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Daniel J. Plews, Paul B. Laursen and Martin Buchheit

Purpose:

Heart-rate variability (HRV) is a popular tool for monitoring autonomic nervous system status and training adaptation in athletes. It is believed that increases in HRV indicate effective training adaptation, but these are not always apparent in elite athletes.

Methods:

Resting HRV was recorded in 4 elite rowers (rowers A, B, C, and D) over the 7-wk period before their success at the 2015 World Rowing Championships. The natural logarithm of the square root of the mean sum of the squared differences (Ln rMSSD) between R–R intervals, Ln rMSSD:R-R ratio trends, and the Ln-rMSSD–to–R-R-interval relationship were assessed for each championship-winning rower.

Results:

The time course of change in Ln rMSSD was athlete-dependent, with stagnation and decreases apparent. However, there were consistent substantial reductions in the Ln rMSSD:R-R ratio: rower A, baseline toward wk 5 (–2.35 ± 1.94); rower B, baseline to wk 4 and 5 (–0.41 ± 0.48 and –0.64 ± 0.65, respectively); rower C, baseline to wk 4 (–0.58 ± 0.66); and rower D, baseline to wk 4, 5, and 6 (–1.15 ± 0.91, –0.81 ± 0.74, and –1.43 ± 0.69, respectively).

Conclusions:

Reductions in Ln rMSSD concurrent with reductions in the Ln rMSSD:R-R ratio are indicative of parasympathetic saturation. Consequently, 3 of 4 rowers displayed substantial increases in parasympathetic activity despite having decreases in Ln rMSSD. These results confirm that a combination of indices should be used to monitor cardiac autonomic activity.

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Jamie Douglas, Daniel J. Plews, Phil J. Handcock and Nancy J. Rehrer

Purpose:

To determine whether a facilitated recovery via cold-water immersion (CWI) after simulated rugby sevens would influence parasympathetic reactivation and repeated-sprint (RS) performance across 6 matches in a 2-d tournament.

Methods:

Ten male team-sport athletes completed 6 rugby sevens match simulations over 2 d with either postmatch passive recovery (PAS) or CWI in a randomized crossover design. Parasympathetic reactivation was determined via the natural logarithm of the square root of the mean of the sum of the squares of differences between adjacent R-R intervals (ln rMSSD). RS performance was calculated as time taken (s) to complete 6 × 30-m sprints within the first half of each match.

Results:

There were large increases in postintervention ln rMSSD between CWI and PAS after all matches (ES 90% CL: +1.13; ±0.21). Average heart rate (HR) during the RS performance task (HRAverage RS) was impaired from baseline from match 3 onward for both conditions. However, HRAverage RS was higher with CWI than with PAS (ES 90% CL: 0.58; ±0.58). Peak HR during the RS performance task (HRPeak RS) was similarly impaired from baseline for match 3 onward during PAS and for match 4 onward with CWI. HRPeak RS was very likely higher with CWI than with PAS (ES 90% CL: +0.80; ±0.56). No effects of match or condition were observed for RS performance, although there were moderate correlations between the changes in HRAverage RS (r 90% CL: –0.33; ±0.14), HRPeak RS (r 90% CL: –0.38; ±0.13), and RS performance.

Conclusion:

CWI facilitated cardiac parasympathetic reactivation after a simulated rugby sevens match. The decline in average and peak HR across matches was partially attenuated by CWI. This decline was moderately correlated with a reduction in RS performance.

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Ed Maunder, Andrew E. Kilding, Christopher J. Stevens and Daniel J. Plews

A common practice among endurance athletes is to purposefully train in hot environments during a “heat stress camp.” However, combined exercise-heat stress poses threats to athlete well-being, and therefore, heat stress training has the potential to induce maladaptation. This case study describes the monitoring strategies used in a successful 3-week heat stress camp undertaken by 2 elite Ironman triathletes, namely resting heart rate variability, self-report well-being, and careful prescription of training based on previously collected physiological data. Despite the added heat stress, training volume very likely increased in both athletes, and training load very likely increased in one of the athletes, while resting heart rate variability and self-report well-being were maintained. There was also some evidence of favorable metabolic changes during routine laboratory testing following the camp. The authors therefore recommend that practitioners working with endurance athletes embarking on a heat stress training camp consider using the simple strategies employed in the present case study to reduce the risk of maladaptation and nonfunctional overreaching.

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Daniel J. Plews, Paul B. Laursen, Andrew E. Kilding and Martin Buchheit

The aim of this study was to compare 2 different methodological assessments when analyzing the relationship between performance and heart-rate (HR) -derived indices (resting HR [RHR] and HR variability [HRV]) to evaluate positive adaptation to training. The relative change in estimated maximum aerobic speed (MAS) and 10-km-running performance was correlated to the relative change in RHR and the natural logarithm of the square root of the mean sum of the squared differences between R-R intervals on an isolated day (RHRday; Ln rMSSDday) or when averaged over 1 wk (RHRweek; Ln rMSSDweek) in 10 runners who responded to a 9-wk training intervention. Moderate and small correlations existed between changes in MAS and 10-km-running performance and RHRday (r = .35, 90%CI [–.35, .76] and r = –.21 [–.68, .39]), compared with large and very large correlations for RHRweek (r = –.62 [–.87, –.11] and r = .73 [.30, .91]). While a trivial correlation was observed for MAS vs Ln rMSSDday (r = –.06 [–.59, .51]), a very large correlation existed with Ln rMSSDweek (r = .72 [.28, .91]). Similarly, changes in 10-km-running performance revealed a small correlation with Ln rMSSDday (r = –.17 [–.66, .42]), vs a very large correlation for Ln rMSSDweek (r = –.76 [–.92, –.36]). In conclusion, the averaging of RHR and HRV values over a 1-wk period appears to be a superior method for evaluating positive adaption to training compared with assessing its value on a single isolated day.

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Daniel J. Plews, Ben Scott, Marco Altini, Matt Wood, Andrew E. Kilding and Paul B. Laursen

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.

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Daniel J. Plews, Paul B. Laursen, Yann Le Meur, Christophe Hausswirth, Andrew E. Kilding and Martin Buchheit

Purpose:

To establish the minimum number of days that heart-rate-variability (HRV, ie, the natural logarithm of square root of the mean sum of the squared differences between R-R intervals, Ln rMSSD) data should be averaged to achieve correspondingly equivalent results as data averaged over a 1-wk period.

Methods:

Standardized changes in Ln rMSSD between different phases of training (normal training, functional overreaching (FOR), overall training, and taper) and the correlation coefficients of percentage changes in performance vs changes in Ln rMSSD were compared when averaging Ln rMSSD from 1 to 7 d, randomly selected within the week.

Results:

Standardized Ln rMSSD changes (90% confidence limits, CL) from baseline to overload (FOR) were 0.20 ± 0.28, 0.33 ± 0.26, 0.49 ± 0.33, 0.48 ± 0.28, 0.47 ± 0.26, 0.45 ± 0.26, and 0.43 ± 0.29 on days 1 to 7, respectively. Correlations (90% CL) over the same time sequence and training phase were –.02 ± .23, –.07 ± .23, –.17 ± .22, –.25 ± .22, –.26 ± .22, –.28 ± .21, and –.25 ± .22 on days 1 to 7. There were almost perfect quadratic relationships between standardized changes/r values vs the number of days Ln rMSSD was averaged (r 2 = .92 and .97, respectively) in trained triathletes during FOR. This indicates a plateau in the increase in standardized changes/r values’ magnitude after 3 and 4 d, respectively, in trained triathletes.

Conclusion:

Practitioners using HRV to monitor training adaptation should use a minimum of 3 (randomly selected) valid data points per week.

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Ana C. Holt, Daniel J. Plews, Katherine T. Oberlin-Brown, Fabrice Merien and Andrew E. Kilding

Purpose: To determine the effect of different high-intensity interval-training (IT) sessions on the postexercise recovery response and time course across varying recovery measures. Methods: A total of 13 highly trained rowers (10 male and 3 female, peak oxygen uptake during a 6-min maximal test 4.9 [0.7] L·min−1) completed 3 IT sessions on a rowing ergometer separated by 7 d. Sessions consisted of 5 × 3.5 min, 4-min rest periods (maximal oxygen uptake [VO2max]); 10 × 30 s, 5-min rest periods (glycolytic); and 5 × 10 min, 4-min rest periods (threshold). Participants were instructed to perform intervals at the highest maintainable pace. Blood lactate and salivary cortisol were measured preexercise and postexercise. Resting heart-rate (HR) variability, post-submaximal-exercise HR variability, submaximal-exercise HR, HR recovery, and modified Wingate peak and mean power were measured preexercise and 1, 10, 24, 34, 48, 58, and 72 h postexercise. Participants resumed training throughout the measurement period. Results: Between-groups short-term response differences (1 h post-IT) across IT sessions were trivial or unclear for all recovery variables. However, post-submaximal-exercise HR variability demonstrated the longest recovery time course (threshold = 37.8 [14.2], glycolytic = 20.2 [11.0], and VO2max = 20.6 [15.2]; mean [h] ± confidence limits). Conclusion: Short-term responses to threshold, glycolytic, and VO2max IT in highly trained male and female rowers were similar. Recovery time course was greatest following threshold compared with glycolytic and VO2max-focused training, suggesting a durational influence on recovery time course at HR intensities ≥80% HRmax. As such, this provides valuable information around the programming and sequencing of high-intensity IT for endurance athletes.

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Daniel J. Plews, Paul B. Laursen, Andrew E. Kilding and Martin Buchheit

Purpose:

Elite endurance athletes may train in a polarized fashion, such that their training-intensity distribution preserves autonomic balance. However, field data supporting this are limited.

Methods:

The authors examined the relationship between heart-rate variability and training-intensity distribution in 9 elite rowers during the 26-wk build-up to the 2012 Olympic Games (2 won gold and 2 won bronze medals). Weekly averaged log-transformed square root of the mean sum of the squared differences between R-R intervals (Ln rMSSD) was examined, with respect to changes in total training time (TTT) and training time below the first lactate threshold (>LT1), above the second lactate threshold (LT2), and between LT1 and LT2 (LT1–LT2).

Results:

After substantial increases in training time in a particular training zone or load, standardized changes in Ln rMSSD were +0.13 (unclear) for TTT, +0.20 (51% chance increase) for time >LT1, –0.02 (trivial) for time LT1–LT2, and –0.20 (53% chance decrease) for time >LT2. Correlations (±90% confidence limits) for Ln rMSSD were small vs TTT (r = .37 ± .80), moderate vs time >LT1 (r = .43 ± .10), unclear vs LT1–LT2 (r = .01 ± .17), and small vs >LT2 (r = –.22 ± .50).

Conclusion:

These data provide supportive rationale for the polarized model of training, showing that training phases with increased time spent at high intensity suppress parasympathetic activity, while low-intensity training preserves and increases it. As such, periodized low-intensity training may be beneficial for optimal training programming.

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Emiel Schulze, Hein A.M. Daanen, Koen Levels, Julia R. Casadio, Daniel J. Plews, Andrew E. Kilding, Rodney Siegel and Paul B. Laursen

Purpose:

To determine the effect of thermal state and thermal comfort on cycling performance in the heat.

Methods:

Seven well-trained male triathletes completed 3 performance trials consisting of 60 min cycling at a fixed rating of perceived exertion (14) followed immediately by a 20-km time trial in hot (30°C) and humid (80% relative humidity) conditions. In a randomized order, cyclists either drank ambient-temperature (30°C) fluid ad libitum during exercise (CON), drank ice slurry (−1°C) ad libitum during exercise (ICE), or precooled with iced towels and ice slurry ingestion (15g/kg) before drinking ice slurry ad libitum during exercise (PC+ICE). Power output, rectal temperature, and ratings of thermal comfort were measured.

Results:

Overall mean power output was possibly higher in ICE (+1.4% ± 1.8% [90% confidence limit]; 0.4 > smallest worthwhile change [SWC]) and likely higher PC+ICE (+2.5% ± 1.9%; 1.5 > SWC) than in CON; however, no substantial differences were shown between PC+ICE and ICE (unclear). Time-trial performance was likely enhanced in ICE compared with CON (+2.4% ± 2.7%; 1.4 > SWC) and PC+ICE (+2.9% ± 3.2%; 1.9 > SWC). Differences in mean rectal temperature during exercise were unclear between trials. Ratings of thermal comfort were likely and very likely lower during exercise in ICE and PC+ICE, respectively, than in CON.

Conclusions:

While PC+ICE had a stronger effect on mean power output compared with CON than ICE did, the ICE strategy enhanced late-stage time-trial performance the most. Findings suggest that thermal comfort may be as important as thermal state for maximizing performance in the heat.