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.

Optimisation of assessment of maximal rate of heart rate increase for tracking training-induced changes in endurance exercise performance.

The maximal rate of heart rate (HR) increase (rHRI), a marker of HR acceleration during transition from rest to submaximal exercise, correlates with exercise performance. In this cohort study, whether rHRI tracked performance better when evaluated over shorter time-periods which include a greater proportion of HR acceleration and less steady-state HR was evaluated. rHRI and five-km treadmill running time-trial performance (5TTT) were assessed in 15 runners following one week of light training (LT), two weeks of heavy training (HT) and 10-day taper (T). rHRI was the first derivative maximum of a sigmoidal curve fit to one, two, three and four minutes of R-R data during transition from rest to running at 8 km/h (rHRI8 km/h), 10.5 km/h, 13 km/h and transition from 8 to 13 km/h (rHRI8-13km/h). 5TTT time increased from LT to HT (effect size [ES] 1.0, p < 0.001) then decreased from HT to T (ES -1.7, p < 0.001). 5TTT time was inversely related to rHRI8 km/h assessed over two (B = -5.54, p = 0.04) three (B = -5.34, p = 0.04) and four (B = -5.37, p = 0.04) minutes, and rHRI8-13km/h over one (B = -11.62, p = 0.006) and three (B = -11.44, p = 0.03) minutes. 5TTT correlated most consistently with rHRI8 km/h. rHRI8 km/h assessed over two to four minutes may be suitable for evaluating athlete responses to training.

Optimization of Maximal Rate of Heart Rate Increase Assessment in Runners.

PURPOSE: Correlations between fatigue-induced changes in exercise performance and maximal rate of heart rate (HR) increase (rHRI) may be affected by exercise intensity during assessment. This study evaluated the sensitivity of rHRI for tracking performance when assessed at varying exercise intensities. METHOD: Performance (time to complete a 5-km treadmill time-trial [5TTT]) and rHRI were assessed in 15 male runners following 1 week of light training, 2 weeks of heavy training (HT), and a 10-day taper (T). Maximal rate of HR increase (measured in bpm·s-1) was the first derivative maximum of a sigmoidal curve fit to HR data recorded during 5 min of running at 8 km·h-1 (rHRI8km·h-1), and during subsequent transition to 13 km·h-1 (rHRI8-13km·h-1) for a further 5 min. RESULTS: Time to complete a 5-km treadmill time-trial was likely slower following HT (effect size ± 90% confidence interval = 0.16 ± 0.06), and almost certainly faster following T (-0.34 ± 0.08). Maximal rate of HR increase during 5 min of running at 8 km·h-1 and rHRI8-13km·h-1 were unchanged following HT and likely increased following T (0.77 ± 0.45 and 0.66 ± 0.62, respectively). A moderate within-individual correlation was found between 5TTT and rHRI8km·h-1 (r value ± 90% confidence interval = -.35 ± .32). However, in a subgroup of athletes (n = 7) who were almost certainly slower to complete the 5TTT (4.22 ± 0.88), larger correlations were found between the 5TTT and rHRI8km·h-1 (r = -.84 ± .22) and rHRI8-13km·h-1 (r = -.52 ± .41). Steady-state HR during rHRI assessment in this group was very likely greater than in the faster subgroup (≥ 1.34 ± 0.86). CONCLUSION(S): The 5TTT performance was tracked by both rHRI8km·h-1 and rHRI8-13km·h-1. Correlations between rHRI and performance were stronger in a subgroup of athletes who exhibited a slower 5TTT. Individualized workloads during rHRI assessment may be required to account for varying levels of physical conditioning.

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.