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Purpose: In long-track speed skating, drafting is a commonly used phenomenon in training; however, it is not allowed in time-trial races. In speed skating, limited research is available on the physical and psychological impact of drafting. The aim of this study was to determine the influence of “skating alone,” “leading,” or “drafting” on physical intensity (heart rate and blood lactate) and perceived intensity (perceived exertion) of speed skaters. Methods: Twenty-two national-level long-track speed skaters with a mean age of 19.3 (2.6) years skated 5 laps, with similar external intensity in 3 different conditions: skating alone, leading, or drafting. Repeated-measures analysis of variance showed differences between the 3 conditions, heart rate (F _2,36 = 10.546, P < .001), lactate (F _2,36 = 12.711, P < .001), and rating of perceived exertion (F _2,36 = 5.759, P < .01). Results: Heart rate and lactate concentration were significantly lower (P < .001) when drafting compared with leading (heart rate Δ = 7 [8] beats·min^–1, 4.0% [4.7%]; lactate Δ = 2.3 [2.3] mmol/L, 28.2% [29.9%]) or skating alone (heart rate Δ = 8 [7.1] beats·min^–1, 4.6% [3.9%]; lactate Δ = 2.8 [2.5] mmol/L, 33.6% [23.6%]). Rating of perceived exertion was significantly lower (P < .01) when drafting (Δ = 0.8 [1.0], 16.5% [20.9%]) or leading (Δ = 0.5 [0.9], 7.7% [20.5%]) versus skating alone. Conclusions: With similar external intensity, physical intensity, as well as perceived intensity, is reduced when drafting in comparison with skating alone. A key finding of this study is the psychological effect: Skating alone was shown to be more demanding than leading, whereas leading and drafting were perceived to be similar in terms of perceived exertion. Knowledge about the reduction of internal intensity for a drafting skater compared with leading or skating alone can be used by coaches and trainers to optimize training conditions.

Purpose: The aim of this brief review was to present an overview of noninvasive markers in trained to professional endurance athletes that can reflect a state of functional overreaching. Methods: A systematic literature search was conducted in the PubMed, Scopus, and PsycINFO databases. After screening 380 articles, 12 research papers were included for the systematic review. Results : Good consensus was found between the different papers in which noninvasive parameters were able to reflect a state of functional overreaching. Changes in power output (PO), heart rate (HR; [sub]maximal and HR recovery), rating of perceived exertion, and scores in the Daily Analysis of Life Demands for Athletes (DALDA) and/or Profile of Mood States (POMS) were shown to be able to reflect functional overreaching, whereas changes in maximal oxygen uptake and HR-variability parameters were not. Conclusion: Functional overreaching within a maximal performance test was characterized by a decrease in peak PO and a lower maximum HR, whereas a lower mean PO and a lower HR were observed during time trials. Changes in parameters during a standardized submaximal test when functionally overreached were characterized by a higher PO at a fixed HR or a lower HR at a fixed intensity, higher rating of perceived exertion, and a faster HR recovery. Although both the DALDA and POMS were able to reflect functional overreaching, the POMS was not able to differentiate this response from acute fatigue, which makes it unsuitable for accurately monitoring functional overreaching.

Background: Training load is typically described in terms of internal and external load. Investigating the coupling of internal and external training load is relevant to many sports. Here, continuous kernel-density estimation (KDE) may be a valuable tool to capture and visualize this coupling. Aim: Using training load data in speed skating, we evaluated how well bivariate KDE plots describe the coupling of internal and external load and differentiate between specific training sessions, compared to training impulse scores or intensity distribution into training zones. Methods: On-ice training sessions of 18 young (sub)elite speed skaters were monitored for velocity and heart rate during 2 consecutive seasons. Training session types were obtained from the coach’s training scheme, including endurance, interval, tempo, and sprint sessions. Differences in training load between session types were assessed using Kruskal–Wallis or Kolmogorov–Smirnov tests for training impulse and KDE scores, respectively. Results: Training impulse scores were not different between training session types, except for extensive endurance sessions. However, all training session types differed when comparing KDEs for heart rate and velocity (both P < .001). In addition, 2D KDE plots of heart rate and velocity provide detailed insights into the (subtle differences in) coupling of internal and external training load that could not be obtained by 2D plots using training zones. Conclusion: 2D KDE plots provide a valuable tool to visualize and inform coaches on the (subtle differences in) coupling of internal and external training load for training sessions. This will help coaches design better training schemes aiming at desired training adaptations.