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.
Sander P.M. Ganzevles, Arnold de Haan, Peter J. Beek, Hein A.M. Daanen and Martin J. Truijens
Hein A.M. Daanen, Robert P. Lamberts, Victor L. Kallen, Anmin Jin and Nico L.U. Van Meeteren
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.
Koen Levels, Lennart P.J. Teunissen, Arnold de Haan, Jos J. de Koning, Bernadet van Os and Hein A.M. Daanen
The best way to apply precooling for endurance exercise in the heat is still unclear. The authors analyzed the effect of different preparation regimens on pacing during a 15-km cycling time trial in the heat.
Ten male subjects completed four 15-km time trials (30°C), preceded by different preparation regimes: 10 min cycling (WARM-UP), 30 min scalp cooling of which 10 min was cycling (SC+WARM-UP), ice-slurry ingestion (ICE), and ice slurry ingestion + 30 min scalp cooling (SC+ICE).
No differences were observed in finish time and mean power output, although power output was lower for WARM-UP than for SC+ICE during km 13–14 (17 ± 16 and 19 ± 14 W, respectively) and for ICE during km 13 (16 ± 16 W). Rectal temperature at the start of the time trial was lower for both ICE conditions (~36.7°C) than both WARMUP conditions (~37.1°C) and remained lower during the first part of the trial. Skin temperature and thermal sensation were lower at the start for SC+ICE.
The preparation regimen providing the lowest body-heat content and sensation of coolness at the start (SC+ICE) was most beneficial for pacing during the latter stages of the time trial, although overall performance did not differ.
Emiel Schulze, Hein A.M. Daanen, Koen Levels, Julia R. Casadio, Daniel J. Plews, Andrew E. Kilding, Rodney Siegel and Paul B. Laursen
To determine the effect of thermal state and thermal comfort on cycling performance in the heat.
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.
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.
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.
Twan ten Haaf, Selma van Staveren, Danilo Iannetta, Bart Roelands, Romain Meeusen, Maria F. Piacentini, Carl Foster, Leo Koenderman, Hein A.M. Daanen and Jos J. de Koning
Purpose: Reaction time has been proposed as a training monitoring tool, but to date, results are equivocal. Therefore, it was investigated whether reaction time can be used as a monitoring tool to establish overreaching. Methods: The study included 30 subjects (11 females and 19 males, age: 40.8 [10.8] years, VO2max: 51.8 [6.3] mL/kg/min) who participated in an 8-day cycling event. The external exercise load increased approximately 900% compared with the preparation period. Performance was measured before and after the event using a maximal incremental cycling test. Subjects with decreased performance after the event were classified as functionally overreached (FOR) and others as acutely fatigued (AF). A choice reaction time test was performed 2 weeks before (pre), 1 week after (post), and 5 weeks after (follow-up), as well as at the start and end of the event. Results: A total of 14 subjects were classified as AF and 14 as FOR (2 subjects were excluded). During the event, reaction time at the end was 68 ms (95% confidence interval, 46–89) faster than at the start. Reaction time post event was 41 ms (95% confidence interval, 12–71) faster than pre event and follow-up was 55 ms faster (95% confidence interval, 26–83). The time by class interaction was not significant during (P = .26) and after (P = .43) the event. Correlations between physical performance and reaction time were not significant (all Ps > .30). Conclusions: No differences in choice reaction time between AF and FOR subjects were observed. It is suggested that choice reaction time is not valid for early detection of overreaching in the field.
Twan ten Haaf, Selma van Staveren, Erik Oudenhoven, Maria F. Piacentini, Romain Meeusen, Bart Roelands, Leo Koenderman, Hein A.M. Daanen, Carl Foster and Jos J. de Koning
To investigate whether monitoring of easily measurable stressors and symptoms can be used to distinguish early between acute fatigue (AF) and functional overreaching (FOR).
The study included 30 subjects (11 female, 19 male; age 40.8 ± 10.8 y, VO2max 51.8 ± 6.3 mL · kg–1 · min–1) who participated in an 8-d cycling event over 1300 km with 18,500 climbing meters. Performance was measured before and after the event using a maximal incremental test. Subjects with decreased performance after the event were classified as FOR, others as AF. Mental and physical well-being, internal training load, resting heart rate, temperature, and mood were measured daily during the event. Differences between AF and FOR were analyzed using mixed-model ANOVAs. Logistic regression was used to determine the best predictors of FOR after 3 and 6 d of cycling.
Fifteen subjects were classified as FOR and 14 as AF (1 excluded). Although total group changes were observed during the event, no differences between AF and FOR were found for individual monitoring parameters. The combination of questionnaire-based changes in fatigue and readiness to train after 3 d cycling correctly predicted 78% of the subjects as AF or FOR (sensitivity = 79%, specificity = 77%).
Monitoring changes in fatigue and readiness to train, using simple visual analog scales, can be used to identify subjects likely to become FOR after only 3 d of cycling. Hence, we encourage athlete support staff to monitor not only fatigue but also the subjective integrated mental and physical readiness to perform.