Heart-rate recovery (HRR) has been proposed as a marker of autonomic function and training status in athletes. The authors performed a systematic review of studies that examined HRR after training. Five cross-sectional studies and 8 studies investigating changes over time (longitudinal) met our criteria. Three out of 5 crosssectional studies observed a faster HRR in trained compared with untrained subjects, while 2 articles showed no change as a result of training. Most longitudinal studies observed a corresponding increase in HRR and power output (training status). Although confounding factors such as age, ambient temperature, and the intensity and duration of the exercise period preceding HRR make it difficult to compare these studies, the available studies indicated that HRR was related to training status. Therefore, the authors conclude that HRR has the potential to become a valuable tool to monitor changes in training status in athletes and less well-trained subjects, but more studies and better standardization are required to match this potential.
Hein A.M. Daanen, Robert P. Lamberts, Victor L. Kallen, Anmin Jin and Nico L.U. Van Meeteren
Sander P.M. Ganzevles, Arnold de Haan, Peter J. Beek, Hein A.M. Daanen and Martin J. Truijens
For training to be optimal, daily training load has to be adapted to the momentary status of the individual athlete, which is often difficult to establish. Therefore, the current study investigated the predictive value of heart-rate recovery (HRR) during a standardized warm-up for training load. Training load was quantified by the variation in heart rate during standardized training in competitive swimmers. Eight female and 5 male Dutch national-level swimmers participated in the study. They all performed 3 sessions consisting of a 300-m warm-up test and a 10 × 100-m training protocol. Both protocols were swum in front crawl at individually standardized velocities derived from an incremental step test. Velocity was related to 75% and 85% heart-rate reserve (% HRres) for the warm-up and training, respectively. Relative HRR during the first 60 s after the warm-up (HRRw-up) and differences between the actual and intended heart rate for the warm-up and the training (ΔHRw-up and ΔHRtr) were determined. No significant relationship between HRRw-up and ΔHRtr was found (F 1,37 = 2.96, P = .09, R 2 = .07, SEE = 4.65). There was considerable daily variation in ΔHRtr at a given swimming velocity (73–93% HRres). ΔHRw-up and ΔHRtr were clearly related (F 1,37 = 74.31, P < .001, R 2 = .67, SEE = 2.78). HRR after a standardized warm-up does not predict heart rate during a directly subsequent and standardized training session. Instead, heart rate during the warm-up protocol seems a promising alternative for coaches to make daily individual-specific adjustments to training programs.
Yann Le Meur, Martin Buchheit, Anaël Aubry, Aaron J Coutts and Christophe Hausswirth
Faster heart-rate recovery (HRR) after high to maximal exercise (≥90% of maximal heart rate) has been reported in athletes suspected of functional overreaching (f-OR). This study investigated whether this response would also occur at lower exercise intensity.
Responses of HRR and rating of perceived exertion (RPE) were compared during an incremental intermittent running protocol to exhaustion in 20 experienced male triathletes (8 control subjects and 13 overload subjects led to f-OR) before and immediately after an overload training period and after a 1-wk taper.
Both groups demonstrated an increase in HRR values immediately after the training period, but this change was very likely to almost certainly larger in the f-OR group at all running intensities (large to very large differences, eg, +16 ± 7 vs +3 ± 5 beats/min, in the f-OR and control groups at 11 km/h, respectively). The highest between-groups differences in changes in HRR were reported at 11 km/h (13 ± 4 beats/min) and 12 km/h (10 ± 6 beats/min). A concomitant increase in RPE at all intensities was reported only in the f-OR group (large to extremely large differences, +2.1 ± 1.5 to +0.7 ± 1.5 arbitrary units).
These findings confirm that faster HRR does not systematically predict better physical performance. However, when interpreted in the context of the athletes’ fatigue state and training phase, HRR after submaximal exercise may be more discriminant than HRR measures taken after maximal exercise for monitoring f-OR. These findings may be applied in practice by regularly assessing HRR after submaximal exercise (ie, warm-up) for monitoring endurance athletes’ responses to training.
Laurent Mourot, Nicolas Fabre, Erik Andersson, Sarah Willis, Martin Buchheit and Hans-Christer Holmberg
Postexercise heart-rate (HR) recovery (HRR) indices have been associated with running and cycling endurance-exercise performance. The current study was designed (1) to test whether such a relationship also exists in the case of cross-country skiing (XCS) and (2) to determine whether the magnitude of any such relationship is related to the intensity of exercise before obtaining HRR indices. Ten elite male cross-country skiers (mean ± SD; 28.2 ± 5.4 y, 181 ± 8 cm, 77.9 ± 9.4 kg, 69.5 ± 4.3 mL · min−1 · kg−1 maximal oxygen uptake [VO2max]) performed 2 sessions of roller-skiing on a treadmill: a 2 × 3-km time trial and the same 6-km at an imposed submaximal speed followed by a final 800-m time trial. VO2 and HR were monitored continuously, while HRR and blood lactate (BLa) were assessed during 2 min immediately after each 6-km and the 800-m time trial. The 6-km time-trial time was largely negatively correlated with VO2max and BLa. On the contrary, there was no clear correlation between the 800-m time-trial time and VO2, HR, or BLa. In addition, in no case was any clear correlation between any of the HRR indices and performance time or VO2max observed. These findings confirm that XCS performance is largely correlated with VO2max and the ability to tolerate high levels of BLa; however, postexercise HRR showed no clear association with performance. The homogeneity of the group of athletes involved and the contribution of the arms and upper body to the exercise preceding determination of HRR may explain this absence of a relationship.
Benoit Capostagno, Michael I. Lambert and Robert P. Lamberts
To determine whether a submaximal cycling test could be used to monitor and prescribe high-intensity interval training (HIT).
Two groups of male cyclists completed 4 HIT sessions over a 2-wk period. The structured-training group (SG; n = 8, VO2max = 58.4 ± 4.2 mL · min−1 · kg−1) followed a predetermined training program while the flexible-training group (FG; n = 7, VO2max = 53.9 ± 5.0 mL · min−1 · kg−1) had the timing of their HIT sessions prescribed based on the data of the Lamberts and Lambert Submaximal Cycle Test (LSCT).
Effect-size calculations showed large differences in the improvements in 40-km time-trial performance after the HIT training between SG (8 ± 45 s) and FG (48 ± 42 s). Heart-rate recovery, monitored during the study, tended to increase in FG and remain unchanged in SG.
The results of the current study suggest that the LSCT may be a useful tool for coaches to monitor and prescribe HIT.
Jason R. Boynton, Fabian Danner, Paolo Menaspà, Jeremiah J. Peiffer and Chris R. Abbiss
determined using cycling power analytics software (GoldenCheetah, version 3.4, 2016; https://www.goldencheetah.org ). Heart rate recovery (HRR) was calculated as the difference between the maximum HR per interval and the minimum HR in the subsequent 1-minute rest. 24 Rectal temperature ( T C ) and skin
Samuel Ryan, Emidio Pacecca, Jye Tebble, Joel Hocking, Thomas Kempton and Aaron J. Coutts
nonfatiguing assessment of changes in aerobic fitness in team sport athletes. 17 Previous research in professional AF reported a submaximal heart rate recovery (HRR) test to be a valid and reliable measure of training status. 17 However, the capacity of this test to detect changes exceeding the TE has not
Daniel Hammes, Sabrina Skorski, Sascha Schwindling, Alexander Ferrauti, Mark Pfeiffer, Michael Kellmann and Tim Meyer
The Lamberts and Lambert Submaximal Cycle Test (LSCT) is a novel test designed to monitor performance and fatigue/recovery in cyclists. Studies have shown the ability to predict performance; however, there is a lack of studies concerning monitoring of fatigue/recovery. In this study, 23 trained male cyclists (age 29 ± 8 y, VO2max 59.4 ± 7.4 mL · min−1 · kg−1) completed a training camp. The LSCT was conducted on days 1, 8, and 11. After day 1, an intensive 6-day training period was performed. Between days 8 and 11, a recovery period was realized. The LSCT consists of 3 stages with fixed heart rates of 6 min at 60% and 80% and 3 min at 90% of maximum heart rate. During the stages, power output and rating of perceived exertion (RPE) were determined. Heart-rate recovery was measured after stage 3. Power output almost certainly (standardized mean difference: 1.0) and RPE very likely (1.7) increased from day 1 to day 8 at stage 2. Power output likely (0.4) and RPE almost certainly (2.6) increased at stage 3. From day 8 to day 11, power output possibly (–0.4) and RPE likely (–1.5) decreased at stage 2 and possibly (–0.1) and almost certainly (–1.9) at stage 3. Heart-rate recovery was likely (0.7) accelerated from day 1 to day 8. Changes from day 8 to day 11 were unclear (–0.1). The LSCT can be used for monitoring fatigue and recovery, since parameters were responsive to a fatiguing training and a following recovery period. However, consideration of multiple LSCT variables is required to interpret the results correctly.
Ville Vesterinen, Ari Nummela, Sami Äyrämö, Tanja Laine, Esa Hynynen, Jussi Mikkola and Keijo Häkkinen
Regular monitoring of adaptation to training is important for optimizing training load and recovery, which is the main factor in successful training.
To investigate the usefulness of a novel submaximal running test (SRT) in field conditions in predicting and tracking changes of endurance performance.
Thirty-five endurance-trained men and women (age 20–55 y) completed the 18-wk endurance-training program. A maximal incremental running test was performed at weeks 0, 9, and 18 for determination of maximal oxygen consumption (VO2max) and running speed (RS) at exhaustion (RSpeak) and lactate thresholds (LTs). In addition, the subjects performed weekly a 3-stage SRT including a postexercise heart-rate-recovery (HRR) measurement. The subjects were retrospectively grouped into 4 clusters according to changes in SRT results.
Large correlations (r = .60–.89) were observed between RS during all stages of SRT and all endurance-performance variables (VO2max, RSpeak, RS at LT2, and RS at LT1). HRR correlated only with VO2max (r = .46). Large relationships were also found between changes in RS during 80% and 90% HRmax stages of SRT and a change of RSpeak (r = .57, r = .79). In addition, the cluster analysis revealed the different trends in RS during 80% and 90% stages during the training between the clusters, which showed different improvements in VO2max and RSpeak.
The current SRT showed great potential as a practical tool for regular monitoring of individual adaptation to endurance training without time-consuming and expensive laboratory tests.
Benoit Capostagno, Michael I. Lambert and Robert P. Lamberts
Finding the optimal balance between high training loads and recovery is a constant challenge for cyclists and their coaches. Monitoring improvements in performance and levels of fatigue is recommended to correctly adjust training to ensure optimal adaptation. However, many performance tests require a maximal or exhaustive effort, which reduces their real-world application. The purpose of this review was to investigate the development and use of submaximal cycling tests that can be used to predict and monitor cycling performance and training status. Twelve studies met the inclusion criteria, and 3 separate submaximal cycling tests were identified from within those 12. Submaximal variables including gross mechanical efficiency, oxygen uptake (VO2), heart rate, lactate, predicted time to exhaustion (pTE), rating of perceived exertion (RPE), power output, and heart-rate recovery (HRR) were the components of the 3 tests. pTE, submaximal power output, RPE, and HRR appear to have the most value for monitoring improvements in performance and indicate a state of fatigue. This literature review shows that several submaximal cycle tests have been developed over the last decade with the aim to predict, monitor, and optimize cycling performance. To be able to conduct a submaximal test on a regular basis, the test needs to be short in duration and as noninvasive as possible. In addition, a test should capture multiple variables and use multivariate analyses to interpret the submaximal outcomes correctly and alter training prescription if needed.