Training intensity distribution in road cyclists: objective versus subjective measures

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

The dose-response relationship between training load and aerobic fitness in academy rugby union players

© 2018 Human Kinetics, Inc. Purpose: To identify the dose-response relationship between measures of training load (TL) and changes in aerobic fitness in academy rugby union players. Method: Training data from 10 academy rugby union players were collected during a 6-wk in-season period. Participants completed a lactate-threshold test that was used to assess VO 2 max, velocity at VO 2 max, velocity at 2 mmol/L (lactate threshold), and velocity at 4 mmol/L (onset of lactate accumulation; vOBLA) as measures of aerobic fitness. Internal-TL measures calculated were Banister training impulse (bTRIMP), Edwards TRIMP, Lucia TRIMP, individualized TRIMP (iTRIMP), and session RPE (sRPE). External-TL measures calculated were total distance, PlayerLoad™, high-speed distance > 15 km/h, very-high-speed distance > 18 km/h, and individualized high-speed distance based on each player’s vOBLA. Results: A second-order-regression (quadratic) analysis found that bTRIMP (R 2 = .78, P = .005) explained 78% of the variance and iTRIMP (R 2 = .55, P = .063) explained 55% of the variance in changes in VO 2 max. All other HR-based internal-TL measures and sRPE explained less than 40% of variance with fitness changes. External TL explained less than 42% of variance with fitness changes. Conclusions: In rugby players, bTRIMP and iTRIMP display a curvilinear dose-response relationship with changes in maximal aerobic fitness

A field-based cycling test to assess predictors of endurance performance and establishing training zones

This study evaluates the relationship between a field-based 8-min time trial (8MTT) and physiological endurance variables assessed with an incremental laboratory test. Secondly, lactate thresholds assessed in the laboratory were compared to estimated functional threshold power (FTP) from the 8MTT. Nineteen well-trained road cyclists (aged 22 +/- 2 yr, height 185.9 +/- 4.5 cm, weight 72.8 +/- 4.6 kg, VO2max 64 +/- 4 ml[middle dot]min-1[middle dot]kg-1) participated. Linear regression revealed that mean 8MTT power output (PO) was strongly to very strongly related to PO at 4 mmol[BULLET OPERATOR]L-1, PO at initial rise of 1.00 mmol[BULLET OPERATOR]L-1, PO at Dmax and modified (mDmax) (r = 0.61 - 0.82). Mean 8MTT PO was largely to very largely different compared to PO at fixed blood lactate concentration (FBLC) of 2 mmol[middle dot]L-1 (ES = 3.20) and 4 mmol[middle dot]L-1 (ES = 1.90), PO at initial rise 1.00 mmol[BULLET OPERATOR]L-1 (ES = 2.33), PO at Dmax (ES = 3.47) and mDmax (ES = 1.79) but only trivially different from maximal power output (Wmax) (ES = 0.09). The 8MTT based estimated FTP was moderate to very largely different compared to PO at initial rise of 1 mmol[BULLET OPERATOR]L-1 (ES = 1.37), PO at Dmax (ES = 2.42), PO at mDmax (ES = 0.77) and PO at 4 mmol[BULLET OPERATOR]L-1(ES = 0.83). Therefore, even though the 8MTT can be valuable as a performance test in cycling shown through its relationships with predictors of endurance performance, coaches should be cautious when using FTP and PO at laboratory-based thresholds interchangeably to inform training prescription

Sleep Profiles of Elite Swimmers During Different Training Phases

This study aims to describe the sleeping patterns during different training phases in competitive swimmers. Twelve national- and international-level swimmers (3 females and 9 males) were monitored during 4 different phases, consisting of a preparation training phase, a taper phase, a competition phase, and a rest phase. Sleep parameters were assessed using wrist activity monitors and self-reported sleep diaries. There was a moderately higher (d = 0.70–1.00) sleep onset latency during the competition phase compared with taper, train, and rest phases. Trivial to small differences were observed for total sleep time between phases (d = 0.05–0.40). Sleep efficiency was moderately higher (d = 0.60–0.75) in the training and taper phases compared with competition and rest. Restfulness and fragmentation index (FI) were lowest in the rest with differences between phases being small (d = 0.43–0.51) for restfulness and small to moderate (d = 0.43–0.62) for FI. Time in and out of bed was very largely later (d = 1.96–2.34) in rest compared with the other phases. Total nap time was moderately lower in rest (d = 1.13–1.18) compared with the training and competition phases, whereas there was a small difference (d = 0.46) compared with taper. To conclude, while there were trivial to small differences in sleep quantity between phases, there are small to moderate differences in other sleep parameters. Specifically, sleep onset latency was higher during the competition phase. In addition, this study highlights the substantial between-individual variations in sleep responses during different training phases

The Relationship between Training Load Measures and Next-Day Well-Being in Rugby Union Players

The aim of this study is to identify the relationship between different internal and external load measures and next day subjective wellbeing. With institutional ethics approval, ten academy rugby union players (Five forwards, and five backs) with a local National League One club agreed to participate in the study (aged; 18.4 ± 1.0 years, height; 181.3 ± 5.9 cm, body mass 85.9 ± 13.0 kg, VO2max 56.2 ± 6.8 mL·kg−1·min−1). Before the 6-week in-season data collection period, participants completed an incremental treadmill test to determine lactate thresholds at 2 mmol·L−1 (LT) and 4 mmol·L−1 and the heart rate blood lactate (HR-BLa) profile for individualized training impulse (iTRIMP) calculations. Internal training load was quantified using Banister’s TRIMP, Edward’s TRIMP, Lucia’s TRIMP, individualised TRIMP and session-RPE. External training load was reported using total distance, PlayerLoadTM, high-speed distances (HSD) > 18 km∙h−1 and >15 km∙h−1, and individualized high-speed distance (iHSD) based on each player’s velocity at OBLA. On arrival and prior to all training sessions players completed a well-being questionnaire (WB). Bayesian linear mixed model analysis identified that a range of internal and external load measures explained between 30% and 37% of next-day total wellbeing and between 65% and 67% of next-day perceived stress. All other internal and external load measures demonstrated very weak to moderate relationships (R2 = 0.08 to 0.39) with all other wellbeing components. Internal sRPE, iTRIMP and bTRIMP loads alongside external HSD loads provide coaches with the most practical measures to influence players’ perceived wellbeing

Analysing a cycling grand tour: Can we monitor fatigue with intensity or load ratios?

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 weekto- 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

Reliability of Change of Direction and Agility Assessments in Youth Soccer Players

Considering the vast physical and neural developments experienced throughout adolescence, the reliability of physical performance may vary in youth populations. This study aimed to examine the reliability of change of direction (COD) and agility tests in youth soccer players. Altogether, 86 youth soccer players, aged 13.6 ± 2.0 years, volunteered to participate. Data were collected from a modified 505 COD test (m505COD) and the Y-sprint drill in both pre-planned (Y-SprintPRE) and reactive (Y-SprintREACT) conditions during 2 sessions, 7 days apart. Anthropometric data including body mass, standing stature, and sitting height were also collected. COD and agility tests demonstrated good reliability (ICC = 0.81–0.91; CV = 1.2–2.0; d = 0.00–0.31; p < 0.01) for our entire sample. However, we observed a small negative relationship between age and intersession differences for the Y-SprintPRE (r = −0.28; p = 0.04), and moderate negative relationships between both age (r = −0.41; p < 0.01), and maturity offset (r = −0.39; p < 0.01) for the Y-SprintREACT. Although the COD and agility tests adopted within this study possess good intersession reliability, we observed greater intersession differences for younger and less mature individuals. We suggest that while COD and agility tests may provide meaningful objective data for monitoring the development of youth soccer players, these tests should be used with caution when evaluating younger, more immature athletes

Methods of monitoring training load and their relationships to changes in fitness and performance in competitive road cyclists

Purpose: The aim of this study was to assess the dose-response relationships between different training load methods and aerobic fitness and performance in competitive road cyclists. Method: Training data from 15 well-trained competitive cyclists were collected during a 10-week (December – March) pre-season training period. Before and after the training period, participants underwent a laboratory incremental exercise test with gas exchange and lactate measures and a performance assessment using an 8-min time trial (8MT). Internal training load was calculated using Banister’s TRIMP (bTRIMP), Edwards’ TRIMP (eTRIMP), individualized TRIMP (iTRIMP), Lucia’s TRIMP (luTRIMP) and session-RPE (sRPE). External load was measured using Training Stress Score™ (TSS). Results: Large to very large relationships (r = 0.54-0.81) between training load and changes in submaximal fitness variables (power at 2 and 4 mmol·L-1) were observed for all training load calculation methods. The strongest relationships with changes in aerobic fitness variables were observed for iTRIMP (r = 0.81 [95% CI: 0.51 to 0.93, r = 0.77 [95% CI 0.43 to 0.92]) and TSS (r = 0.75 [95% CI 0.31 to 0.93], r = 0.79 [95% CI: 0.40 to 0.94]). The highest dose-response relationships with changes in the 8MT performance test were observed for iTRIMP (r = 0.63 [95% CI 0.17 to 0.86]) and luTRIMP (r = 0.70 [95% CI: 0.29 to 0.89). Conclusions: The results show that training load quantification methods that integrate individual physiological characteristics have the strongest dose-response relationships, suggesting this to be an essential factor in the quantification of training load in cycling

Differences in Performance Assessments Conducted Indoors and Outdoors in Professional Cyclists

Purpose: The purpose of this study was to assess the relationship between typical performance tests amongst elite and professional cyclists when conducted indoors and outdoors. Methods: 14 male cyclists of either UCI Continental or UCI World Tour level (mean ± SD: age 20.9 ± 2.8 y, mass 68.13 ± 7.25 kg) were recruited to participate in 4 test sessions (2 test sessions indoors, 2 test sessions outdoors) within a 14-day period, consisting of maximum mean power (MMP) testing for durations of 60s, 180s, 300s and 840s. Results: Across all MMP test durations, the trimmed mean power was higher outdoors compared to indoor testing (p < 0.05). Critical Power (CP) was higher outdoors compared to indoors (+19 W, p = 0.005) whilst no difference was observed for the work capacity above CP (W’). Self-selected cadence was 6 rev∙min-1 higher indoors versus outdoors for test durations of 60s (p = 0.038) and 300s (p = 0.002). Conclusions: These findings suggest that maximal power testing in indoor and outdoor settings cannot be used interchangeably. Furthermore, there was substantial individual variation in the difference between indoor and outdoor MMPs, across all time durations, further highlighting the difficulty of translating results from indoor testing to outdoor, on an individual level in elite populations

Evaluation of multi-directional speed qualities throughout adolescence in youth soccer: the non-linear nature of transfer

Training and assessment of agility is often prioritised by soccer coaches and practitioners aiming to develop multi-directional speed. Although the importance of agility is advocated throughout childhood and adolescence, limited data evidence agility performance at different stages of adolescence. The purpose of this study was to examine differences in multi-directional speed performance in youth soccer players spanning an entire soccer academy. A total of 86 male junior-elite soccer players volunteered to participate. Anthropometric data were collected, alongside performance data from a battery of physical tests including sprinting, jumping, change of direction, reaction time, and agility. Bayesian models using log-likelihoods from posterior simulations of parameter values displayed linear or curvilinear relationships between both chronological and biological age and performance in all tests other than agility and reaction time. For agility and reaction time tests, performance improved until ~14 years of age or the estimated age of peak height velocity whereby arrested development in performance was observed. Our results demonstrate that while most performance skills improve as chronological or biological age increases, measures of agility and reaction time may not. These findings support the notion that agility performance is complex and multifaceted, eliciting unique, challenging physical demands and non-linear development