia Komarek  2   3 , Kurt Mölgg  2   3 , Benjamin Dejakum  2   3 , Christian Böhme  2 , Rudolf Kirchmair  1 , Sebastian Reinstadler  1 , Axel Bauer  1
Affiliations

Smartwatch-derived heart rate variability: a head-to-head comparison with the gold standard in cardiovascular disease

Fabian Theurl et al. Eur Heart J Digit Health. .

Abstract

Aims: We aimed to investigate the concordance between heart rate variability (HRV) derived from the photoplethysmographic (PPG) signal of a commercially available smartwatch compared with the gold-standard high-resolution electrocardiogram (ECG)-derived HRV in patients with cardiovascular disease.

Methods and results: We prospectively enrolled 104 survivors of acute ST-elevation myocardial infarction, 129 patients after an ischaemic stroke, and 30 controls. All subjects underwent simultaneous recording of a smartwatch (Garmin vivoactive 4; Garmin Ltd, Olathe, KS, USA)-derived PPG signal and a high-resolution (1000 Hz) ECG for 30 min under standardized conditions. HRV measures in time and frequency domain, non-linear measures, as well as deceleration capacity (DC) were calculated according to previously published technologies from both signals. Lin's concordance correlation coefficient (ρc) between smartwatch-derived and ECG-based HRV markers was used as a measure of diagnostic accuracy. A very high concordance within the whole study cohort was observed for the mean heart rate (ρc = 0.9998), standard deviation of the averages of normal-to-normal (NN) intervals in all 5min segments (SDANN; ρc = 0.9617), and very low frequency power (VLF power; ρc = 0.9613). In contrast, detrended fluctuation analysis (DF-α1; ρc = 0.5919) and the square mean root of the sum of squares of adjacent NN-interval differences (rMSSD; ρc = 0.6617) showed only moderate concordance.

Conclusion: Smartwatch-derived HRV provides a practical alternative with excellent accuracy compared with ECG-based HRV for global markers and those characterizing lower frequency components. However, caution is warranted with HRV markers that predominantly assess short-term variability.

Keywords: Deceleration capacity; Heart rate variability; Smartwatch; Wearables.

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Conflict of interest statement

Conflict of interest: None declared.

Figures

Graphical Abstract
Graphical Abstract
Figure 1
Figure 1
Consort diagram.
Figure 2
Figure 2
Agreement between ECG-derived and smartwatch-derived HRV metrics.
Figure 3
Figure 3
Bland–Altman plots with calculated bias (dashed black line) and limits of agreement (dashed grey lines) for mean heart rate (A), DCt1 (B), DCt4 (C), SDNN (D), SDANN (E), HRVi (F), rMSSD (G), VLF power (H), LF power (I), HF power (J), SD1 (K), SD2 (L), DF-α1 (M), and DF-α2 (N).
Figure 4
Figure 4
Concordance as a function of target frequency in HRV power spectra: concordance between ECG-derived and smartwatch-derived power content in arbitrary overlapping segments of 0.01 Hz width for all participants (A) as well as STEMI cohort (B), STROKE cohort (C), and CONTROL cohort (D) alone. r, Spearman’s rank correlation coefficient; ρc, Lin’s concordance correlation coefficient.
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