Carl Foster

Purpose: The purpose of this study was to determine whether session rating of perceived exertion-derived training load (sRPE-TL) correlates with GPS-derived measures of external load in National Collegiate Athletics Association (NCAA) Division I female soccer athletes. Methods: Twenty-one NCAA Division 1 collegiate women’s soccer athletes (11 starters, 10 non-starters; 65.1 ± 7.2 kg, 168.4 ± 7.9 cm, 20.3 ± 1.5 yrs) volunteered to take part in this study. Data for this study were collected over the course of 16 weeks during the 2018 NCAA women’s soccer season. External load and heart rate (HR) data were collected during each training session and match during the season. At least 30 min after the end of an activity (e.g., match or practice), athletes were prompted to complete a questionnaire reporting their perceived exertion for the session. sRPE-TL was calculated at the end of the season by multiplying perceived exertion by the respective session duration. Results: sRPE-TL was very strongly correlated with total distance, distance covered in velocity zones 1–3, the number of accelerations in zones 4 and 5, total PlayerLoad™, and PlayerLoad™. For internal load, sRPE-TL correlated very strongly (0.70 ≤ |r| < 0.90) with Edward’s and Bannister’s TRIMP and strongly (0.50 ≤ |r| < 0.70) with duration spent in in heart rate zones 5 and 6 (80–90% and 90–100% max HR, respectively) while correlations with maximum HR (bpm), mean HR (bpm), and mean HR (%) and sRPE-TL were moderate (0.30 ≤ |r| < 0.50). Conclusions: In NCAA Division I women soccer, sRPE-TL is strongly associated with external measures of workload. These relationships were stronger during match play, with acceleration load and total distance exhibiting the strongest relationship with sRPE-TL.

The session rating of perceived exertion (sRPE) method was developed 25 years ago as a modification of the Borg concept of rating of perceived exertion (RPE), designed to estimate the intensity of an entire training session. It appears to be well accepted as a marker of the internal training load. Early studies demonstrated that sRPE correlated well with objective measures of internal training load, such as the percentage of heart rate reserve and blood lactate concentration. It has been shown to be useful in a wide variety of exercise activities ranging from aerobic to resistance to games. It has also been shown to be useful in populations ranging from patients to elite athletes. The sRPE is a reasonable measure of the average RPE acquired across an exercise session. Originally designed to be acquired ∼30 minutes after a training bout to prevent the terminal elements of an exercise session from unduly influencing the rating, sRPE has been shown to be temporally robust across periods ranging from 1 minute to 14 days following an exercise session. Within the training impulse concept, sRPE, or other indices derived from sRPE, has been shown to be able to account for both positive and negative training outcomes and has contributed to our understanding of how training is periodized to optimize training outcomes and to understand maladaptations such as overtraining syndrome. The sRPE as a method of monitoring training has the advantage of extreme simplicity. While it is not ideal for the precise recording of the details of the external training load, it has large advantages relative to evaluating the internal training load.

Ever since the farm boy, Milo of Crotone, lifted a growing bullock every day, to become the strongest man in the world, and six-time champion of the ancient Olympic Games, we have known about the principle of progression of exercise training

Background: The association between heart rate variability (HRV), training load (TL), and performance is poorly understood. Methods: A middle-aged recreational female runner was monitored during a competitive 20-wk macrocycle divided into first (M1) and second mesocycle (M2) in which best performances over 10 km and 21 km were recorded. Volume (km), session rating of perceived exertion (sRPE), TL, and monotony (mean TL/SD TL) were the workload parameters recorded. The root mean square of the successive differences in R-R intervals (RMSSD), its coefficient of variation (RMSSDcv), and the RMSSD:RR ratio were the HRV parameters monitored. Results: During M2, RMSSD (p = 0.006) and RMSSD:RR (p = 0.002) were significantly increased, while RR was significantly reduced (p = 0.017). Significant correlations were identified between monotony and volume (r = 0.552; p = 0.012), RR (r = 0.447; p = 0.048), and RMSSD:RR (r = −0.458; p = 0.042). A sudden reduction in RMSSD (from 40.31 to 24.34 ms) was observed the day before the first symptoms of an influenza. Conclusions: The current results confirm the practicality of concurrent HRV and sRPE monitoring in recreational runners, with the RMSSD:RR ratio indicative of specific adaptations. Excessive training volume may be associated to both elevated monotony and reduced RMSSD:RR. Identification of mesocycle patterns is recommended for better individualization of the periodization used.

The aerobic cost of running (CR), an important determinant of running performance, is usually measured during constant speed running. However, constant speed does not adequately reflect the nature of human locomotion, particularly competitive races, which include stochastic variations in pace. Studies in non-athletic individuals suggest that stochastic variations in running velocity produce little change in CR. This study was designed to evaluate whether variations in running speed influence CR in trained runners. Twenty competitive runners (12 m, VO_2max = 73 ± 7 mL/kg; 8f, VO_2max = 57 ± 6 mL/kg) ran four 6-minute bouts at an average speed calculated to require ~90% ventilatory threshold (VT) (measured using both v-slope and ventilatory equivalent). Each interval was run with minute-to-minute pace variation around average speed. CR was measured over the last 2 min. The coefficient of variation (CV) of running speed was calculated to quantify pace variations: ±0.0 m∙s⁻¹ (CV = 0%), ±0.04 m∙s⁻¹ (CV = 1.4%), ±0.13 m∙s⁻¹(CV = 4.2%), and ±0.22 m∙s⁻¹(CV = 7%). No differences in CR, HR, or blood lactate (BLa) were found amongst the variations in running pace. Rating of perceived exertion (RPE) was significantly higher only in the 7% CV condition. The results support earlier studies with short term (3s) pace variations, that pace variation within the limits often seen in competitive races did not affect CR when measured at running speeds below VT.

During competitive events, the pacing strategy depends upon how an athlete feels at a specific moment and the distance remaining. It may be expressed as the Hazard Score (HS) with momentary HS being shown to provide a measure of the likelihood of changing power output (PO) within an event and summated HS as a marker of how difficult an event is likely to be perceived to be. This study aimed to manipulate time trial (TT) starting strategies to establish whether the summated HS, as opposed to momentary HS, will improve understanding of performance during a simulated cycling competition. Seven subjects (peak PO: 286 ± 49.7 W) performed two practice 10-km cycling TTs followed by three 10-km TTs with imposed PO (±5% of mean PO achieved during second practice TT and a self-paced TT). PO, rating of perceived exertion (RPE), lactate, heart rate (HR), HS, summated HS, session RPE (sRPE) were collected. Finishing time and mean PO for self-paced (time: 17.51 ± 1.41 min; PO: 234 ± 62.6 W), fast-start (time: 17.72 ± 1.87 min; PO: 230 ± 62.0 W), and slow-start (time: 17.77 ± 1.74 min; PO: 230 ± 62.7) TT were not different. There was a significant interaction between each secondary outcome variable (PO, RPE, lactate, HR, HS, and summated HS) for starting strategy and distance. The evolution of HS reflected the imposed starting strategy, with a reduction in PO following a fast-start, an increased PO following a slow-start with similar HS during the last part of all TTs. The summated HS was strongly correlated with the sRPE of the TTs (r = 0.88). The summated HS was higher with a fast start, indicating greater effort, with limited time advantage. Thus, the HS appears to regulate both PO within a TT, but also the overall impression of the difficulty of a TT.

Although cycling class intensity can be modified by changing interval intensity sequencing, it has not been established whether the intensity order can alter physiological and perceptual responses. Therefore, this study aimed to determine the effects of interval intensity sequencing on energy expenditure (EE), physiological markers, and perceptual responses during indoor cycling. Healthy volunteers (10 males = 20.0 ± 0.8years; 8 females = 21.3 ± 2.7years) completed three randomly ordered interval bouts (mixed pyramid—MP, ascending intervals—AI, descending intervals—DI) including three 3-min work bouts at 50%, 75%, and 100% of peak power output (PPO) and three 3-min recovery periods at 25% PPO. Heart rate (HR) and oxygen consumption (VO₂) were expressed as percentages of maximal HR (%HR_max) and VO₂ (%VO_2max). EE was computed for both the work bout and for the 5-min recovery period. Session Rating of Perceived Exertion (sRPE) and Exercise Enjoyment Scale (EES) were recorded. No differences emerged for % HR_max (MP = 73.3 ± 6.1%; AI = 72.1 ± 4.9%; DI = 71.8 ± 4.5%), % VO_2max (MP = 51.8 ± 4.6%; AI = 51.4 ± 3.9%; DI = 51.3 ± 4.5%), EE (MP = 277.5 ± 39.9 kcal; AI = 275.8 ± 39.4 kcal; DI = 274.9 ± 42.1 kcal), EES (MP = 4.9 ± 1.0; AI = 5.3 ± 1.1; DI = 4.9 ± 0.9), and sRPE (MP = 4.9 ± 1.0; AI = 5.3 ± 1.1; DI = 4.9 ± 0.9). EE during recovery was significantly (p < 0.005) lower after DI (11.9 ± 3.2 kcal) with respect to MP (13.2 ± 2.5 kcal) and AI (13.3 ± 2.5 kcal). Although lower EE was observed during recovery in DI, interval intensity sequencing does not affect overall EE, physiological markers, and perceptual responses.

The time to exhaustion (t_lim) at the respiratory compensation point (RCP) and whether a physiological steady state is observed at this workload remains unknown. Thus, this study analyzed t_lim at the power output eliciting the RCP (t_lim at RCP), the oxygen uptake (VO₂) response to this effort, and the influence of endurance fitness. Sixty male recreational cyclists (peak oxygen uptake [VO_2peak] 40–60 mL∙kg∙min⁻¹) performed an incremental test to determine the RCP, VO_2peak, and maximal aerobic power (MAP). They also performed constant-load tests to determine the t_lim at RCP and t_lim at MAP. Participants were divided based on their VO_2peak into a low-performance group (LP, n = 30) and a high-performance group (HP, n = 30). The t_lim at RCP averaged 20 min 32 s ± 5 min 42 s, with a high between-subject variability (coefficient of variation 28%) but with no differences between groups (p = 0.788, effect size = 0.06). No consistent relationships were found between the t_lim at RCP and the different fitness markers analyzed (RCP, power output (PO) at RCP, VO_2peak, MAP, or t_lim at MAP; all p > 0.05). VO₂ remained steady overall during the t_lim test, although a VO₂ slow component (i.e., an increase in VO₂ >200 mL·min⁻¹ from the third min to the end of the tests) was present in 33% and 40% of the participants in HP and LP, respectively. In summary, the PO at RCP could be maintained for about 20 min. However, there was a high between-subject variability in both the t_lim and in the VO₂ response to this effort that seemed to be independent of fitness level, which raises concerns on the suitability of this test for fitness assessment.

Although there is evidence supporting the benefit of regular exercise, and recommendations about exercise and physical activity, the process of individually prescribing exercise following exercise testing is more difficult. Guidelines like % heart rate (HR) reserve (HRR) require an anchoring maximal test and do not always provide a homogenous training experience. When prescribing HR on the basis of % HRR, rating of perceived exertion or Talk Test, cardiovascular/perceptual drift during sustained exercise makes prescription of the actual workload difficult. To overcome this issue, we have demonstrated a strategy for “translating” exercise test responses to steady state exercise training on the basis of % HRR or the Talk Test that appeared adequate for individuals ranging from cardiac patients to athletes. However, these methods depended on the nature of the exercise test details. In this viewpoint, we combine these data with workload expressed as Metabolic Equivalent Task (METs). We demonstrate that there is a regular stepdown between the METs during training to achieve the same degree of homeostatic disturbance during testing. The relationship was linear, was highly-correlated (r = 0.89), and averaged 71.8% (Training METs/Test METs). We conclude that it appears possible to generate a generalized approach to correctly translate exercise test responses to exercise training.

We are glad to introduce the Third Journal Club of Volume five, the third issue. This edition is focused on relevant studies published in the last years in the field of PhysioMechanics of Human Locomotion, chosen by our Editorial Board members and their colleagues. We hope to stimulate your curiosity in this field and to share with you the passion for the Sports Medicine and Movement Sciences seen also from the scientific point of view. The Editorial Board members wish you an inspiring lecture.