Exercise prescription dose for castrate-resistant prostate cancer patients: a phase I prescription dose escalation trial.

PURPOSE: To determine the start exercise prescription dose in metastatic castrate-resistant prostate cancer (mCRPC) patients receiving second-line hormone treatment and recommended phase II exercise prescription. METHODS: Patients were enrolled in a 3 + 3 dose escalation phase I trial of aerobic, resistance, and flexibility exercises to evaluate dose-limiting tolerance and safety. Tolerance was defined as Borg score ≤ 16 and safety (pain) as a visual analogue scale score (VAS) ≤ 3 and CTCAE grade < 2. Dose level 1 (escalation start dose) was set at 15 min. Aerobic training (50-80% HRmax warm-up and cooling-down; and 65-80% HRmax. core), 1 set with 8-10 repetitions (reps.) resistance training (50-60% 1-RM, 8 exercises), and 1 set (30s) with 2 reps flexibility training (5 exercises). The prescription dose escalation was designed in four levels (from dose -1 to 3), with a dose escalation in volume and intensity of the exercises. RESULTS: Nine patients were included in two dosing cohorts and were under active treatment (n = 4 abiraterone acetate and n = 5 enzalutamide). Dose limiting safety concerns were observed in 2 out of 3 patients in dose level 2 and 1 patient out of 6 in dose level 1 due to VAS > 3 during resistance training and/or flexibility training. No tolerance issues were observed in the two dosing cohorts. The optimal start exercise prescription dose was set at dose level 1 due to safety issues at dose level 2. CONCLUSION: Our findings suggest that exercise is perceived tolerable in mCRPC patients receiving second-line hormone therapy. Caution is indicated on safety during performance of the exercises.

Ramp vs. step tests: valid alternatives to determine the maximal lactate steady-state intensity?

PURPOSE: The aims of this study were (1) to investigate if the respiratory compensation point (RCP) as derived from ramp incremental (RI) exercise could accurately predict the power output (PO) at the maximal lactate steady state (MLSS), and (2) to compare its accuracy with the second lactate threshold (LT2) obtained from step incremental (SI) exercise. METHODS: Nineteen participants performed a RI test (30 W·min-1) to determine RCP, a SI test (30 or 40 W·3 min-1) to determine LT2, and two or more constant work rate (CWR) tests to determine MLSS. For each participant, the [Formula: see text]O2/PO relationship for RI and CWR exercise was established. The ramp-identified PO at RCP was corrected by accounting for the gap between these relationships using the individually determined [Formula: see text] O2/PO regression above GET (RCPcorr-1) or using a fixed regression slope (RCPcorr-2). LT2 was determined using four methods: Dmax, modified Dmax (ModDmax), 4-mM threshold (LT4mM) and an expert-determined LT2 (LT2-expert). RESULTS: RCPcorr-1 (235 ± 69 W), RCPcorr-2 (228 ± 58 W) and LT2-expert (227 ± 61 W) were not different from MLSS (225 ± 60 W). Dmax (203 ± 53 W) underestimated MLSS, while RCP (280 ± 60 W), ModDmax (235 ± 67 W) and LT4mM (234 ± 68 W) overestimated MLSS. The [Formula: see text]O2 at RCP (3.13 ± 0.79L·min-1) and LT2-expert (2.99 ± 0.19L·min-1) did not differ from MLSS (3.05 ± 0.72 L·min-1). CONCLUSION: This study demonstrated that RCP as derived from RI exercise and LT2 as derived from SI exercise can be equally accurate to determine the PO associated with MLSS. Although these results confirmed the suitability of RI and SI tests for this purpose, they also highlighted the importance of an appropriate threshold method selection and the eye of the expert.

Periodization of Plyometrics: Is There an Optimal Overload Principle?

ABSTRACT: Lievens, M, Bourgois, JG, and Boone, J. Periodization of plyometrics: Is there an optimal overload principle? J Strength Cond Res 35(10): 2669-2676, 2021-This study investigated the acute and chronic effects of 3 plyometric training (PT) programs with equal training loads (intensity × volume × frequency) on speed, agility, and jumping performance. Forty-four male recreational team sport athletes were either assigned to a program that increased training volume with exercises of mixed intensity (Mix), kept training volume equal and increased exercise intensity (LowHi), increased training volume and kept exercise intensity low (Low), or to a control group (Control). Subjects were trained twice a week for 8 weeks and were tested for 5- (5 m) and 10-m sprint (10 m), 5 × 10-m shuttle run (5 × 10 m), squat jump (SJ), countermovement jump without and with arm swing, and standing broad jump. Five-, 10- and 5 × 10-m performance did not change (p > 0.05) after the PT program. Jumping performance, except for SJ (p = 0.114), improved significantly (p < 0.05) in the PT groups compared with the control group. However, no mutual differences (p < 0.05) were established between plyometric groups. In addition, it was shown that a PT of high intensity was more likely to affect performance and blood inflammation markers in the following days. To conclude, PT programs following a different overload pattern, i.e., different combination of volume and intensity, but equal training load showed similar performance effects in recreationally trained men. However, before competition, a PT of low intensity is preferred over a PT of high intensity to avoid a decline in performance.

Training Progression in Recreational Cyclists: No Linear Dose-Response Relationship With Training Load.

ABSTRACT: Vermeire, KM, Vandewiele, G, Caen, K, Lievens, M, Bourgois, JG, and Boone, J. Training progression in recreational cyclists: no linear dose-response relationship with training load. J Strength Cond Res 35(12): 3500-3505, 2021-The purpose of the study was to assess the relationship between training load (TL) and performance improvement in a homogeneous group of recreational cyclists, training with a self-oriented training plan. Training data from 11 recreational cyclists were collected over a 12-week period. Before and after the training period, subjects underwent a laboratory incremental exercise test with blood lactate measurements to determine the power output associated with the aerobic threshold (PAT) and the anaerobic threshold (PANT), and the maximal power output (PMAX) was also determined. Mean weekly TL (calculated using the training impulse (TRIMP) of Banister, Edwards TRIMP, Lucia TRIMP and the individualized TRIMP) were correlated to the progression in fitness parameters using Pearson Correlation. Training intensity distribution (TID) was also determined (% in zone 1 as ANT). No significant correlations between mean weekly TRIMP values and the improvement on PMAX (r = -0.22 to 0.08), PANT (r = -0.56 to -0.31) and PAT (r = -0.08 to 0.41) were found. The TID was significant in a multiple regression with PANT as dependent variable (y = 0.0088 + 0.1094 × Z1 - 0.2704 × Z2 + 1.0416 × Z3; p = 0.02; R2 = 0.62). In conclusion, this study shows that the commonly used TRIMP methods to quantify TL do not show a linear dose-response relationship with performance improvement in recreational cyclists. Furthermore, the study shows that TID might be a key factor to establish a relationship with performance improvement.

Characterizing the Exponential Profile of W' Recovery Following Partial Depletion.

PURPOSE: The aim of this study was to characterize W' recovery kinetics in response to a partial W' depletion. We hypothesized that W' recovery following partial depletion would be better described by a biexponential than by a monoexponential model. METHODS: Nine healthy men performed a ramp incremental exercise test, three to five constant load trials to determine critical power and W', and ten experimental trials to quantify W' depletion. Each experimental trial consisted of two constant load work bouts (WB1 + WB2) interspersed by a recovery interval. WB1 was designed to evoke a 25% or 75% W' depletion (DEP 25% + DEP 75% ). Subsequently, participants recovered for 30, 60, 120, 300 or 600 s, and then performed WB2 to exhaustion in order to calculate the observed W' recovery (W' OBS ). W' OBS data were fitted using monoexponential and biexponential models, both with a variable and a fixed model amplitude. Root mean square error (RMSE) and Akaike information criterion (AIC c ) were calculated to evaluate the models' goodness-of-fit. RESULTS: The biexponential model fits were associated with overall lower RMSE values (0.4-5.0%) compared to the monoexponential models (2.9-8.0%). However, ΔAIC c resulted in negative values (-15.5 and -23.3) for the model fits where the amplitude was free, thereby favoring the use of a monoexponential model for both depletion conditions. For the model fits where the amplitude was fixed at 100%, ΔAIC c was negative for DEP 25% (-15.0), but positive for DEP 75% (11.2). W' OBS values were strongly correlated between both depletion conditions ( r = 0.92), and positively associated with V̇O 2peak , CP and GET ( r = 0.67-0.77). CONCLUSIONS: The present study results did not provide evidence in favor of a biexponential modeling technique to characterize W' recovery following partial depletion. Moreover, we demonstrated that fixed t values were insufficient to model W' recovery across different depletion levels, and that W' recovery was positively associated with aerobic fitness. These findings underline the importance of employing variable and individualized t values in future predictive W' models.

Physical Preparation of a World-Class Lightweight Men's Double Sculls Team for the Tokyo 2020 Olympics.

PURPOSE: To analyze the physical profile and training program of a world-class lightweight double sculls rowing crew toward the Tokyo 2020 Olympics. METHOD: A case study in which both rowers performed physical testing in November 2020 and April 2021 (anthropometrics, incremental rowing test, and power profiling). The training program (38 wk) in the buildup to the Olympics was analyzed, providing insight into training characteristics (volume; contribution of rowing, alternative, and strength training; prescribed and recorded [heart rate] training-intensity distribution). The entire period was split into 3 phases: preparation period (8 wk), competition period 1 (11 wk), and competition period 2 (9 wk), and training characteristics were compared. RESULTS: In the April 2021 testing, rower A (1.89 m, 74.6 kg, 4.4% body fat) had a peak oxygen uptake of 5.8 L·min-1 (77.8 mL·min-1·kg-1) and a peak power output of 491 W. Rower B (1.82 m, 70.6 kg, 7.8% body fat) had a peak oxygen uptake of 5.5 L·min-1 (77.9 mL·min-1·kg-1) and a peak power output of 482 W. The mean weekly training volume was 14 hours 47 minutes (4 h 5 min), of which 58.5% (14.6%) consisted of rowing, 13.4% (6.8%) strength training, and 28.1% (2.6%) alternative training. Heart-rate training-intensity distribution was 77.8% (4.2%) in zone 1, 16.6% (3.7%) in zone 2, and 5.6% (2.8%) in zone 3 with a lower contribution of zone 1 in competition period 1 (P = .029) and competition period 2 (P = .023) compared with the preparation period, and a higher contribution of zone 3 in competition period 1 (P = .018) and competition period 2 (P = .011) compared with the preparation period. CONCLUSION: The crew combined a high volume of rowing, alternative, and strength training in a pyramidal heart-rate training-intensity distribution throughout the year.

W' Reconstitution Accelerates More with Decreasing Intensity in the Heavy- versus the Moderate-Intensity Domain.

INTRODUCTION: The purpose of this study was to investigate the effect of the recovery intensity domain on W' reconstitution. We used the W'BAL model as a framework and tested its predictive capabilities (W'PRED) across the different intensity domains. METHODS: Twelve young men (51.7 ± 5.9 mL·kg-1·min-1) completed a ramp incremental test, three to five constant power output (PO) tests to determine critical power (CP) and W', and minimally two trials to verify the maximal lactate (La-) steady state. During four experimental trials, subjects performed two work bouts (WB1 and WB2) at P6 (i.e., PO that predicts exhaustion within 6 min) separated by a recovery interval at CP-10 W, Δgas exchange threshold (GET)-CP, GET, and 50% GET, respectively. WB1 was designed to deplete 75% W', and the recovery time varied to replenish 50% W'. WB2 was performed to exhaustion (W'ACT). W'PRED was compared with W'ACT to evaluate the accuracy of the W'BAL model. Excess postexercise oxygen consumption was calculated as the difference between the measured and the predicted oxygen uptake during recovery. RESULTS: W'ACT averaged 49% ± 24%, 69% ± 24%, 81% ± 28%, and 93% ± 21% for CP-10 W, ΔGET-CP, GET, and 50% GET, respectively (P = 0.002). W'PRED overestimated W'ACT in CP-10 W (34% ± 32%, P = 0.004) and underestimated W'ACT in 50% GET (24% ± 28%, P = 0.013). Excess postexercise oxygen consumption was lowest in CP-10 W (P < 0.01) and higher in GET compared with ΔGET-CP (P = 0.01). CONCLUSION: We demonstrated that W'PRED overestimated and underestimated W'ACT in the heavy- and moderate-intensity domain, respectively. Therefore, the practical applicability of a single recovery time constant, which only relies on the difference between the recovery PO and the CP, is questionable.

Positional Match Running Performance and Performance Profiles of Elite Female Field Hockey.

PURPOSE: To determine if there is a link between the demands of competitive game activity and performance profiles of elite female field hockey players. METHODS: Global positioning systems (GPS) were used to quantify running performance of elite female field hockey players (N = 20) during 26 competitive games. Performance profiles were assessed at 2 time points (preseason and midseason) for 2 competitive seasons. A battery of anthropometric and performance field-based tests (30-15 intermittent fitness test, incremental run test, 10-30-m speed test, T test, and vertical jump test) were used to determine the performance profiles of the players. RESULTS: Players covered a mean total distance of 5384 (835) m, of which 19% was spent at high intensities (zone 5: 796 [221] m; zone 6: 274 [105] m). Forwards covered the lowest mean total distance (estimated marginal means 4586 m; 95% confidence interval, 4275-4897), whereas work rate was higher in forwards compared with midfielders (P = .006, d = 0.43) and central defenders (P = .001, d = 1.41). Players showed an improvement in body composition and anaerobic performance from preseason to midseason. Aerobic performance capacity (maximal oxygen uptake and speed at the 4-mM lactate threshold) was positively correlated with high-intensity activities. CONCLUSIONS: There is a clear relationship between running performance and aerobic performance profiles in elite female hockey players. These results highlight the importance of a well-developed aerobic performance capacity in order to maintain a high performance level during hockey games.

Heart rate-index estimates aerobic metabolism in professional soccer players.

OBJECTIVES: This study aimed at proposing a new heart rate (HR) method to track aerobic metabolism in soccer by: (i) validating a recently developed HR index (HRindex) in professional soccer players, (ii) comparing HRindex vs the most common HR parameters and (iii) testing the agreement between measured and estimated VO2 values using HRindex. DESIGN: cross-sectional. METHODS: 184 professional soccer players performed a step incremental running test on a treadmill while VO2 and HR were recorded. HRindex was calculated (actual HR/resting HR) and its relationship with VO2 was compared with the relationships with the metabolism of actual HR, net HR, and % of HR reserve. Finally, HRindex was used to predict VO2=((HRindex·6)-5)·3.5) and measured and estimated VO2 were compared by 2W RM-ANOVA and Bland-Altman analysis. RESULTS: HRindex/VO2 relationship explained 85% of the variability in data, showing a higher performance than actual HR (77%) and similar values to the other parameters. Measured and estimated VO2 were not significantly different ≤14kmh-1, whereas at speeds ≥14kmh-1 measured VO2 was higher than estimated VO2. Finally, measured and estimated VO2 were highly correlated (R2=0.85, p=0.000), and showed no significant bias (bias=-1.03, z=-0.69, precision=3.75mlkgmin-1). CONCLUSIONS: We validated the HRindex/VO2 relationship in professional soccer players. HRindex showed better agreement with metabolism than actual HR and similar agreement to the other HR parameters. HRindex allowed to estimate VO2, but at very high-intensity HRindex underestimated VO2. Future studies should test this in real game conditions. HRindex could offer a time-efficient and easy-to-use "field" method to monitor aerobic metabolism in soccer.

Modeling the Prediction of the Session Rating of Perceived Exertion in Soccer: Unraveling the Puzzle of Predictive Indicators.

PURPOSE: To predict the session rating of perceived exertion (sRPE) in soccer and determine its main predictive indicators. METHODS: A total of 70 external-load indicators (ELIs), internal-load indicators, individual characteristics, and supplementary variables were used to build a predictive model. RESULTS: The analysis using gradient-boosting machines showed a mean absolute error of 0.67 (0.09) arbitrary units (AU) and a root-mean-square error of 0.93 (0.16) AU. ELIs were found to be the strongest predictors of the sRPE, accounting for 61.5% of the total normalized importance (NI), with total distance as the strongest predictor. The included internal-load indicators and individual characteristics accounted only for 1.0% and 4.5%, respectively, of the total NI. Predictive accuracy improved when including supplementary variables such as group-based sRPE predictions (10.5% of NI), individual deviation variables (5.8% of NI), and individual player markers (17.0% of NI). CONCLUSIONS: The results showed that the sRPE can be predicted quite accurately using only a relatively limited number of training observations. ELIs are the strongest predictors of the sRPE. However, it is useful to include a broad range of variables other than ELIs, because the accumulated importance of these variables accounts for a reasonable component of the total NI. Applications resulting from predictive modeling of the sRPE can help coaching staff plan, monitor, and evaluate both the external and internal training load.