scholarly journals Wrist-Based Photoplethysmography Assessment of Heart Rate and Heart Rate Variability: Validation of WHOOP

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3571
Author(s):  
Clint R. Bellenger ◽  
Dean Miller ◽  
Shona L. Halson ◽  
Greg Roach ◽  
Charli Sargent
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Coefficient Of Variation ◽  
Limits Of Agreement ◽  
Acceptable Agreement

Heart rate (HR) and HR variability (HRV) infer readiness to perform exercise in athletic populations. Technological advancements have facilitated HR and HRV quantification via photoplethysmography (PPG). This study evaluated the validity of WHOOP’s PPG-derived HR and HRV against electrocardiogram-derived (ECG) measures. HR and HRV were assessed via WHOOP and ECG over 15 opportunities. WHOOP-derived pulse-to-pulse (PP) intervals were edited with WHOOP’s proprietary filter, in addition to various filter strengths via Kubios HRV software. HR and HRV (Ln RMSSD) were quantified for each filter strength. Agreement was assessed via bias and limits of agreement (LOA), and contextualised using smallest worthwhile change (SWC) and coefficient of variation (CV). Regardless of filter strength, bias (≤0.39 ± 0.38%) and LOA (≤1.56%) in HR were lower than the CV (10–11%) and SWC (5–5.5%) for this parameter. For Ln RMSSD, bias (1.66 ± 1.80%) and LOA (±5.93%) were lowest for a 200 ms filter and WHOOP’s proprietary filter, which approached or exceeded the CV (3–13%) and SWC (1.5–6.5%) for this parameter. Acceptable agreement was found between WHOOP- and ECG-derived HR. Bias and LOA in Ln RMSSD approached or exceeded the SWC/CV for this variable and should be interpreted against its own level of bias precision.

scholarly journals Applicability of Dmax Method on Heart Rate Variability to Estimate the Lactate Thresholds in Male Runners

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Eduardo Marcel Fernandes Nascimento ◽  
Diego Antunes ◽  
Paulo Cesar do Nascimento Salvador ◽  
Fernando Klitzke Borszcz ◽  
Ricardo Dantas de Lucas
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Blood Lactate ◽  
Standard Error ◽  
Intraclass Correlation ◽  
Effect Sizes ◽  
Initial Speed ◽  
Limits Of Agreement ◽  
Aerobic Threshold ◽  
Post Hoc

Introduction. The purpose of this study was to evaluate the application of the Dmax method on heart rate variability (HRV) to estimate the lactate thresholds (LT), during a maximal incremental running test (MIRT). Methods. Nineteen male runners performed two MIRTs, with the initial speed at 8 km·h−1 and increments of 1 km·h−1 every 3 minutes, until exhaustion. Measures of HRV and blood lactate concentrations were obtained, and lactate (LT1 and LT2) and HRV (HRVTDMAX1 and HRVTDMAX2) thresholds were identified. ANOVA with Scheffe’s post hoc test, effect sizes (d), the bias ± 95% limits of agreement (LoA), standard error of the estimate (SEE), Pearson’s (r), and intraclass correlation coefficient (ICC) were calculated to assess validity. Results. No significant differences were observed between HRVTDMAX1 and LT1 when expressed for speed (12.1 ± 1.4 km·h−1 and 11.2 ± 2.1 km·h−1; p=0.55; d = 0.45; r = 0.46; bias ± LoA = 0.8 ± 3.7 km·h−1; SEE = 1.2 km·h−1 (95% CI, 0.9–1.9)). Significant differences were observed between HRVTDMAX2 and LT2 when expressed for speed (12.0 ± 1.2 km·h−1 and 14.1 ± 2.5 km·h−1; p=0.00; d = 1.21; r = 0.48; bias ± LoA = −1.0 ± 1.8 km·h−1; SEE = 1.1 km·h−1 (95% CI, 0.8–1.6)), respectively. Reproducibility values were found for the LT1 (ICC = 0.90; bias ± LoA = −0.7 ± 2.0 km·h−1), LT2 (ICC = 0.97; bias ± LoA = −0.1 ± 1.1 km·h−1), HRVTDMAX1 (ICC = 0.48; bias ± LoA = −0.2 ± 3.4 km·h−1), and HRVTDMAX2 (ICC = 0.30; bias ± LoA = 0.3 ± 3.5 km·h−1). Conclusions. The Dmax method applied over a HRV dataset allowed the identification of LT1 that is close to aerobic threshold, during a MIRT.

scholarly journals Validity of the Empatica E4 Wristband to Measure Heart Rate Variability (HRV) Parameters: a Comparison to Electrocardiography (ECG)

2020 ◽  
Vol 44 (11) ◽  
Author(s):  
Angela A. T. Schuurmans ◽  
Peter de Looff ◽  
Karin S. Nijhof ◽  
Catarina Rosada ◽  
Ron H. J. Scholte ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Experimental Testing ◽  
Ambulatory Monitoring ◽  
Clinical Population ◽  
Limits Of Agreement ◽  
Significant Difference ◽  
Cross Correlations ◽  
Measure Heart Rate ◽  
Monitoring Devices

Abstract Wearable monitoring devices are an innovative way to measure heart rate (HR) and heart rate variability (HRV), however, there is still debate about the validity of these wearables. This study aimed to validate the accuracy and predictive value of the Empatica E4 wristband against the VU University Ambulatory Monitoring System (VU-AMS) in a clinical population of traumatized adolescents in residential care. A sample of 345 recordings of both the Empatica E4 wristband and the VU-AMS was derived from a feasibility study that included fifteen participants. They wore both devices during two experimental testing and twelve intervention sessions. We used correlations, cross-correlations, Mann-Whitney tests, difference factors, Bland-Altman plots, and Limits of Agreement to evaluate differences in outcomes between devices. Significant correlations were found between Empatica E4 and VU-AMS recordings for HR, SDNN, RMSSD, and HF recordings. There was a significant difference between the devices for all parameters but HR, although effect sizes were small for SDNN, LF, and HF. For all parameters but RMSSD, testing outcomes of the two devices led to the same conclusions regarding significance. The Empatica E4 wristband provides a new opportunity to measure HRV in an unobtrusive way. Results of this study indicate the potential of the Empatica E4 as a practical and valid tool for research on HR and HRV under non-movement conditions. While more research needs to be conducted, this study could be considered as a first step to support the use of HRV recordings provided by wearables.

Monitoring the Heart Rate Variability Responses to Training Loads in Competitive Swimmers Using a Smartphone Application and the Banister Impulse-Response Model

2020 ◽  
pp. 1-9
Author(s):  
Eva Piatrikova ◽  
Nicholas J. Willsmer ◽  
Marco Altini ◽  
Mladen Jovanović ◽  
Lachlan J.G. Mitchell ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Impulse Response ◽  
Coefficient Of Variation ◽  
Seasonal Changes ◽  
Well Being ◽  
Smartphone Application ◽  
Response Model ◽  
Subjective Well Being ◽  
Competitive Swimmers

Purpose: First, to examine whether heart rate variability (HRV) responses can be modeled effectively via the Banister impulse-response model when the session rating of perceived exertion (sRPE) alone, and in combination with subjective well-being measures, are utilized. Second, to describe seasonal HRV responses and their associations with changes in critical speed (CS) in competitive swimmers. Methods: A total of 10 highly trained swimmers collected daily 1-minute HRV recordings, sRPE training load, and subjective well-being scores via a novel smartphone application for 15 weeks. The impulse-response model was used to describe chronic root mean square of the successive differences (rMSSD) responses to training, with sRPE and subjective well-being measures used as systems inputs. Changes in CS were obtained from a 3-minute all-out test completed in weeks 1 and 14. Results: The level of agreement between predicted and actual HRV data was R2 = .66 (.25) when sRPE alone was used. Model fits improved in the range of 4% to 21% when different subjective well-being measures were combined with sRPE, representing trivial-to-moderate improvements. There were no significant differences in weekly group averages of log-transformed (Ln) rMSSD (P = .34) or HRV coefficient of variation of Ln rMSSD (P = .12); however, small-to-large changes (d = 0.21–1.46) were observed in these parameters throughout the season. Large correlations were observed between seasonal changes in HRV measures and CS (changes in averages of Ln rMSSD: r = .51, P = .13; changes in coefficient of variation of Ln rMSSD: r = −.68, P = .03). Conclusion: The impulse-response model and data collected via a novel smartphone application can be used to model HRV responses to swimming training and nontraining-related stressors. Large relationships between seasonal changes in measured HRV parameters and CS provide further evidence for incorporating a HRV-guided training approach.

Comparison of Heart-Rate-Variability Recording With Smartphone Photoplethysmography, Polar H7 Chest Strap, and Electrocardiography

2017 ◽  
Vol 12 (10) ◽  
pp. 1324-1328 ◽  
Author(s):  
Daniel J. Plews ◽  
Ben Scott ◽  
Marco Altini ◽  
Matt Wood ◽  
Andrew E. Kilding ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Data Collection ◽  
Healthy Subjects ◽  
Ease Of Use ◽  
Technical Error ◽  
Normal Breathing ◽  
Standard Difference ◽  
Rate Sensor ◽  
Acceptable Agreement

Purpose: To establish the validity of smartphone photoplethysmography (PPG) and heart-rate sensor in the measurement of heart-rate variability (HRV). Methods: 29 healthy subjects were measured at rest during 5 min of guided breathing and normal breathing using smartphone PPG, a heart-rate chest strap, and electrocardiography (ECG). The root mean sum of the squared differences between R–R intervals (rMSSD) was determined from each device. Results: Compared to ECG, the technical error of estimate (TEE) was acceptable for all conditions (average TEE CV% [90% CI] = 6.35 [5.13; 8.5]). When assessed as a standardized difference, all differences were deemed “trivial” (average standard difference [90% CI] = 0.10 [0.08; 0.13]). Both PPG- and heart-rate-sensor-derived measures had almost perfect correlations with ECG (R = 1.00 [0.99; 1.00]). Conclusion: Both PPG and heart-rate sensors provide an acceptable agreement for the measurement of rMSSD when compared with ECG. Smartphone PPG technology may be a preferred method of HRV data collection for athletes due to its practicality and ease of use in the field.

Reproducibility of Heart Rate Variability Indices at Post-maximal Exercise

2020 ◽  
Vol 41 (08) ◽  
pp. 512-519 ◽  
Author(s):  
Jaqueline Alves Araújo ◽  
Tiago Peçanha ◽  
Fabiula Isoton Novelli ◽  
César Siqueira Aleixes Mello ◽  
Daniel Moreira-Gonçalves ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Coefficient Of Variation ◽  
Maximal Exercise ◽  
High Reliability ◽  
Active Recovery ◽  
Coefficient Corrélation ◽  
Agreement Coefficient ◽  
The Time Domain ◽  
Good Agreement

AbstractTo analyze whether heart rate variability is reproducible after maximal exercise, 11 men (22.1±3.2 years) performed four incremental exercise tests followed by passive or active recovery. There was high reliability (intraclass coefficient correlation: 0.72–0.96) and fair-to-excellent agreement (coefficient of variation: 7.81–22.09%) in passive recovery, as well as moderate-to-high reliability (intraclass coefficient correlation: 0.50–0.87) and good agreement (coefficient of variation: 11.08–20.89%) in active recovery for LnRMSSD index. There was moderate-to-high reliability (intraclass coefficient correlation: 0.51–0.81) and good agreement (coefficient of variation: 10.41–18.87%) in most of the analyzed time points, in both recovery types for LnSDNN. In both types of recovery, the time domain heart rate variability 5–10 min indices (passive: intraclass coefficient correlation : 0.87–0.88; coefficient of variation: 7.67–13.44%; active: intraclass coefficient correlation 0.59–0.80; coefficient of variation: 14.62–16.26%) presented higher intraclass coefficient correlation and lower coefficient of variation than the spectral heart rate variability indices (passive: intraclass coefficient correlation: 0.71–0.87; coefficient of variation: 12.33–34.21%; active: intraclass coefficient correlation: 0.46–0.77; coefficient of variation: 24.41–105.12%). The LnRMSSD and LnSDNN indices analyzed in 30 s segments and the heart rate variability 5–10 min indices after maximal exercise in untrained healthy men showed satisfactory reproducibility, regardless of the type of recovery, with the time-domain indices showing higher reproducibility than the frequency-domain indices.

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