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Abstract

This study analyses the influence of race category and result on the demands of professional cycling races. In total, 2920 race files were collected from 20 male professional cyclists, within a variety of race categories: Single-day (1.WT) and multi-day (2.WT) World Tour races, single-day (1.HC) and multi-day (2.HC ) Hors Catégorie races and single-day (1.1) and multi-day (2.1) category 1 races. Additionally, the five cycling "monuments" were analysed separately. Maximal mean power outputs (MMP) were measured across a broad range of durations. Volume and load were large to very largely (d = 1.30 – 4.80) higher in monuments compared to other single-day race categories. Trivial to small differences were observed for most intensity measures between different single-day race categories, with only RPE and sRPE·km⁻¹ being moderately (d = 0.70 – 1.50) higher in the monuments. Distance and duration were small to moderately (d = 0.20 – 0.80) higher in 2.WT races compared to 2.HC and 2.1 multi-day race categories with only small differences in terms of load and intensity. Generally, higher ranked races (i.e. Monuments, 2.WT and GT) tend to present with lower shorter-duration MMPs (e.g. 5 to 120 sec) compared to races of “lower rank” (with less differences and/or mixed results being present over longer durations), potentially caused by a “blunting” effect of the higher race duration and load of higher ranked races on short duration MMPs. MMP were small to largely higher over shorter durations (<5min) for a top-10 result compared to no top-10, within the same category.
Submission type: Original Investigation
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Article Title: Demands of Professional Cycling Races: Influence of Race Category and
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Result
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Authors: Teun van Erp and Dajo Sanders
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Full names of the Authors and Institutional/Corporate Affiliations:
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Teun van Erp, Division of Orthopaedic Surgery, Department of Surgical Sciences, Faculty of
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Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa. 2Department of
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Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, The
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Netherlands
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Dajo Sanders, 3Department of Human Movement Science, Faculty of Health, Medicine and Life
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Sciences, Maastricht University, Maastricht, Netherlands
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Corresponding Author: Teun van Erp (teunvanerp@hotmail.com)
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Running head: The Influence of Race Category in Professional Cycling Races
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Keywords: elite, cycling, power output, heart rate, performance
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Abstract word count
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247
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Text-only Word Count
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4500
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Number of tables and figures
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4 Tables and 1 Figure
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Abstract
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This study analyses the influence of race category and result on the demands of professional
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cycling races. In total, 2920 race files were collected from 20 male professional cyclists, within
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a variety of race categories: Single-day (1.WT) and multi-day (2.WT) World Tour races,
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single-day (1.HC) and multi-day (2.HC ) Hors Catégorie races and single-day (1.1) and multi-
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day (2.1) category 1 races. Additionally, the five cycling "monuments" were analysed
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separately. Maximal mean power outputs (MMP) were measured across a broad range of
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durations. Volume and load were large to very largely (d = 1.30 4.80) higher in monuments
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compared to other single-day race categories. Trivial to small differences were observed for
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most intensity measures between different single-day race categories, with only RPE and
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sRPE∙km-1 being moderately (d = 0.70 1.50) higher in the monuments. Distance and duration
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were small to moderately (d = 0.20 0.80) higher in 2.WT races compared to 2.HC and 2.1
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multi-day race categories with only small differences in terms of load and intensity. Generally,
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higher ranked races (i.e. Monuments, 2.WT and GT) tend to present with lower shorter-
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duration MMPs (e.g. 5 to 120 sec) compared to races of “lower rank” (with less differences
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and/or mixed results being present over longer durations), potentially caused by a “blunting”
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effect of the higher race duration and load of higher ranked races on short duration MMPs.
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MMP were small to largely higher over shorter durations (<5min) for a top-10 result compared
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to no top-10, within the same category.
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Introduction
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Alongside technological developments with the use of mobile power meters and heart
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rate (HR) monitors, there has been an increasing amount of research being published in recent
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decades aiming to quantify the training and race characteristics of professional male cyclists
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(1-9). A particular focus within such research (and generally within the endurance sports
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literature), is the quantification of the (training) intensity distribution and exercise load of
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training and racing (10-12). Intensity distributions are typically quantified using time spent in
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pre-defined HR and power output (PO) zones, as well as some research focusing on the
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intensity distribution quantified using subjective measures such as rating of perceived exertion
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(RPE) (10, 13). A 3-zone model has often been used to quantify the intensity distribution with
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the zones anchored around metabolic inflection points such as the first and second lactate or
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ventilatory thresholds (10, 14). In addition, when data on such threshold is not available (or
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data is analyzed retrospectively), studies have also used a 5-zone model based on a percentage
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of maximal HR (HRmax) to quantify intensity distribution (7, 15, 16). Irrespective of the
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methodology used to quantify the intensity distributions, these studies have the overarching
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aim to characterize the (intensity) demands of professional cycling races with the practical aim
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to improve training strategies preparing for such demands. Besides the quantification of
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intensity characteristics, another focus is on the quantification of the overall training or
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competition “dose”, using either internal or external load metrics (depending if we are referring
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to processes measured internal or external to the athlete (17)) (4-6, 18, 19). Load metrics
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typically multiply training or race duration with an intensity measure (i.e. RPE, HR or PO) and
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an intensity-specific weighting factor. Load measures used to quantify the load demands of
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cycling races include HR-based training impulse (TRIMP) metrics (20-23), the PO-based
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Training Stress Score™ (TSS) (24, 25) or the subjectively-based session-RPE (26). Previous
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research has used sRPE, TSS and a TRIMP metric to quantify the load demands of professional
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cycling in a Grand Tour (GT) (6, 15, 19) or to compare male versus female professional cycling
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races (7).
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Functional performance parameters within professional cycling contexts can be assesed
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by evaluating maximal mean POs (MMP) achieved over different durations (i.e typically called
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the “power profile” (PP) (25) or “record power outputs” (27)) in both training and race
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scenarios. Previous research has shown that the PP differs with different rider specialities, (27),
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that it can be used to track improvement of riders across training and competition phases (28),
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and that the MMP “fingerprint” differs between different stage types within a GT (i.e. flat,
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semi-mountain, mountain and time trial) (6). In addition, Menapsa et al. (2017) described the
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differences in MMP achieved during a top-10 and no top-10 result in World Cup races of
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female professional cyclists highlighting the usefullness of such measures in characteristing
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(succes in) road cycling races (29).
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The increasing amount of research papers being published aiming to quantify the
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intensity, load and performance demands of professional cycling races has resulted in an
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improved understanding of the (general) characteristics of professional cycling. However, no
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research has focused on the intensity, load and performance demands of professional male
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cycling races of different "categories" or levels. That is, within professional cycling, races
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recognized by the international cycling federation (i.e. "UCI) are classified into different
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categories such as World Tour Cycling races (.WT), Hors Category (.HC) races, level 1 (.1)
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and level 2 (.2) races. Nevertheless, previous research has either focused solely on the demands
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of GTs (i.e. Giro d’Italia, Tour de France, Vuelta a España) (4-6, 9, 15, 19, 30), demands of
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World Tour cycling races (29) or has used a large database of professional cycling races
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without controlling for race categories (7). Gaining more insight into the intensity, load and
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performance demands of specific categories, and the potential differences between categories
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can provide valuable insight for practitioners and coaches aiming to prepare cyclists for such
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races. Accordingly, this study aims to describe the intensity, load and performance demands of
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professional road cycling races, highlighting the differences between different race categories.
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Secondly, this study aims to assess the differences in PP between achieving a result (i.e. top-
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10) versus no result (i.e. no top-10) within each category.
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Methods
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Participants
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In total, 20 male (mean ± SD: age: 27.5 ± 4.0 yrs, height: 184.8 ± 6.2 cm, bodyweight:
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73.2 ± 7.1 kg) professional cyclists participated in this study. All participants are part of a
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(current) World Tour professional cycling team. Across the time of the data collection, the
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participating cyclists won more than 100 UCI races, whereof 45 victories in the World Tour,
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including 29 stages in a GT. Institutional ethics approval was granted and, in agreement with
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the Helsinki Declaration. Written informed consent was obtained from the participants.
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Research design
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During 4 consecutive years, RPE, HR and PO data were collected during cycling races
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of varying categories. Depending on how long the cyclist was involved in riding for the team,
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the data set of an individual cyclist contains data ranging from 1 to 4 years. If a cyclist was not
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able to ride for a period of 3 months or more, because of illness or an injury, the data set of this
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particular year was excluded. All PO files were visually checked for irregularities (missing or
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erroneous PO or HR) or incomplete data files due to technological issues (e.g. flat battery of
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power-meter or HR monitor) and were excluded
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Race Categories
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Races were classified into different categories according to the international cycling
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federation’s (i.e. UCI) classification system. Races included within the study design were:
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World Tour races (.WT), “Hors Category” (.HC) and category 1 races (.1). Both single-day, as
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well as multi-day stage races, were classified and described using the UCI classification
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system: “1.” races representing single-day races and “2.” representing multi-day races. For
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example, a single-day World Tour race would be classified as “1.WT” whilst a multi-day
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category 1 race will be classified as “2.1”. In addition, from the collected 2.WT races, GT
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stages were analysed separately. Lastly, we also separately classified five single-day World
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Tour races known as cycling “monuments”; considered to be the oldest and most prestigious
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single-day events in road cycling, namely: Milan-San Remo, Tour of Flanders, Paris-Roubaix,
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Liege-Bastogne-Liege and Giro Di Lombardia. Data was excluded when a cyclist did not finish
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the race.
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Race characteristics
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Intensity distribution was quantified by measuring the time spent in five pre-
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determined HR and PO zones. Due to the fact that this was a retrospective analysis no
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regularly assessed test data was present on metabolic inflection points (i.e. lactate or
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ventilatory thresholds) for the riders to anchor the zones around, which would be considered
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the preferred approach (14). Hence, we anchored zones based on time spent in five HR zones
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based on the percentage of HRmax and five PO zones based on the percentage of functional
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threshold power (FTP), which both were available for each rider within the analyzed dataset.
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Similar to previous research (7, 16, 21), HR zones were classified as followed: zone 1: 50-
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59% HRmax, zone 2: 60-69% HRmax, zone 3: 70-79% HRmax, zone 4: 80-89% HRmax, zone 5:
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90-100%. HRmax was defined as the highest HR achieved by the cyclist during training or
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competition of the analyzed season and adjusted every season (if needed). The five PO zones
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were based on a percentage of FTP based on guidelines provided by Coggan (31): zone 1:
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≤55% of FTP, zone 2: 56-75% FTP, zone 3: 76-90% FTP, zone 4: 91-105% FTP, zone 5:
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≥106% FTP. Per season, FTP was determined as 95% of the highest 20 min MMP, either
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achieved during a specific 20min time trial in training or adjusted when the mean maximal 20
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min PO was higher during a race. Within the team it was common to perform a maximal
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20min test in the pre-season (January), therefore the FTP was in most cases determined based
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on this test. However, on some occasions it occurred that riders achieved a higher 20min
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value during the competitive season. It is known that the FTP can vary with 0.4 W∙kg-1 per
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year (32) and thus the FTP could either be on under or overestimated in certain periods which
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could lead to inaccuracies with the determination of TSS and PO-based intensity zones.
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Competition load was calculated using different methods based on either HR, PO or
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RPE: Edwards’ TRIMP (TRIMP) (21), TSS (31) and sRPE (26). TRIMP was calculated
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based on the time spent in the five pre-defined HR zones described above and multiplied by a
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zone-specific arbitrary weighting factor (zone 1: weighting factor = 1, zone 2: weighting
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factor = 2, zone 3: weighting factor = 3, zone 4: weighting factor = 4, zone 5: weighting
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factor = 5) and then summated to provide a total TRIMP score (21). TSS was calculated
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based on power data collected with portable power meters (SRM, Jülich, Welldorf, Germany
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and Pioneer, Kawasaki, Japan). The SRM power meters were calibrated by a static calibration
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once a year (in the pre-season) by the manufacturer. A similar procedure was performed prior
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to the use of the Pioneer power meters, and these were subsequently only in use for one year.
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TSS was calculated according to Coggan (31), using the following formula:
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TSS = [ (t x NP™ x IF™) / (FTP x 3600) ] x 100
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where t is the duration of the exercise bout in seconds, NP™ is normalized power of the
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exercise bout (31), and IF™ is an intensity factor which is the ratio between the NP of the
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exercise bout and the individual’s FTP (31). All riders were informed about the importance of
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the zero-calibration of the power meter and were instructed to do the zero-calibration before
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every ride. Both TRIMP and TSS have previously been shown to have a strong dose-response
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validity with changes in fitness in competitive road cyclists (18). sRPE was calculated using
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the post-exercise RPE rating on a 6-20 scale and session duration. The RPE was obtained after
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the race, using a self-filled in logbook, based on the question: "How hard was your workout?".
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Even though the general recommendation is to obtain a RPE score within 30 minutes of each
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competition, the time between the end of the race and the cyclist filling in the RPE score could
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have been longer in this study (~15 hours). However, it has been previously shown that
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athletes are able to accurately recall RPE scores for a particular training or competition when
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done within 48 hours (33, 34). The exercise load for the session was then quantified by
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multiplying the RPE by the duration of the session (minutes) (26). Similar to previous research
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(35), all load metrics (TRIMP, TSS and sRPE), as well as total work performed (kJ spent),
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were also expressed relatively as load per km (i.e. TRIMP∙km-1, TSS∙km-1, sRPE∙km-1 and kJ
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spentkm-1).
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Power Profile
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To assess the performance characteristics of the different races categories, the PP (i.e.
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MMP over a variety of durations) was assessed for each race. In this study, the PP corresponds
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to the MMP for the durations of 5, 10, 30 seconds and 1, 2, 5, 10, 20, 60 and 180 minutes,
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achieved within the respective race. All are presented as absolute PO and relative to the body
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mass of the cyclist (27). To assess the differences in performance characteristics between being
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successful and not successful, the PP within each category was compared between a race
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where a top-10 result was achieved and a race where no top-10 result was achieved.
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Statistical Analysis
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Descriptive results are presented as mean ± standard deviation. Prior to analysis, the
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assumption of normality was verified by using Shapiro-Wilk W test and by visual inspection
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of QQ plots. The intensity and load variables were compared between categories using a
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multilevel random intercept model using Tukey’s method for pairwise comparisons in R (R: A
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Language and environment for statistical computing, Vienna, Austria). The intensity and load
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measures were fitted as the response variables with each race category included as a categorical
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fixed effect. Random effect variability was modelled using a random intercept for each
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individual participant. This approach was chosen as it offered a better fit to the dataset given
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the fact that multilevel models handle an uneven sample size better and don't assume
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independence amongst observations (36). A similar approach (i.e. using a random intercept for
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each individual) was used to compare a top-10 with a non-top-10 finish within each race
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category. Level of significance was established at P < 0.05. Standardized effect size is reported
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as Cohen's d, using the pooled standard deviation as the denominator. Qualitative interpretation
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of d was based on the guidelines provided by Hopkins et al. (37): 0 - 0.19 trivial; 0.20 0.59
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small; 0.6 1.19 moderate; 1.20 1.99 large; ≥ 2.00 very large.
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Results
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In total, 2920 race files were collected and analyzed from which 322 single-day races
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including: 39 monuments, 89 1.WT races, 64 1.HC races and 130 1.1 races and 2598 multi-
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day races including: 843 GT stages, 808 2.WT stages, 552 2.HC stages and 395 2.1 stages. All
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collected race files contained PO data, whereas 1703 (58%) of the training files contained HR
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data. From 1074 (37%) files, RPE-scores were reported after the race.
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Volume, load, and intensity measures of the single-day races are presented in Table 1
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and differentiated between the different single-day race categories. Volume (i.e. distance and
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duration) and load (i.e. kJ spent, TSS, TRIMP and sRPE) values were large to very largely (d
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= 1.30 - 4.80) higher in the monuments compared to the other single-day race categories. Most
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intensity measures showed trivial to small (d < 0.60) differences between different single-day
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race categories. Only, %HRmax was moderately (d = 0.67 0.72) lower in 1.WT races compared
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to Monuments and 1.1 races and RPE and sRPE∙km-1 were moderate to largely higher in the
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monuments compared to the other single-day race categories (d = 0.70 1.50). Lastly, mean
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PO was moderately (d = 0.63) higher in 1.1 races compared to 1.WT races. Figure 1 (A, B, C
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and D) graphically displays the differences in the absolute and percentage time spent in the five
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different HR and PO intensity zones when competing in the different single-day race
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categories.
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Volume, load, and intensity measures of the multi-day races are presented in Table 2
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and differentiated between the different multi-day race categories. Volume (i.e. distance and
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duration) were small to moderately higher (d = 0.20 - 0.80) in 2.WT (incl. GTs) races compared
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to the 2.HC and 2.1 multi-day race categories. Small differences (d < 0.60) were reported
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between the different multi-stage race categories in terms of total competition load (i.e. TSS,
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TRIMP, sRPE) or load expressed per km. Moderately lower (d = 0.72 0.96) HR-based
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intensity (i.e. Mean HR and Mean HR as %HRmax) was observed in GT stages compared to
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2.HC and 2.1 stages whilst these differences were small (d < 0.60) when compared to 2.WT
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races. Figure 1 (E, F, G and H) graphically displays the differences in absolute and percentage
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of time spent in the different HR and PO intensity zones when competing in the different multi-
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day race categories.
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The absolute and relative MMP achieved within the different race categories are
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presented in Table 3 and are differentiated between the single-day and multi-day races and the
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different race categories. For single-day races, absolute (W) and relative (W∙kg-1) longer
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duration POs (i.e. > 20minutes) were small to largely (d = 0.201.20) higher in the monuments
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compared to other single-day categories races while the shorter duration POs (i.e.
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<120seconds) were small to largely (d = 0.20 - 1.00) lower for the monuments. Small to
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moderately (d = 0.40 - 0.70) lower numbers were observed for shorter duration POs (i.e. 15 to
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120seconds) when competing in the WT (GT and 2.WT) compared to the 2.HC and 2.1 race
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categories.
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Table 4 presents the absolute and relative PP for each category (single-day and multi-
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day combined), filtered for the fact if the rider reached a top-10 or no top-10 results. The main
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difference between a top-10 and no top-10 result is, regardless of the category, that MMP are
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small to largely (d = 0.34 1.66) higher over the shorter durations (<5min) and trivial to small
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(d < 0.36) differences were found for MMP >10min.
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Discussion
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This study is the first to describe the intensity and load demands of professional male
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road cycling races in different race categories. For the single-day races, the main difference
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was the volume and load between the different race categories whilst smaller differences were
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found in terms of intensity measures. Furthermore, the differences observed between the
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volume, load and intensity characteristics within the multi-day race categories were small,
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besides the lower HR-based intensity responses observed in GT stages compared to other multi-
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day race categories. Additionally, the effect of race category on MMP over different durations
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has also been presented with some distinct differences between race categories being present
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(i.e. higher level races typically present with lower shorter duration MMP). Furthermore, this
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study provides a first indication on what it takes to be “sucessfull” in different race categories
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by showing the differences in MMP between a top-10 and no top-10 result. That is, for all
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categories, absolute and relative MMP were higher over shorter durations (< 5min) for a top10
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compared to no top-10 result.
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It was expected that higher-ranked races (i.e. monuments and 1.WT) will have a higher
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volume and load as different race regulations apply to the different single-day race categories.
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Namely, the monuments and 1.WT are the only race categories with no maximum distance set
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by the UCI (i.e. distance determined by the professional cycling council). This is in contrast
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to the 1.HC and 1.1 race categories which have a maximum distance of 200km. Cyclists that
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want to be competitive in one of the monuments should be capable to sustain ~20% more load
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compared to any other single-day professional cycling event. Whilst volume and load differs
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between the single-day race categories, it seems that there are no major differences between
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most (objective) intensity measures (i.e. mean HR, intensity factor, kJ spent∙km-1, TSS∙km-1,
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TRIMP∙km-1 and percentage of time spent in PO and HR intensity zones) of the different
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single-day race categories with only mean HR as %HRmax being moderately (d = 0.67 0.72)
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lower in 1.WT races compared to monuments and 1.1 races. Nevertheless, whilst the
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percentage of time spent in PO and HR zones may not be different, the absolute time spent (i.e.
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minutes) in intensity zones (PO and HR) is higher in higher ranked races (e.g. 1.WT vs 1.HC
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and 1.1) due to the higher overall race duration. Therefore, whilst percentage-wise two of these
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races might be similar, it’s likely that for a higher-ranked race a rider should be able to sustain
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a significantly longer amount of minutes in high-intensity zones compared to a lower ranked
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(single-day) race. Interestingly, from all intensity measures, only subjective measures seem to
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differ between the different single-day race categories with RPE and sRPE∙km-1 being
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moderate to largely higher in the monuments compared to the other single-day race categories
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(d = 0.70 1.50). As previously suggested, distinct differences can be present when using
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subjective (i.e. RPE) and objective (i.e. HR and power output) intensity measures in road
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cyclists (13).
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Although some significant differences between the different multi-day race categories
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were observed, there are no large differences between the volume, load and intensity
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characteristics of the different multi-day race categories. In contrast to the race regulations of
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a single-day race, the race regulations for a multi-day race are the same for the different
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categories. The maximum average distance is 180 km and the maximum distance for a stage is
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240 km. Only, moderate differences are present in terms of HR-based intensity measures
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between categories showing lower (d = 0.72 0.96) mean HR and mean HR as %HRmax in GT
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stages compared to 2.HC and 2.1 stages. This can most likely be explained by the typically
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observed suppressed HR response towards the end of a GT caused by the accumulated fatigue
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and high cumulative load of a GT (19, 38, 39). When comparing single-day races with multi-
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day races, it is clear that for all the race categories the single-day races are higher in volume,
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load and intensity compared to the multi-day races. Race regulations are an important
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contributor to this. Volume and load are higher competing in single-day races because race
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regulations allow longer races within all the single-day race categories compared to the multi-
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day race categories. Furthermore, the higher intensities within the single-day races could be
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caused by differences in race tactics between the single-day and multi-day races. In a single-
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day race, a cycling team has one goal and that is to finish as high as possible and thus the whole
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team (race leader and domestiques) will work without any necessity to hold back for other days
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to come. Within a multi-day race, a team has different goals per stage and this will depend on
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their overall goal. For example, when a team brings a sprinter as a team leader to a multi-day
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race, on the flat stages the support riders will likely have to work on the front of the peloton
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which will result in an increased exercise intensity and load whilst the support riders for a
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climber will have a higher exercise load on the climbing stages when working for their leader.
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Overall race length (i.e. number of stages) can be a cause for the slightly higher intensity
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measures (absolute and relative PO, IF) in the 2.1 race category compared to higher level multi-
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day stage races. On average, the lower category races are shorter and some have only two-race
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days. The more days a multi-day race consists of, the more riders will most likely aim to spread
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their energy over multiple days (and aim to minimise energy expenditure on days where it’s
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possible).
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The race category also seems to influence the PP and different race categories seem to
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present with a different PP “fingerprint”. For example, Monuments present with higher long-
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duration MMP (≥10min) whilst lower ranked single-day races like 1.1 races tend to present
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with higher MMP over shorter-durations (<2min). Generally, it was observed that higher
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ranked races (i.e. Monuments, 2.WT and GT) tend to present with lower shorter-duration
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MMPs (e.g. 5sec to 2min) compared to other categories with less differences and/or mixed
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results being present over longer durations. Whilst this trend seems to be apparent, it is
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important to acknowledge that this data just provide just a summary/mean value for the
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“whole” category and distinct differences can obviously still exist between different races. For
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example, there will still be races where a single-day WT race will result in higher MMP over
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shorter durations compared to another 1.1 level race. Nevertheless, in general, it seems that the
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higher level races tend to have lower MMPs over shorter durations. A multitude of factors can
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contribute to this, such as: the higher race duration, load and race length (i.e. number of stages
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within a multi-day race) of higher ranked races compared to lower-level races may have a
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blunting effect" on shorter-duration MMPs and may put a tendency to increased importance
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of longer-duration power outputs; higher total elevation gain in higher-level races contribute
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to higher MMP over long duration due to increased proportion of (longer) climbs. Furthermore,
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as mentioned above, tactical reasons will also largely contribute to differences in the PP of the
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races.
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These results also indicate that achieving a top-10 in a professional cycling race is
351
largely determined by shorter duration absolute and relative MPP (<5min) as higher POs over
352
those durations were observed for a top-10 result (compared to no top-10) for all race
353
categories. This is in line with Menaspa et al. who also showed that the main difference between
354
a non-top 10 and a top-10 result in World Cup competitions in female professional cyclists is
355
to be found in the shorter-duration POs (<5min), albeit they also found significant differences
356
for 5min and 10min MMP (29). Noteworthy, however, is the fact this is also largely determined
357
by race profile and tactics (e.g. sprint finish vs mountain stage). Particularly, the data analysed
358
in this study is from one team which achieved great success with sprinters therefore a larger
359
proportion of top-10 results where achieved with sprint stages. Hence, this will be a
360
contributing factor to the fact that the shorter-duration POs are higher for the top-10 places in
361
our study. Future research should evaluate the MMPs for riders being “successful” in different
362
stage types (e.g. sprint finish vs mountain stage)
363
This was a retrospective study where a large dataset of ~3000 races within professional
364
cyclists was analysed. The data was mainly collected for monitoring the cyclists and secondly
365
for the purpose of this study therefore there are some limitations. During the time of data
366
collection, no regular laboratory testing was performed within the team. Thus it was not
367
possible to anchor the HR and PO zones around physiological thresholds determined by
368
laboratory testing, which would be ideal. Further, the FTP is determined once per year although
369
it seems that FTP can variate with 0.4 W∙kg-1 per year (32) and therefore the FTP could either
370
be on under or overestimated in certain periods which could lead to inaccuracies with the
371
determination of TSS (TSS∙km-1) and the PO zones. Furthermore, all cyclists and mechanics
372
were aware of the importance of the zero-offset and were instructed to perform the zero-
373
calibration before every race, however, this was not controlled. Lastly, there are some inherent
374
limitations with the metrics used in this study that are important to consider with the
375
interpretation of this data, namely: i) PO based load metrics like TSS don’t take the added
376
physical stress of environmental factors (e.g. altitude) in to account; and ii) the influence of a
377
variety of factors on HR measurements, particularly the well-known “blunting” effect of
378
accumulating fatigue on the HR response,(19, 39) will impact the HR-based intensity and load
379
metrics incorporated within this study.
380
381
382
383
Conclusions
384
To conclude, this study is the first to show the differences in volume, intensity and load
385
characteristics between different race categories of professional cycling. The main differences
386
in single-day race categories seem to exist in terms of volume (i.e. duration and distance) and
387
load (e.g. TSS, TRIMP) whilst smaller differences being observed in terms of exercise
388
intensity. For multi-day races, smaller differences were observed for volume, intensity, and
389
load between different race categories with the main differences being lower HR-based
390
intensity measures during GT stages. When evaluating the performance characteristics of the
391
different categories, higher ranked races (like Monuments, GT stages and 2.WT stages) tend
392
to present with lower MMP over shorter durations, potentially caused by a “blunting” effect of
393
overall race duration and load on shorter-duration MMPs. However, within all categories,
394
absolute and relative MMP are higher over shorter durations (e.g. 5sec to 2min) for a top-10
395
result compared to no top-10, providing some evidence on what it takes to be “successful” in
396
races of varying categories.
397
398
Acknowledgments
399
We would like to thank the cyclists for their participation in this investigation.
400
401
Disclosure statement
402
No potential conflicts of interest were reported by the authors.
403
404
405
406
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407
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511
512
513
514
515
Figure 1: Absolute (A, B) and % (C, D) intensity distribution in 5 different PO (A, C) and HR
516
(B, D) zones during different single-day race categories in professional cycling road races and
517
absolute (E, F) and % (G, H) intensity distribution in 5 different PO (E, G) and HR (F, H) zones
518
during different multi-day race categories in professional cycling road races. PO indicates
519
Power Output; HR indicates Heart Rate; 1.WT.m indicates the Monuments (namely: Milan-
520
San Remo, Tour of Flanders, Paris-Roubaix, Liege-Bastogne-Liege and Giro Di Lombardia);
521
WT indicates World Tour; 1.HC indicates Hors Category; .1 indicate .1 level; 1. races
522
representing single-day races and 2. representing multi-day races.
523
524
525
Figure 2: Absolue (A, B, E and F) and relative (C, D, G, H) power profiles for single-day and
526
multi-day races categorised based on top-10 or no top-10. Top-10 races are classified as
527
sprinter and mountain based on the rider speciality. *Significant difference between TOP-10
528
sprint, TOP-10 mountain and no top-10. Significant difference (P < 0.05).
529
Table 1: Volume, load and intensity descriptors of one-day races differentiated between the
different race categories within professional male road cycling. Values are mean (SD).
Monuments
(n=39)
1.WT
(n=89)
1.1
(n=130)
Distance (km)
268 (19.5)
219 (25.5)1
187 (36.0)1,2
Duration (min)
406 (28.3)
332 (40.1)1
285 (34.8)1,2
Mean PO (W)
236 (26.4)
223 (29.9)
240 (27.9)2
Mean PO (W∙kg-1)
3.32 (0.32)
3.10 (0.44)1
3.22 (0.39)2,3
Intensity Factor™
0.77 (0.06)
0.76 (0.07)
0.79 (0.07)
Mean HR (beats∙min-1)
144 (10)
139 (12)
144 (8)2
Mean HR (%HRmax)
75.0 (4.5)
71.5 (5.2)
74.6 (4.1)2
Peak HR
181 (9)
184 (24)
185 (18)
Mean RPE (AU)
18.2 (1.5)
16.9 (1.8)1
16.1 (1.7)1,2
Total Elevation Gain
2381 (1300)
2098 (788)
1239 (821)1,2
Total work (kJ)
5756 (625)
4490 (739)1
4134 (589)1,2
TSS (AU)
402 (51.4)
324 (68.6)1
298 (57.8)1,2
TRIMP (AU)
1284 (135)
949 (212)1
891 (146)1
sRPE (AU)
7480 (688)
5646 (861)1
4633 (817)1,2
kJ spent∙km-1 (AU)
21.5 (2.7)
20.5 (2.8)
21.3 (2.9)2,3
TSS∙km-1 (AU)
1.51 (0.23)
1.48 (0.28)
1.53 (0.27)3
TRIMP∙km-1 (AU)
4.79 (0.78)
4.32 (0.83)
4.58 (0.69)
sRPE∙km-1 (AU)
27.8 (2.67)
25.8 (3.21)1
24.3 (3.39)1,2
Abbreviations: PO, Power Output; HR, Heart Rate; RPE, Rating of Perceived Exertion; TSS, Training Stress;
TRIMP, Edwards’s Training Impulse; sRPE, session Rating of Perceived Exertion; WT, World Tour; 1.HC,
Hors Category; 1.1, .1 level. Monuments: Milan-San Remo, Tour of Flanders, Paris-Roubaix, Liege-
Bastogne-Liege and Giro Di Lombardia. Significant difference (P < 0.05). 1Significantly different from
Monuments, 2significantly different from 1.WT, 3significantly different from 1.HC.
530
531
532
533
534
535
536
537
538
539
540
541
542
Table 2: Volume, load and intensity descriptors of multi-day races differentiated between the
different race categories within professional male road cycling. Values are mean (SD).
Grand Tours
(n = 843)
2.WT
(n = 808)
2.HC
(n = 552)
2.1
(n = 395)
Distance (km)
182 (40.2)
178 (36.1)
169 (32.0)1,2
170 (37.2)1,2
Duration (min)
300 (52.6)
285 (55.4)1
260 (51.1)1,2
263 (44.7)1,2
Mean PO (W)
212 (34.8)
215 (29.9)
211 (33.5)2
230 (30.5)1,2,3
Mean PO (W∙kg-1)
2.89 (0.44)
3.00 (0.42)1
2.82 (0.46)2
3.07 (0.41)1,2,3
Intensity Factor™
0.71 (0.07)
0.72 (0.07)
0.72 (0.08)
0.75 (0.07)1,2,3
Mean HR (beats∙min-1)
125 (11.7)
132 (10.7)1
133 (10.4)1
136 (10.1)1,2
Mean HR (%HRmax)
65.8 (5.65)
68.8 (4.92)1
70.2 (5.07)1
70.2 (4.58)1,2
Peak HR
172 (20.5)
178 (13.6)1
180 (14.4)1
179 (19.2)1
Mean RPE (AU)
14.5 (2.60)
15.5 (1.93)1
14.9 (1.90)2
15.2 (1.88)
Total Elevation Gain
2207 (1125)
1993 (1011)1
1310 (912)1,2
1298 (908)1,2
Total work (kJ)
3851 (951)
3681 (842)1
3289 (812)1,2
3628 (744)1,2,3
TSS (AU)
254 (63.0)
250 (60.5)
228 (63.9)1,2
248 (58.3)3
TRIMP (AU)
688 (203)
727 (173)
694 (178)2
731 (171.5)1,3
sRPE (AU)
4380 (1164)
4425 (1068)
3932 (947)1,2
4032 (981)1,2
kJ spent∙km-1 (AU)
20.9 (4.86)
20.5 (3.61)
19.3 (3.47)1,2
20.7 (3.05)3
TSS∙km-1 (AU)
1.38 (0.34)
1.39 (0.29)
1.34 (0.31)1,2
1.42 (0.28)3
TRIMP∙km-1 (AU)
3.73 (1.09)
4.07 (0.89)1
4.09 (0.86)1
4.17 (0.74)1
sRPE∙km-1 (AU)
23.6 (5.21)
24.7 (4.30)
22.9 (3.77)1,2
22.9 (3.93) 2
Abbreviations: PO. Power Output; HR. Heart Rate; RPE. Rating of Perceived Exertion; TSS. Training Stress;
TRIMP. Edwards’s Training Impulse; sRPE. session Rating of Perceived Exertion. 2.WT, World Tour races
(excluded Grand Tours); GT, Grand Tours; 2.HC, Hors Category; 2.1, .1 level. Significant difference (P <
0.05). 1Significantly different from Grand Tour stages, 2significantly different from 2.WT, 3significantly
different from 2.HC.
543
544
545
546
547
548
549
550
551
552
553
554
555
Table 3: Maximal mean power outputs over different durations differentiated between the different race categories (one-day and multi-day) within professional road cycling.
Values are mean (SD).
Category
5 sec
10 sec
30 sec
1 min
2 min
5 min
10 min
20 min
60 min
180 min
Single-
day races
Monuments
PO (W)
1237 (351)
890 (131)
628 (83)
522 (47)
462 (38)
417 (36)
378 (29)
344 (28)
302 (28)
272 (29)
PO (W∙kg-1)
17.2 (3.8)
12.5 (1.2)
8.8 (1.0)
7.3 (0.6)
6.5 (0.5)
5.9 (0.5)
5.3 (0.5)
4.8 (0.4)
4.2 (0.3)
3.8 (0.3)
1.WT
PO (W)
1187 (179)1
906 (125)
656 (72)1
561 (61)1
481 (45)
406 (38)
358 (31)1
325 (31)1
289 (30)
255 (30)1
PO (W∙kg-1)
16.3 (1.7)1
12.5 (1.4)
9.1 (1.0)
7.7 (0.8)1
6.6 (0.6)
5.6 (0.6)
4.9 (0.5)1
4.5 (0.5)1
4.0 (0.4)
3.5 (0.5)1
1.HC
PO (W)
1192 (197)1
913 (161
673 (97)
562 (62)1
480 (51)
399 (39)1
359 (34)1
330 (35)
288 (33)1
245 (31)1
PO (W∙kg-1)
16.2 (1.9)1
12.4 (1.5)
9.2 (1.0)
7.7 (0.8)1
6.6 (0.7)
5.5 (0.6)1
4.9 (0.5)1
4.5 (0.5)
3.9 (0.4)1
3.4 (0.4)1
1.1
PO (W)
1255 (225)
988 (176)2,3
719 (120)1,2,3
588 (82)1,2,3
491 (49)1
410 (35)
368 (34)2
336 (32)2
296 (29)
261 (28)3
PO (W∙kg-1)
16.5 (1.9)
13.0 (1.6)2,3
9.5 (1.0)1,2
7.8 (0.7)1
6.5 (0.5)1
5.5 (0.6)
4.9 (0.6)2
4.5 (0.6)2
3.9 (0.5)
3.5 (0.4)3
Multi-
day races
GT
PO (W)
1134 (217)
838 (161
614 (108)
519 (75)
459 (57)
404 (47)
370 (45)
337 (44)
285 (41)
238 (37)
PO (W∙kg-1)
15.4 (2.3)
11.4 (1.7)
8.4 (1.2)
7.1 (0.8)
6.3 (0.6)
5.5 (0.5)
5.0 (0.6)
4.6 (0.6)
3.9 (0.5)
3.2 (0.5)
2.WT
PO (W)
1118 (180)
842 (135)
613 (95)
516 (67)
456 (47)
402 (40)
368 (39)
333 (38)
280 (36)
235 (31)
PO (W∙kg-1)
15.5 (1.7)
11.7 (1.5)1
8.5 (1.1)
7.2 (0.8)
6.4 (0.6)
5.6 (0.5)
5.1 (0.5)
4.6 (0.5)
3.9 (0.5)
3.3 (0.4)
2.HC
PO (W)
1189 (212)1,2
903 (184)1,2
658 (119)1,2
547 (79)1,2
472 (56)1
405 (43)
360 (42)1,2
320 (41)1,2
267 (40)1,2
225 (35)1,2
PO (W∙kg-1)
15.8 (1.9)1,2
12.0 (1.9)1,2
8.8 (1.3)1,2
7.3 (0.8)1,2
6.3 (0.6)
5.4 (0.6)
4.8 (0.6)1,2
4.3 (0.6)1,2
3.6 (0.5)1,2
3.0 (0.5)1,2
2.1
PO (W)
1216 (216)1,2,3
931 (175)1,2,3
678 (122)1,2,3
568 (86)1,2,3
487 (59)1,2,3
415 (44)1,3
372 (39)3
334 (36)3
284 (35)3
244 (32)3
PO (W∙kg-1)
16.1 (1.9)1,2,3
12.4 (1.7)1,2,3
9.0 (1.3)1,2
7.6 (0.9)1,2,3
6.5 (0.6)1,2,3
5.5 (0.5)1,3
5.0 (0.5)3
4.5 (0.5)1,2,3
3.8 (0.5)3
3.2 (0.4)3
Abbreviations: PO: Power Output; Monuments, Milan-San Remo, Tour of Flanders, Paris-Roubaix, Liege-Bastogne-Liege and Giro Di Lombardia; WT: World Tour; 1.HC: one-day Hors Category;
1.1: one-day .1; GT; Grand Tours: 2.HC: multi-day Hors Category; 2.1: multi-day .1.
Significant difference (P < 0.05). 1 Different from Monuments or GT, 2 Different from 1.WT/2.WT, 3 Different from 1.HC/2.HC
556
557
558
559
560
561
562
563
564
565
566
Table 4: Mean maximal power outputs over different durations differentiated between a top-10 or no top-10 result achieved within the different race categories (one-day and
multi-day combined) within professional road cycling. Values are mean (SD).
Category
5 sec
10 sec
30 sec
1 min
2 min
5 min
10 min
20 min
60 min
180 min
GT T10
(n=59)
PO (W)
1317 (293)
1079 (131)
764 (172)
609 (118)
511 (81)
428 (46)
384 (40)
344 (41)
288 (35)
240 (39)
PO (W∙kg-1)
17.5 (2.9)
14.3 (2.4)
10.2 (1.4)
8.1 (0.6)
6.8 (0.6)
5.8 (0.6)
5.2 (0.7)
4.7 (0.8)
3.9 (0.7)
3.3 (0.7)
GT N-T10
(n=784)
PO (W)
1121 (204)*
820 (133)*
603 (92)*
513 (66)*
455 (52)*
403(46)*
369 (45)
336 (45)
285 (41)
237 (37)
PO (W∙kg-1)
15.2 (2.2)*
11.2 (1.4)*
8.2 (1.1)*
7.0 (0.8)*
6.2 (0.6)*
5.5 (0.5)*
5.0 (0.5)*
4.6 (0.5)
3.9 (0.5)
3.2 (0.5)
WT T10
(n=64)
PO (W)
1193 (205)
940 (190)
704 (136)
578 (96)
495 (57)
414 (38)
372 (37)
329 (34)
274 (34)
231 (31)
PO (W∙kg-1)
16.5 (1.6)
13.0 (1.7)
9.7 (1.3)
8.0 (0.9)
6.9 (0.6)
5.8 (0.6)
5.2 (0.6)
4.6 (0.6)
3.8 (0.5)
3.2 (0.5)
WT N-T10
(n=870)
PO (W)
1125 (190)*
843 (128)*
611 (86)*
516 (62)*
456 (45)*
402 (39)*
367 (38)
333 (37)
282 (35)
239 (33)
PO (W∙kg-1)
15.6 (1.9)*
11.7 (1.4)*
8.5 (1.1)*
7.2 (0.8)*
6.4 (0.6)*
5.6 (0.5)*
5.1 (0.5)
4.6 (0.5)
3.9 (0.5)
3.3 (0.4)
HC T10
(n=70)
PO (W)
1381 (208)
1112 (233)
798 (133)
637 (79)
518 (59
425 (40)
369 (40)
323 (40)
270 (36)
228 (34)
PO (W∙kg-1)
17.5 (1.6)
14.1 (2.1)
10.2 (1.3)
8.1 (0.8)
6.6 (0.6)
5.4 (0.5)
4.7 (0.6)
4.1 (0.6)
3.5 (0.5)
2.9 (0.5)
HC N-T10
(n=546)
PO (W)
1165 (198)*
877 (156)*
642 (102)*
537 (70)*
467 (53)*
402 (42)*
359 (41)
321 (40)
269 (40)
227 (35)
PO (W∙kg-1)
15.6 (1.8)*
11.8 (1.6)*
8.6 (1.1)*
7.2 (0.8)*
6.3 (0.6)*
5.4 (0.6)
4.9 (0.6)
4.3 (0.6)
3.6 (0.5)
3.1 (0.5)
Cat.1 T10
(n=74)
PO (W)
1404 (225)
1136 (218)
832 (149)
661 (104)
537 (65)
426 (39)
370 (34)
329 (32)
283 (31)
246 (29)
PO (W∙kg-1)
17.7 (1.5)
14.3 (1.8)
10.5 (1.3)
8.3 (0.9)
6.8 (0.5)
5.4 (0.5)
4.7 (0.5)
4.2 (0.5)
3.6 (0.5)
3.1 (0.5)
Cat.1 N-T10
(n=451)
PO (W)
1195 (204)*
913 (146)*
664 (100)*
558 (72)*
480 (51)*
412 (42)
371 (39)
335 (36)
288 (35)
248 (32)
PO (W∙kg-1)
16.0 (1.8)*
12.2 (1.5)*
8.9 (1.1)*
7.5 (0.8)*
6.4 (0.6)*
5.5 (0.5)
5.0 (0.5)
4.5 (0.5)*
3.9 (0.5)
3.3 (0.4)
Abbreviations: GT, Grand Tour stages; WT, World Tour races; HC, Hors Category races; Cat.1, Category 1 races; T10, races where a top-10 result was achieved; NT-10, races where no top-10
result was achieved. Significant difference (P < 0.05). *Different from T10 (within the same category).
... To the best of the authors' knowledge, only Menaspa et al 5 presented the power profiles of female professional cyclists. The power profile of male cyclists have often been described in literature 3,11,16,17 and is influenced by race type (ie, flat, semimountain, mountain), 13,18,19 race level, 17 and number of race days. 3 Most studies have presented the mean power profile of a group (or team) of cyclists, and as such, the data do not always present the full picture of the race winning effort exerted in a particular race. ...
... To the best of the authors' knowledge, only Menaspa et al 5 presented the power profiles of female professional cyclists. The power profile of male cyclists have often been described in literature 3,11,16,17 and is influenced by race type (ie, flat, semimountain, mountain), 13,18,19 race level, 17 and number of race days. 3 Most studies have presented the mean power profile of a group (or team) of cyclists, and as such, the data do not always present the full picture of the race winning effort exerted in a particular race. ...
... 3,18 For example, the average 20-minute MMP reported in a mountain stage is 5.1 W·kg −1 ; however, several case studies have clearly indicated that higher values are needed to be successful in a mountain stage. 14,20 However, comparing power profiles of successful (ie, TOP20, TOP10, or TOP5 finishes) with not successful races 5,17,21 can provide valuable information regarding "race winning effort." These comparative studies have shown that differences between successful and not successful races in both male 17 and female 5 cyclists are mainly based on higher short-duration MMPs (≤5 min). ...
Article
Introduction: Maximal mean power output (MMP) is commonly used to describe the demands and performances of races in professional male cycling. In the female professional cyclist domain, however, there is limited knowledge regarding MMPs in races. Therefore, this study aimed to describe MMPs in female professional cycling races while investigating differences between TOP5 and NOT-TOP5 races. Methods: Race data (N = 1324) were collected from 14 professional female cyclists between 2013 and 2019. Races were categorized as TOP5 or NOT-TOP5. The MMPs were consequently determined over a range of different time frames (5 s to 60 min). To provide these MMPs with additional context, 2 factors were determined: when these MMPs were attained in a race (based on duration and kilojoules spent [kJspent·kg-1]) and these MMPs relative to the cyclist's season's best MMP (MMP%best). Results: Short-duration power outputs (≤1 min) were higher in TOP5 races compared with NOT-TOP5 races. In addition, the timing (both duration and kJspent·kg-1) of all MMPs was later and after more workload in the race in TOP5 compared with NOT-TOP5 races. In contrast, no difference in MMP%best was noted between TOP5 and NOT-TOP5 races. Conclusions: TOP5 races in female cycling are presented with higher short-duration MMPs (≤1 min) when compared with NOT-TOP5 races, and cyclists were able to reach a higher percentage of their seasonal best MMP when they were able to finish TOP5. In addition, these MMPs are performed later and after more kJspent·kg-1 in TOP5 versus NOT-TOP5 races, which confirms the importance of "fatigue resistance" in professional (female) cycling.
... Usually, these studies present technological advances that can be used in the future to provide athletes with insights about their performances. These insights can range from "simple" metrics or visuals, such as descriptive statistics (min, max, mean) of heart-rate, speed, power, steps, jumps [21][22][23], to more complex visuals, such as Poincaré plots [19]; analyses of cycling power over fixed time periods [24][25][26]; or even mathematically derived measures, such as TRIMP scores, "training load" or stress scores (e.g., [22,23,27,28]). When classified as a pure sensor validation study, the paper usually does not give insights in how the information can or should be presented to athletes. ...
... Usually, these studies present technological advances that can be used in the future to provide athletes with insights about their performances. These insights can range from "simple" metrics or visuals, such as descriptive statistics (min, max, mean) of heart-rate, speed, power, steps, jumps [21][22][23], to more complex visuals, such as Poincaré plots [19]; analyses of cycling power over fixed time periods [24][25][26]; or even mathematically derived measures, such as TRIMP scores, "training load" or stress scores (e.g., [22,23,27,28]). When classified as a pure sensor validation study, the paper usually does not give insights in how the information can or should be presented to athletes. ...
Article
Full-text available
In sports feedback systems, digital systems perform tasks such as capturing, analysing and representing data. These systems not only aim to provide athletes and coaches with insights into performances but also help athletes learn new tasks and control movements, for example, to prevent injuries. However, designing mobile feedback systems requires a high level of expertise from researchers and practitioners in many areas. As a solution to this problem, we present Direct Mobile Coaching (DMC) as a design paradigm and model for mobile feedback systems. Besides components for feedback provisioning, the model consists of components for data recording, storage and management. For the evaluation of the model, its features are compared against state-of-the-art frameworks. Furthermore, the capabilities are benchmarked using a review of the literature. We conclude that DMC is capable of modelling all 39 identified systems while other identified frameworks (MobileCoach, Garmin Connect IQ SDK, RADAR) could (at best) only model parts of them. The presented design paradigm/model is applicable for a wide range of mobile feedback systems and equips researchers and practitioners with a valuable tool.
... Thus, it can be noted that the outcome of a cycling race depends on power-to-weight characteristics (Gallo et al., 2021;Lee et al., 2002). Van Erp and Sanders, (2020) show that in professional cycling races maximum mean power over shorter durations (<5 min) are higher for riders who are placed in the top-10 of a race than for riders who are not in the top-10. In addition to physiological parameters, factors such as the bike mass and body mass (both are particularly important in the mountains; Jeukendrup and Martin, 2001) as well as aerodynamic components such as body position, bike frame, and wheels can affect the competition result (Faria et al., 2005;Malizia et al., 2021). ...
... Thus, it is still unclear what influence the actual power output in the competition has on the competition result. Although performance in-competition data from professional athletes are increasingly available via platforms such as Strava, the quantity of available data is still insufficient to calculate the effect on the competition result (Sanders and Heijboer, 2019;Van Erp and Sanders, 2020;Westlake, 2020;Van Erp et al., 2021a). ...
Article
Full-text available
Background: Mixed-reality sports are increasingly reaching the highest level of sport, exemplified by the first Virtual Tour de France, held in 2020. In road races, power output data are only sporadically available, which is why the effect of power output on race results is largely unknown. However, in mixed-reality competitions, measuring and comparing the power output data of all participants is a fundamental prerequisite for evaluating the athlete’s performance. Objective: This study investigates the influence of different power output parameters (absolute and relative peak power output) as well as body mass and height on the results in mixed-reality competitions. Methods: We scrape data from all six stages of the 2020 Virtual Tour de France of women and men and analyze it using regression analysis. Third-order polynomial regressions are performed as a cubic relationship between power output and competition result can be assumed. Results: Across all stages, relative power output over the entire distance explains most of the variance in the results, with maximum explanatory power between 77% and 98% for women and between 84% and 99% for men. Thus, power output is the most powerful predictor of success in mixed-reality sports. However, the identified performance-result gap reveals that other determinants have a subordinate role in success. Body mass and height can explain the results only in a few stages. The explanatory power of the determinants considered depends in particular on the stage profile and the progression of the race. Conclusion: By identifying this performance-result gap that needs to be addressed by considering additional factors like competition strategy or the specific use of equipment, important implications for the future of sports science and mixed-reality sports emerge.
... However, most studies have simply reported the maximal mean power output values (MMP) (the maximum power output recorded for a defined continuous period of time). However, there is uncertainty as to whether the reported MMP values represent 'race winning' efforts i.e. those efforts that differentiate between competitors or simply the maximal record power output values (12). ...
Article
Results: Absolute 5MMPfatigue, 12MMPfatigue and relative 12MMPfatigue were significantly lower in late-season compared with early- and mid-season (p < 0.05). The difference in absolute 12MMPfresh and 12MMPfatigue was significantly greater in late than in early- and mid-season.A significant relationship was found between training time below the first ventilatory threshold (Time < VT1) and improvements in absolute and relative 2MMPfatigue (r = 0.43 p = 0.018 and r = 0.376 p = 0.04 respectively); and between a shift towards a polarised training intensity distribution and improvements in absolute and relative 12MMPfatigue (r = 0.414p = 0.023 for both) between subsequent periods. Conclusion: There is greater variability in the fatigue power profile across a competitive season than the fresh power profile.
... It was noted that, even though cyclists attained similar MMP values during the whole race (thus attending to the highest values attained during the 3 wk) regardless of their categories, WT cyclists showed higher MMP values as the race progressed, that is, during the second and third weeks. 24 Van Erp and Sanders 23 reported that MMP values (particularly for short duration, ie, <5 min) obtained during racing were a differentiating factor between top-10 professional cyclists and those with a lower performance level. Leo et al 25 reported that changes in absolute MMP values (2, 5, and 12 min) during a season in U23 professional cyclists correlated to the changes observed in training loads, which might also support the sensitivity of the RPP. ...
Article
Full-text available
Purpose: The present study aimed to determine the influence of fatigue on the record power profile of professional male cyclists. We also assessed whether fatigue could differently affect cyclists of 2 competition categories. Methods: We analyzed the record power profile in 112 professional cyclists (n = 46 and n = 66 in the ProTeam [PT] and WorldTour [WT] category, respectively; age 29 [6] y, 8 [5] y experience in the professional category) during 2013-2021 (8 [5] seasons/cyclist). We analyzed their mean maximal power (MMP) values for efforts lasting 10 seconds to 120 minutes with no fatigue (after 0 kJ·kg-1) and with increasing levels of fatigue (after 15, 25, 35, and 45 kJ·kg-1). Results: A significant (P < .001) and progressive deterioration of all MMP values was observed from the lowest levels of fatigue assessed (ie, -1.6% to -3.0% decline after 15 kJ·kg-1, and -6.0% to -9.7% after 45 kJ·kg-1). Compared with WT, PT cyclists showed a greater decay of MMP values under fatigue conditions (P < .001), and these differences increased with accumulating levels of fatigue (decay of -1.8 to -2.9% [WT] with reference to 0 kJ·kg-1 vs -1.1% to -4.4% [PT] after 15 kJ·kg-1 and of -4.7% to -8.8% [WT] vs -7.6% to -11.6% [PT] after 45 kJ·kg-1). No consistent differences were found between WT and PT cyclists in MMP values assessed in nonfatigue conditions (after 0 kJ·kg-1), but WT cyclists attained significantly higher MMP values with accumulating levels of fatigue, particularly for long-duration efforts (≥5 min). Conclusions: Our findings highlight the importance of considering fatigue when assessing the record power profile of endurance athletes and support the ability to attenuate fatigue-induced decline in MMP values as a determinant of endurance performance.
... It was noted that, even though cyclists attained similar MMP values during the whole race (thus attending to the highest values attained during the 3 wk) regardless of their categories, WT cyclists showed higher MMP values as the race progressed, that is, during the second and third weeks. 24 Van Erp and Sanders 23 reported that MMP values (particularly for short duration, ie, <5 min) obtained during racing were a differentiating factor between top-10 professional cyclists and those with a lower performance level. Leo et al 25 reported that changes in absolute MMP values (2, 5, and 12 min) during a season in U23 professional cyclists correlated to the changes observed in training loads, which might also support the sensitivity of the RPP. ...
Purpose: To present normative data for the record power profile of male professional cyclists attending to team categories and riding typologies. Methods: Power output data registered from 4 professional teams during 8 years (N = 144 cyclists, 129,262 files, and 1062 total seasons [7 (5) per cyclist] corresponding to both training and competition sessions) were analyzed. Cyclists were categorized as ProTeam (n = 46) or WorldTour (n = 98) and as all-rounders (n = 65), time trialists (n = 11), climbers (n = 50), sprinters (n = 11), or general classification contenders (n = 7). The record power profile was computed as the highest maximum mean power (MMP) value attained for different durations (1 s to 240 min) in both relative (W·kg-1) and absolute units (W). Results: Significant differences between ProTeam and WorldTour were found for both relative (P = .002) and absolute MMP values (P = .006), with WT showing lower relative, but not absolute, MMP values at shorter durations (30-60 s). However, higher relative and absolute MMP values were recorded for very short- (1 s) and long-duration efforts (60 and 240 min for relative MMP values and ≥5 min for absolute ones). Differences were also found regarding cyclists' typologies for both relative and absolute MMP values (P < .001 for both), with sprinters presenting the highest relative and absolute MMP values for short-duration efforts (5-30 s) and general classification contenders presenting the highest relative MMP values for longer efforts (1-240 min). Conclusions: The present results--obtained from the largest cohort of professional cyclists assessed to date-could be used to assess cyclists' capabilities and indicate that the record power profile can differ between cyclists' categories and typologies.
... Also, supporting the sensitivity of the RPP in women, Ebert et al 12 Other studies in male cyclists reported that the RPP can be used to differentiate top-10 professional cyclists from those with a lower competition performance level. 20 In addition, changes in the RPP correlate with changes in training loads 18 and might therefore support the use of the RPP for monitoring cyclists' performance. The RPP may also guide specific training prescriptions for specific team roles such as increasing relative MMP values at long-effort durations for climbers and time trialists. ...
Purpose: To describe the record power profile of professional female cyclists and to assess potential differences based on the type of rider. Methods: Power output data (32,028 files containing both training and competition sessions recorded) in 44 female professional cyclists during 1-6 years were analyzed. Cyclists were categorized as all-rounders, time trialists, climbers, or sprinters. The record power profile was calculated using the mean maximal power output (MMP) values attained by each cyclist for different-effort durations (5 s to 60 min) expressed in relative (W·kg-1), as well as absolute, power output (W). Results: Participants' MMP averaged 15.3 (1.8) W·kg-1 for 5 seconds, 8.4 (0.8) W·kg-1 for 1 minute, 5.2 (0.5) W·kg-1 for 10 minutes, and 4.2 (0.4) W·kg-1 for 60 minutes. For short-duration efforts (5-30 s), sprinters attained the highest MMP results, with significantly higher relative (Hedges g = 1.40-2.31) or absolute (g = 4.48-8.06) values than the remainder of categories or climbers only, respectively. Time trialists attained the highest MMP for longer efforts, with higher relative values than both all-rounders and climbers when comparing efforts lasting 10 to 60 minutes (P < .05, g = 1.21-1.54). Conclusions: In professional female cyclists, the record power profile substantially differs based on the specific category of the rider. These findings provide unique insights into the physical capacities of female professional cyclists, as well as a benchmark for coaches and scientists aiming to identify talent in female cycling.
... 7 Indeed, power output (PO) and HR-derived parameters provide insights about the external (the objective measure of the work that an athlete completes) and internal (the individual psychophysiological response to cope with the external load) demands of exercise. 8,9 A number of studies have analyzed the external and internal demands of professional road races, 10,11 comparing men and women events, 12 professional men races with different competitive levels, 13 and altimetric profiles. 14 On the other hand, only one study described the racing demands of youth cycling categories. ...
Article
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Purpose: To compare the race demands of junior (JUN), under 23 (U23), and professional (PRO) road cyclists. Methods: Thirty male cyclists, divided into 3 age-related categories (JUN, n = 10; U23, n = 10; and PRO, n = 10), participated in this study. Race data collected during the 2019 competitive season were retrospectively analyzed for race characteristics, external, and internal competition load. Results: Higher annual and per race duration, distance, elevation gain, Edward's training impulse, total work, and work per hour were observed in PRO versus U23 and JUN, and U23 versus JUN (P < .01). PRO and U23 recorded higher mean maximal power (RPOs) between 5 and 180 minutes compared with JUN (P < .01). Edward's training impulse per hour was higher in JUN than PRO and U23 (P < .01). Accordingly, JUN spent a higher percentage of racing time in high internal intensity zones compared with U23 and PRO, while these 2 categories spent more time at low internal intensity zones (P < .01). Conclusions: JUN races were shorter and included less elevation gain per distance unit compared to U23 and PRO races, but more internally demanding. JUN produced less power output in the moderate-, heavy-, and severe-intensity exercise domains compared with U23 and PRO (RPOs: 5-180 min). U23 and PRO races presented similar work demands per hour and RPOs, but PRO races were longer than U23.
Article
Full-text available
Emerging trends in technological innovations, data analysis and practical applications have facilitated the measurement of cycling power output in the field, leading to improvements in training prescription, performance testing and race analysis. This review aimed to critically reflect on power profiling strategies in association with the power-duration relationship in cycling, to provide an updated view for applied researchers and practitioners. The authors elaborate on measuring power output followed by an outline of the methodological approaches to power profiling. Moreover, the deriving a power-duration relationship section presents existing concepts of power-duration models alongside exercise intensity domains. Combining laboratory and field testing discusses how traditional laboratory and field testing can be combined to inform and individualize the power profiling approach. Deriving the parameters of power-duration modelling suggests how these measures can be obtained from laboratory and field testing, including criteria for ensuring a high ecological validity (e.g. rider specialization, race demands). It is recommended that field testing should always be conducted in accordance with pre-established guidelines from the existing literature (e.g. set number of prediction trials, inter-trial recovery, road gradient and data analysis). It is also recommended to avoid single effort prediction trials, such as functional threshold power. Power-duration parameter estimates can be derived from the 2 parameter linear or non-linear critical power model: P ( t ) = W ′/ t + CP ( W ′—work capacity above CP; t —time). Structured field testing should be included to obtain an accurate fingerprint of a cyclist’s power profile.
Thesis
Monitoring and evaluating the physiological and performance characteristics of endurance athletes provides relevant information about the long-time athletic development, training process and talent identification. While there is growing evidence for the physiological and performance attributes in junior and professional cyclists, limited information is available about the U23 category. Therefore, the aim of this thesis was to examine the longitudinal physiological and performance characteristics of U23 elite cyclists, with a special focus on the application of the power profile and the power-duration relationship. Study 1 involved a critical evaluation of the current literature on power profiling methodologies and the application of the power-duration relationship. In order to improve the predictive ability of the power profile and the power-duration relationship across exercise intensity domains, it is recommended to ensure a high ecological validity (e.g. rider specialization, race demands) during standardized field testing. For this reason, single effort prediction trials outside the severe exercise intensity domain should be avoided, due to a high measurement bias and a low predictive ability regarding the power-duration relationship. Standardized field testing for power profiling should be conducted at least two times per season to obtain an accurate fingerprint of a cyclist’s performance capacity in the field. In addition, future research is required to better understand the fatigue mechanisms and downward-shift of the power profile and power-duration relationship in the moderate and heavy exercise intensity domains following prior heavy exercise. In Studies 2 and 3 the power profile and power-duration relationship were investigated throughout a competitive season in U23 elite cyclists. Study 2 examined the changes in maximal mean power output (MMP) and derived critical power (CP) and work capacity above critical power (W´) obtained during training and racing. The results revealed that the absolute power profile was not significantly different during a competitive season, except changes in the relative power profile due to a reduction in body mass. Study 3 investigated the differences in the power profile derived from training and racing, the training characteristics across a competitive season, and the relationships between the training characteristics and the power profile in U23 elite cyclists. Higher absolute and relative power profiles were recorded during racing than training. Training characteristics were lowest in pre-season followed by late-season. Changes in training characteristics correlated with changes in the power profile in early- and mid-season, but not in late-season. Practitioners should consider the influence of racing on the derived power profile and adequately balance training programs throughout a competitive season. Studies 4 and 5 analysed the power profile, workload characteristics and race performance in U23 and professional cyclists during a five-day multi-stage race. Study 4 compared the power profile, internal and external workloads, and racing performance between U23 and professional cyclists and between varying rider types, including allrounders, domestiques and general classification (GC) riders. This study demonstrated that the power profile after 1.000-3.000 kJ of total work could be used to evaluate the readiness of U23 cyclists to move into the professional ranks, as well as differentiate between rider types during racing. Study 5 specifically analysed climbing performance in a professional multistage race, and assessed the influence of climb category, prior workload, and intensity measures on climbing performance in U23 and professional cyclists. The findings indicated that climbing performance in professional road cycling is influenced by climb categorization as well as prior workload and intensity measures. Professional cyclists displayed better climbing performance than U23 cyclists, while the workload and intensity measures were higher in U23 than professional cyclists. Collectively the studies within this thesis have contributed to an improved understanding of the physiological and performance attributes of U23 elite cyclists in their maturation to the professional level. These studies have confirmed the practical application of the power profile and power-duration relationship for performance evaluation and prediction during training and racing. This thesis has enabled detailed insights about factors affecting the power profile and the power-duration relationship, and it has provided a concise applied strategy for the inclusion of power profiling in the longitudinal athletic development pathway to maximize cycling performance.
Article
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Introduction: The aim of this study was to present the load, intensity and performance characteristics of a general classification (GC) contender during multiple grand tours (GTs). This study also investigated which factors influence climbing performance. Methods: Power output (PO) data were collected from a GC contender from the Vuelta a España 2015, the Giro d’Italia 2017, the Giro d’Italia 2018 and the Tour de France 2018. Load (e.g. Training Stress Score and kJ spent) and intensity in 5 PO zones was quantified. One-way analysis of variance was used to identify differences between the GTs. Further, performance during the four GTs was quantified based on maximum mean power output (W∙kg-1) over different durations and by the relative PO (W∙kg-1) on the key mountains in the GTs. Stepwise multiple regression analysis was used to identify which factors influence relative PO on the key mountains. Results: No significant differences were found between load and intensity characteristics between the four GTs with the exception that during the Giro d’Italia 2018 a significantly lower absolute time was spent in PO zone 5 (P=0.005) compared to the other three GTs. The average relative PO on the key mountains (n=33) was 5.9±0.6 W∙kg-1 and was negatively influenced by the duration of the climb and the total elevation gain before the key mountain, while the gradient of the mountain had a positive effect on relative PO. Conclusions: The physiological load imposed on a GC contender did not differ between multiple GTs. Climbing performance was influenced by short-term fatigue induced by previous altitude meters in the stage and the duration and gradient of the mountain.
Article
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Purpose: To describe the training intensity and load characteristics of professional cyclists using a 4-year retrospective analysis. Particularly, this study aimed to describe the differences in training characteristics between men and women professional cyclists. Method: For 4 consecutive years, training data were collected from 20 male and 10 female professional cyclists. From those training sessions, heart rate, rating of perceived exertion, and power output (PO) were analyzed. Training intensity distribution as time spent in different heart rate and PO zones was quantified. Training load was calculated using different metrics such as Training Stress Score, training impulse, and session rating of perceived exertion. Standardized effect size is reported as Cohen's d. Results: Small to large higher values were observed for distance, duration, kilojoules spent, and (relative) mean PO in men's training (d = 0.44-1.98). Furthermore, men spent more time in low-intensity zones (ie, zones 1 and 2) compared with women. Trivial differences in training load (ie, Training Stress Score and training impulse) were observed between men's and women's training (d = 0.07-0.12). However, load values expressed per kilometer were moderately (d = 0.67-0.76) higher in women compared with men's training. Conclusions: Substantial differences in training characteristics exist between male and female professional cyclists. Particularly, it seems that female professional cyclists compensate their lower training volume, with a higher training intensity, in comparison with male professional cyclists.
Article
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This study aims to describe the intensity and load demands of different stage types within a cycling Grand Tour. Nine professional cyclists, whom are all part of the same World-Tour professional cycling team, participated in this investigation. Competition data were collected during the 2016 Giro d’Italia. Stages within the Grand Tour were classified into four categories: flat stages (FLAT), semi-mountainous stages (SMT), mountain stages (MT) and individual time trials (TT). Exercise intensity, measured with different heart rate and power output based variables, was highest in the TT compared to other stage types. During TT’s the main proportion of time was spent at the high-intensity zone, whilst the main proportion of time was spent at low intensity for the mass start stage types (FLAT, SMT, MT). Exercise load, quantified using Training Stress Score and Training Impulse, was highest in the mass start stage types with exercise load being highest in MT (329, 359 AU) followed by SMT (280, 311 AU) and FLAT (217, 298 AU). Substantial between-stage type differences were observed in maximal mean power outputs over different durations. FLAT and SMT were characterised by higher short-duration maximal power outputs (5–30 s for FLAT, 30 s–2 min for SMT) whilst TT and MT are characterised by high longer duration maximal power outputs (>10 min). The results of this study contribute to the growing body of evidence on the physical demands of stage types within a cycling Grand Tour.
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Purpose: This study provides a retrospective analysis of a large competition database describing the intensity and load demands of professional road cycling races, highlighting the differences between men's and women's races. Method: Twenty male and ten female professional cyclists participated in this study. During 4 consecutive years, heart rate (HR), rating of perceived exertion (RPE) and power output (PO) data were collected during both male (n = 3024) and female (n = 667) professional races. Intensity distribution in five HR zones was quantified. Competition load was calculated using different metrics including Training Stress Score (TSS), Training Impulse (TRIMP) and session-RPE (sRPE). Standardized effect size is reported as Cohen's d. Results: Large to very large higher values (d = 1.36 - 2.86) were observed for distance, duration, total work (kJ) and mean PO in men's races. Time spent in high intensity HR zones (i.e. zone 4 and zone 5) was largely higher in women's races (d = 1.38 - 1.55) compared to men's races. Small higher loads were observed in men's races quantified using TSS (d = 0.53) and TRIMP (d = 0.23). However, load metrics expressed per km were large to very largely higher in women's races for TSS∙km-1 (d = 1.50) and TRIMP∙km-1 (d = 2.31). Conclusions: Volume and absolute load are higher in men's races whilst intensity and time spent at high intensity zones is higher in women's races. Coaches and practitioners should consider these differences in demands in the preparation of professional road cyclists.
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This study evaluated the changes in ratios of different intensity (rating of perceived exertion; RPE, heart rate; HR, power output; PO) and load measures (session-RPE; sRPE, individualized TRIMP; iTRIMP, Training Stress Score™; TSS) in professional cyclists. RPE, PO and HR data was collected from twelve professional cyclists (VO2max 75 ± 6 ml∙min∙kg⁻¹) during a two-week baseline training period and during two cycling Grand Tours. Subjective:objective intensity (RPE:HR, RPE:PO) and load (sRPE:iTRIMP, sRPE:TSS) ratios and external:internal intensity (PO:HR) and load (TSS:iTRIMP) ratios were calculated for every session. Moderate to large increases in the RPE:HR, RPE:PO and sRPE:TSS ratios (d = 0.79–1.79) and small increases in the PO:HR and sRPE:iTRIMP ratio (d = 0.21–0.41) were observed during Grand Tours compared to baseline training data. Differences in the TSS:iTRIMP ratio were trivial to small (d = 0.03–0.27). Small to moderate week-to-week changes (d = 0.21–0.63) in the PO:HR, RPE:PO, RPE:HR, TSS:iTRIMP, sRPE:iTRIMP and sRPE:TSS were observed during the Grand Tour. Concluding, this study shows the value of using ratios of intensity and load measures in monitoring cyclists. Increases in ratios could reflect progressive fatigue that is not readily detected by changes in solitary intensity/load measures.
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Scantlebury, S, Till, K, Sawczuk, T, Phibbs, P, and Jones, B. Validity of retrospective session rating of perceived exertion to quantify training load in youth athletes. J Strength Cond Res 32(7): 1975-1980, 2018-Youth athletes frequently participate in multiple sports or for multiple teams within the same sport. To optimize player development and minimize undesirable training outcomes (e.g., overuse injuries), practitioners must be cognizant of an athlete's training load within and outside their practice. This study aimed to establish the validity of a 24-hour (s-RPE24) and 72-hour (s-RPE72) recall of session rating of perceived exertion (s-RPE) against the criterion measure of s-RPE collected 30 minutes' post training (s-RPE30). Thirty-eight adolescent athletes provided a s-RPE30 following the first field based training session of the week. Approximately 24 hours later subjects were asked to recall the intensity and duration of the previous days training. The following week subjects once again provided an s-RPE30 measure after training before recalling the intensity and duration of the session approximately 72 hours later. A nearly perfect correlation (0.98 [0.97-0.99]) was found between s-RPE30 and s-RPE24, with a small typical error of estimate (TEE; 8.3% [6.9-10.5]) and trivial mean bias (-1.1% [-2.8 to 0.6]). Despite a large correlation between s-RPE30 and s-RPE72 (0.73 [0.59-0.82]) and a trivial mean bias (-0.2% [-6.8 to 6.8]), there was a large TEE (35.3% [29.6-43.9]). s-RPE24 provides a valid measure of retrospectively quantifying s-RPE; however, the large error associated with s-RPE72 suggests that it is not a suitable method for monitoring training load in youth athletes.
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PURPOSE: The aim of this study was to analyze professional cyclists' performance decline after, and the exercise demands during, a Grand Tour. METHOD: Seven professional cyclists performed two incremental exercise tests, 1-week before and the day after the Vuelta España. During the race the exercise demands were analyzed on the basis of the HR. Three intensity zones were established according to reference HR values corresponding to the ventilatory (VT) and respiratory compensation (RCT) thresholds determined during the pre-race test. In addition, exercise demands for the last weeks of the Vuelta were recalculated: using the reference HR determined during the post-race test for the 3rd week and averaging the change observed in the VT and RCT per stage for the 2nd week. The reference HR for the beginning of the 2nd week was estimated. RESULTS: A significant (P-value range, 0.044-0.000) decrement in VO2, power output and HR at maximal exercise, VT and RCT were found after the race. Based on the pre-race test, the mean time spent daily above the RCT was 13.8 ± 10.2 min. This time decreased -1.2 min·day-1 across the race. When the exercise intensity was corrected according to the post-race test, the time above RCT (34.1±9.9 min) increased 1.0 min·day-1. CONCLUSION: These data indicate that completing a Grand Tour may result in a significant decrement in maximal and submaximal endurance performance capacity. This may modify reference values used to analyze the exercise demands. As a consequence, the high-intensity exercise performed by cyclists may be underestimated.
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Purpose: Describing the demand of recent World Cup (WC) races comparing Top 10 (T10) and non-Top 10 (N-T10) performances using power data. Methods: Race data were collected in 1-day World Cup races during the 2012-2015 road cycling seasons. Seven female cyclists completed 49 WC races, finishing 25 times in T10 and 24 times N-T10. Peak power (1 s) and Maximal Mean Power (MMP) for durations of 5, 10, 20 and 30 s and 1, 2, 5, 10, 20, 30 and 60 min expressed as power to weight ratio were analysed in T10 and N-T10. The percentage of total race time spent at different power bands was compared between T10 and N-T10 using 0.75 W˖kg(-1) power bands, ranging from <0.75 to >7.50 W˖kg(-1). The number of efforts in which the power output remained above 7.50 W˖kg(-1) for at least 10 seconds were recorded. Results: MMP were significantly higher in T10 than in N-Top 10, with a large effect size for durations between 10 seconds and 5 minutes. N-T10 spent more time in the 3.01-3.75 W·kg(-1) power band when compared to T10 (P=0.011); conversely, T10 spent more time in the 6.75-7.50 and >7.50 W·kg(-1) power bands (P=0.009 and 0.005, respectively) than N-T10. A significantly higher number of short and high intensity efforts (≥10s, >7.5 W·kg(-1)) was ridden by T10, compared to N-T10 (P=0.002). Specifically, 46±20 and 30±15 efforts for T10 and N-T10, respectively. Conclusions: The ability to ride at high intensity was determinant for successful road cycling performances in WC races.
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Purpose: This study aims to evaluate training intensity distribution using different intensity measures based on session rating of perceived exertion (sRPE), heart rate (HR) and power output (PO) in well-trained cyclists. Methods: Fifteen road cyclists participated in the study. Training data was collected during a 10-week training period. Training intensity distribution was quantified using HR, PO and sRPE categorized in a 3-zone training intensity model. Three zones for HR and PO were based around a first and second lactate threshold. The three sRPE zones were defined using a 10-point scale: zone 1, sRPE scores 1-4; zone 2, sRPE scores 5-6; zone 3, sRPE scores 7-10. Results: Training intensity distribution as percentages of time spent in zone 1, zone 2 and zone 3 was moderate to very largely different for sRPE (44.9%, 29.9%, 25.2%) compared to HR (86.8%, 8.8%, 4.4%) and PO (79.5%, 9.0%, 11.5%). Time in zone 1 quantified using sRPE was large to very largely lower for sRPE compared to PO (P < 0.001) and HR (P < 0.001). Time in zone 2 and zone 3 was moderate to very largely higher when quantified using sRPE compared to intensity quantified using HR (P < 0.001) and PO (P < 0.001). Conclusions: Training intensity distribution quantified using sRPE demonstrates moderate to very large differences compared to intensity distributions quantified based on HR and PO. The choice of intensity measure impacts on the intensity distribution and has implications for training load quantification, training prescription and the evaluation of training characteristics.
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Exercise is a stressor that induces various psychophysiological responses, which mediate cellular adaptations in many organ systems. To maximize this adaptive response, coaches and scientists need to control the stress applied to the athlete at the individual level. To achieve this, precise control and manipulation of the training load are required. In 2003, the authors introduced a theoretical framework to define and conceptualize the measurable constructs of the training process. They described training load as having 2 measurable components: internal and external load. The aim of this commentary is to extend, clarify, and refine both the theoretical framework and the definitions of internal and external training load to avoid misinterpretation of this concept.