Abstract

The aim was to examine the validity of heart rate variability (HRV) measurements from photoplethysmography (PPG) via a smartphone application pre- and post-resistance exercise (RE) and to examine the intraday and interday reliability of the smartphone PPG method. Thirty-one adults underwent two simultaneous ultrashort-term electrocardiograph (ECG) and PPG measurements followed by 1-repetition maximum testing for back squats, bench presses, and bent-over rows. The participants then performed RE, where simultaneous ultrashort-term ECG and PPG measurements were taken: two pre- and one post-exercise. The natural logarithm of the root mean square of successive normal-to-normal (R-R) differences (LnRMSSD) values were compared with paired-sample t-tests, Pearson product correlations, Cohen’s d effect sizes (ESs), and Bland–Altman analysis. Intra-class correlations (ICC) were determined between PPG LnRMSSDs. Significant, small–moderate differences were found for all measurements between ECG and PPG: BasePre1 (ES = 0.42), BasePre2 (0.30), REPre1 (0.26), REPre2 (0.36), and REPost (1.14). The correlations ranged from moderate to very large: BasePre1 (r = 0.59), BasePre2 (r = 0.63), REPre1 (r = 0.63), REPre2 (r = 0.76), and REPost (r = 0.41)—all p < 0.05. The agreement for all the measurements was “moderate” (0.10–0.16). The PPG LnRMSSD exhibited “nearly-perfect” intraday reliability (ICC = 0.91) and “very large” interday reliability (0.88). The smartphone PPG was comparable to the ECG for measuring HRV at rest, but with larger error after resistance exercise.

Highlights

  • Advances in technology have produced a plethora of commercially available mobile devices that can measure health and fitness outcomes

  • Heart rate variability is defined as the oscillations that occur between successive heartbeats and is considered a non-invasive marker of autonomic nervous system (ANS) control of the cardiovascular

  • The logarithmic transformation of the RMSSD (LnRMSSD) values of both the PPG and ECG as well as validity statistics are displayed in values were consistent with all the resting measurements, with the CE values falling within the upper and lower ranges of the limits of agreement

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Summary

Introduction

Advances in technology have produced a plethora of commercially available mobile devices that can measure health and fitness outcomes. These mobile devices have seen an expansive increase in use in medicine, healthcare and the military but most notably through personal use by athletes and active individuals of the general population. Of the new methods and equipment currently available, heart rate variability (HRV) assessment has emerged as one of the more useful and practically applied tools. Heart rate variability is defined as the oscillations that occur between successive heartbeats and is considered a non-invasive marker of autonomic nervous system (ANS) control of the cardiovascular

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