Evaluating the typical day-to-day variability of WHOOP-derived heart rate variability in Olympic water polo athletes

Heart rate (HR) and HR variability (HRV) can be used to infer readiness to perform exercise in athletic populations. Advancements in the photoplethysmography technology of wearable devices such as WHOOP allow for the frequent and convenient measurement of HR and HRV, and therefore enhanced application in athletes. However, it is important that the reliability of such technology is acceptable prior to its application in practical settings. Eleven elite male water polo players (age 28.8 ± 5.3 years [mean ± standard deviation]; height 190.3 ± 3.8 cm; body mass 95.0 ± 6.9 kg; international matches 117.9 ± 92.1) collected their HR and HRV daily via a WHOOP strap (WHOOP 3.0, CB Rank, Boston, MA, USA) over 16 weeks ahead of the 2021 Tokyo Olympic Games. The WHOOP strap quantified HR and HRV via wrist-based photoplethysmography during overnight sleep periods. The weekly (i.e., 7-day) coefficient of variation in lnRMSSD (lnRMSSDCV) and HR (HRCV) was calculated as a measure of day-to-day variability in lnRMSSD and HR, and presented as a mean of the entire recording period. The mean weekly lnRMSSDCV and HRCV over the 16-week period was 5.4 ± 0.7% (mean ± 95% confidence intervals) and 7.6 ± 1.3%, respectively. The day-to-day variability in WHOOP-derived lnRMSSD and HR is within or below the range of day-to-day variability in alternative lnRMSSD (~3–13%) and HR (~10–11%) assessment protocols, indicating that the assessment of HR and HRV by WHOOP does not introduce any more variability than that which is naturally present in these variables

A validation study of a commercial wearable device to automatically detect and estimate sleep

The aims of this study were to: (1) compare actigraphy (ACTICAL) and a commercially available sleep wearable (i.e., WHOOP) under two functionalities (i.e., sleep auto-detection (WHOOP-AUTO) and manual adjustment of sleep (WHOOP-MANUAL)) for two-stage categorisation of sleep (sleep or wake) against polysomnography, and; (2) compare WHOOP-AUTO and WHOOP-MANUAL for four-stage categorisation of sleep (wake, light sleep, slow wave sleep (SWS), or rapid eye movement sleep (REM)) against polysomnography. Six healthy adults (male: n = 3; female: n = 3; age: 23.0 ± 2.2 yr) participated in the nine-night protocol. Fifty-four sleeps assessed by ACTICAL, WHOOP-AUTO and WHOOP-MANUAL were compared to polysomnography using difference testing, Bland–Altman comparisons, and 30-s epoch-by-epoch comparisons. Compared to polysomnography, ACTICAL overestimated total sleep time (37.6 min) and underestimated wake (−37.6 min); WHOOP-AUTO underestimated SWS (−15.5 min); and WHOOP-MANUAL underestimated wake (−16.7 min). For ACTICAL, sensitivity for sleep, specificity for wake and overall agreement were 98%, 60% and 89%, respectively. For WHOOP-AUTO, sensitivity for sleep, wake, and agreement for two-stage and four-stage categorisation of sleep were 90%, 60%, 86% and 63%, respectively. For WHOOP-MANUAL, sensitivity for sleep, wake, and agreement for two-stage and four-stage categorisation of sleep were 97%, 45%, 90% and 62%, respectively. WHOOP-AUTO and WHOOP-MANUAL have a similar sensitivity and specificity to actigraphy for two-stage categorisation of sleep and can be used as a practical alternative to polysomnography for two-stage categorisation of sleep and four-stage categorisation of sleep

Correction: Bellenger et al. Wrist-Based Photoplethysmography Assessment of Heart Rate and Heart Rate Variability: Validation of WHOOP. Sensors 2021, 21, 3571

The authors wish to correct the following errors in the original paper [...

Wrist-based photoplethysmography assessment of heart rate and heart rate variability : Validation of WHOOP

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

Monitoring athletic training status using the maximal rate of heart rate increase

Objectives: Reductions in maximal rate of heart rate increase (rHRI) correlate with performance reductions when training load is increased. This study evaluated whether rHRI tracked performance changes across a range of training states. Design: Prospective intervention. Methods: rHRI was assessed during five min of cycling at 100 W (rHRIcyc) and running at 8 km/h (rHRIrun) in 13 male triathletes following two weeks of light-training (LT), two weeks of heavy-training (HT) and a two-day recovery period (RP). A five min cycling time-trial assessed performance and peak oxygen consumption (VO2peak). Results: Performance likely decreased following HT (Effect size ± 90% confidence interval = -0.18 ± 0.09), then very likely increased following RP (0.32 ± 0.14). rHRIcyc very likely decreased (-0.48 ± 0.24), and rHRIrun possibly decreased (-0.33 ± 0.48), following HT. Changes in both measures were unclear following RP. Steady-state HR was almost certainly lower (-0.81 ± 0.31) during rHRIcyc than rHRIrun. A large correlation was found between reductions in performance and rHRIrun (r ± 90%; CI = 0.65 ± 0.34) from LT to HT, but was unclear for rHRIcyc. Trivial within-subject correlations were found between rHRI and performance, but the strength of relationship between rHRIrun and performance was largely associated with VO2peak following LT (r = -0.58 ± 0.38). Conclusions: Performance reductions were most sensitively tracked by rHRIrun following HT. This may be due to rHRIrun being assessed at a higher intensity than rHRIcyc, inferred from a higher steady-state HR and supported by a stronger within-subject relationship between rHRIrun and performance in individuals with a lower VO2peak, in whom the same exercise intensity would represent a greater physiological stress. rHRI assessed at relatively high exercise intensities may better track performance changes

Correction: Bellenger et al. Wrist-Based Photoplethysmography Assessment of Heart Rate and Heart Rate Variability: Validation of WHOOP. <i>Sensors</i> 2021, <i>21</i>, 3571

The authors wish to correct the following errors in the original paper [...

The Effect of footwear on running performance and running economy in distance runners

Background: The effect of footwear on running economy has been investigated in numerous studies. However, no systematic review and meta-analysis has synthesised the available literature and the effect of footwear on running performance is not known. Objective: The aim of this systematic review and meta-analysis was to investigate the effect of footwear on running performance and running economy in distance runners, by reviewing controlled trials that compare different footwear conditions or compare footwear with barefoot. Methods: The Web of Science, Scopus, MEDLINE, CENTRAL (Cochrane Central Register of Controlled Trials), EMBASE, AMED (Allied and Complementary Medicine), CINAHL and SPORTDiscus databases were searched from inception up until April 2014. Included articles reported on controlled trials that examined the effects of footwear or footwear characteristics (including shoe mass, cushioning, motion control, longitudinal bending stiffness, midsole viscoelasticity, drop height and comfort) on running performance or running economy and were published in a peer-reviewed journal. Results: Of the 1,044 records retrieved, 19 studies were included in the systematic review and 14 studies were included in the meta-analysis. No studies were identified that reported effects on running performance. Individual studies reported significant, but trivial, beneficial effects on running economy for comfortable and stiff-soled shoes [standardised mean difference (SMD) <0.12; P < 0.05), a significant small beneficial effect on running economy for cushioned shoes (SMD = 0.37; P < 0.05) and a significant moderate beneficial effect on running economy for training in minimalist shoes (SMD = 0.79; P < 0.05). Meta-analysis found significant small beneficial effects on running economy for light shoes and barefoot compared with heavy shoes (SMD < 0.34; P < 0.01) and for minimalist shoes compared with conventional shoes (SMD = 0.29; P < 0.01). A significant positive association between shoe mass and metabolic cost of running was identified (P < 0.01). Footwear with a combined shoe mass less than 440 g per pair had no detrimental effect on running economy. Conclusions: Certain models of footwear and footwear characteristics can improve running economy. Future research in footwear performance should include measures of running performance.12 page(s

Wrist-based photoplethysmography assessment of heart rate and heart rate variability: Validation of whoop

Heart rate (HR) and HR variability (HRV) infer readiness to perform exercise in athletic populations. Technological advancements have facilitated HR and HRV quantification via photo-plethysmography (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

Predicting maximal aerobic speed through set distance time-trials

Purpose: Knowledge of aerobic performance capacity allows for the optimisation of training programs in aerobically dominant sports. Maximal aerobic speed (MAS) is a measure of aerobic performance; however, the time and personnel demands of establishing MAS are considerable. This study aimed to determine whether time-trials (TT), which are shorter and less onerous than traditional MAS protocols, may be used to predict MAS. Methods: 28 Australian Rules football players completed a test of MAS, followed by TTs of six different distances in random order, each separated by at least 48 h. Half of the participants completed TT distances of 1200, 1600 and 2000 m, and the others completed distances of 1400, 1800 and 2200 m. Results: Average speed for the 1200 and 1400 m TTs were greater than MAS (P  0.08). Average speed for all TT distances correlated with MAS (r = 0.69–0.84; P < 0.02), but there was a negative association between the difference in average TT speed and MAS with increasing TT distance (r = −0.79; P < 0.01). Average TT speed over the 2000 m distance exhibited the best agreement with MAS. Conclusions: MAS may be predicted from the average speed during a TT for any distance between 1200 and 2200 m, with 2000 m being optimal. Performance of a TT may provide a simple alternative to traditional MAS testing.6 page(s

Monitoring athletic training status through autonomic heart rate regulation : a systematic review and meta-analysis

Background: Autonomic regulation of heart rate (HR) as an indicator of the body’s ability to adapt to an exercise stimulus has been evaluated in many studies through HR variability (HRV) and post-exercise HR recovery (HRR). Recently, HR acceleration has also been investigated. Objective: The aim of this systematic literature review and meta-analysis was to evaluate the effect of negative adaptations to endurance training (i.e., a period of overreaching leading to attenuated performance) and positive adaptations (i.e., training leading to improved performance) on autonomic HR regulation in endurance-trained athletes. Methods: We searched Ovid MEDLINE, Embase, CINAHL, SPORTDiscus, PubMed, and Academic Search Premier databases from inception until April 2015. Included articles examined the effects of endurance training leading to increased or decreased exercise performance on four measures of autonomic HR regulation: resting and post-exercise HRV [vagal-related indices of the root-mean-square difference of successive normal R–R intervals (RMSSD), high frequency power (HFP) and the standard deviation of instantaneous beat-to-beat R–R interval variability (SD1) only], and post-exercise HRR and HR acceleration. Results: Of the 5377 records retrieved, 27 studies were included in the systematic review and 24 studies were included in the meta-analysis. Studies inducing increases in performance showed small increases in resting RMSSD [standardised mean difference (SMD) = 0.58; P < 0.001], HFP (SMD = 0.55; P < 0.001) and SD1 (SMD = 0.23; P = 0.16), and moderate increases in post-exercise RMSSD (SMD = 0.60; P < 0.001), HFP (SMD = 0.90; P < 0.04), SD1 (SMD = 1.20; P = 0.04), and post-exercise HRR (SMD = 0.63; P = 0.002). A large increase in HR acceleration (SMD = 1.34) was found in the single study assessing this parameter. Studies inducing decreases in performance showed a small increase in resting RMSSD (SMD = 0.26; P = 0.01), but trivial changes in resting HFP (SMD = 0.04; P = 0.77) and SD1 (SMD = 0.04; P = 0.82). Post-exercise RMSSD (SMD = 0.64; P = 0.04) and HFP (SMD = 0.49; P = 0.18) were increased, as was HRR (SMD = 0.46; P < 0.001), while HR acceleration was decreased (SMD = −0.48; P < 0.001). Conclusions: Increases in vagal-related indices of resting and post-exercise HRV, post-exercise HRR, and HR acceleration are evident when positive adaptation to training has occurred, allowing for increases in performance. However, increases in post-exercise HRV and HRR also occur in response to overreaching, demonstrating that additional measures of training tolerance may be required to determine whether training-induced changes in these parameters are related to positive or negative adaptations. Resting HRV is largely unaffected by overreaching, although this may be the result of methodological issues that warrant further investigation. HR acceleration appears to decrease in response to overreaching training, and thus may be a potential indicator of training-induced fatigue.26 page(s