The Accuracy of Acquiring Heart Rate Variability from Portable Devices: A Systematic Review and Meta-Analysis

Dobbs, Ward C; Fedewa, Michael V.; MacDonald, Hayley V.; Holmes, Clifton J.; Cicone, Zackary S.; Plews, Daniel J.; Esco, Michael R.

doi:10.1007/s40279-019-01061-5

Cited by 117 publications

(100 citation statements)

References 73 publications

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“…The assessment of HRV has been utilized by athletes to indirectly examine ANS status in response to various types of training [ 3 , 4 ] and has been demonstrated as a valid tool for monitoring fatigue accumulation and recovery [ 5 , 6 ]. The majority of HRV research occurs in controlled laboratory settings with sophisticated equipment, such as an electrocardiograms (ECGs) [ 7 ]. The criterion method of ECG for acquiring HRV data requires 10 min short-term measures consisting of a 5 min stabilization period followed by a 5 min recording period [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…Technological advances have allowed HRV to be collected with mobile devices. A recent meta-analysis consisting of twenty-three studies concluded that portable devices yielded small but acceptable ranges of error in comparison to ECG (effect size (ES) = 0.23, 95% CI: 0.05, 0.42) [ 7 ]. Photoplethysmography (PPG) has been proposed as a surrogate for traditional ECG for the measurement of HRV [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Validity of Smartphone Heart Rate Variability Pre- and Post-Resistance Exercise

Holmes

Fedewa

Winchester

et al. 2020

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validity of Smartphone Heart Rate Variability Pre- and Post-Resistance Exercise

Holmes

Fedewa

Winchester

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

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“…However, numerous studies focus on evaluating the reproducibility and accuracy of different devices against ECG in the calculation of HRV measurements, both in the time domain and in the frequency domain, but not in the agreement of the time intervals measurement based on the correct detection of R waves. Thus, the most recent meta-analysis on this problem [11] works with 23 studies and selects a total of 20 devices and mobile applications in which it analyses the precision with which they calculate different measures in relation to the ECG.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Omegawave Device and an Ambulatory ECG for RR Interval Measurement at rest

2020

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The aim of this study was to validate the measurements of the beat intervals taken at rest by the Omegawave® device by comparing them to an ambulatory electrocardiogram system. For this purpose, the electrocardiogram was digitally processed, time-aligned, and scrutinized for its suitable use as gold-standard. Rest measurements were made for 10 minutes on 5 different days to 10 men and 3 women (24.8±5.05 years; 71.82±11.02 kg; 174.35±9.13 cm). RR intervals were simultaneously recorded using the Omegawave device and a Holter electrocardiogram. The processing of Holter electrocardiogram signals included the detrending of baseline noise and a high-pass filtering for emphasizing the QRS complexes and attenuating the T waves. After obtaining the RR intervals from the electrocardiogram, those from the Omegawave device were automatically aligned to them with cross-correlation digital processing techniques and compared to check whether both measurements could be considered superimposable. A Bland-Altman analysis was applied to the 5 measurements made for all subjects. The Omegawave device exhibited very strong agreement with a quality-controlled Holter electrocardiogram. Deviations not exceeding 25 ms could be expected in 95% of the cases, which is within manageable ranges both for clinical practice and for sports.

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“…We included representative variables for parasympathetic activity from the time‐domain indices: standard deviation of normal‐to‐normal RR intervals (SDNN), root mean square of successive differences (RMSSD), and proportion of successive NN intervals that differ by more than 50 ms relative to the total number of NN intervals (pNN50); from the frequency‐domain indices: low‐frequency (LF) power, high‐frequency (HF) power, and total power; and from the non‐linear indices: dispersion of points from line of identity (SD1). RMSSD, HF power, and SD1 are reported to have the lowest rates of absolute error, and are less influenced by breathing rate than other calculated HRV indices …”

Section: Methodsmentioning

confidence: 89%

A pilot study of the effects of a high‐intensity aerobic exercise session on heart rate variability and arterial compliance in adolescents with or without type 1 diabetes

et al. 2020

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Arterial compliance and autonomic regulation are predictors of cardiovascular disease.In adults, both are altered chronically by type 1 diabetes (T1D) and acutely by exercise; however, the effects of T1D and exercise are less clear in adolescents. We measured short-term effects of a high-intensity aerobic interval exercise session on cardiovascular and metabolic variables in normal weight adolescents with T1D or without T1D (Control). Energy expenditure (EE), heart rate variability (HRV), arterial compliance, and blood pressure (BP) were measured before exercise (baseline) and three times over 105 minutes postexercise. The T1D and control groups had similar cardiorespiratory

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The Accuracy of Acquiring Heart Rate Variability from Portable Devices: A Systematic Review and Meta-Analysis

Cited by 117 publications

References 73 publications

Validity of Smartphone Heart Rate Variability Pre- and Post-Resistance Exercise

Validity of Smartphone Heart Rate Variability Pre- and Post-Resistance Exercise

Comparison of Omegawave Device and an Ambulatory ECG for RR Interval Measurement at rest

A pilot study of the effects of a high‐intensity aerobic exercise session on heart rate variability and arterial compliance in adolescents with or without type 1 diabetes

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