Purpose: Reported relationships between electromyographic (EMG) thresholds and systemic thresholds based on lactate, ventilation, or heart rate are contradictory. This might be related to the complexity of the investigated whole-body movements involving many muscles with different activation patterns. Therefore, the aim of the study was to investigate these relationships during an incremental single-joint exercise. Methods: Eighteen male subjects (29.7 [4.4] y) performed single-arm elbow flexions on a biceps curl machine with loads increasing every minute until exhaustion. EMG signals of the main elbow flexors (short and long head of the biceps brachii, flexor carpi radialis, and brachioradialis) as well as gas exchange variables, blood lactate concentration, and heart rate were measured, and 2 turn points based on a 3-phase model of metabolism were determined for each variable. Results: The first and second turn points for EMG were determined at 32.0% to 33.1% and 64.4% to 66.5% of maximal achieved performance (maximum weight), respectively. Systemic turn points were determined at 33.3% to 34.4% and 65.9% to 66.7% of maximum weight and were not significantly different from EMG turn points. Furthermore, systemic and EMG turn points showed a strong or very strong relationship at the first (ρ = .54–.93, P < .05) and second turn point (ρ = .76–.93, P < .01). Conclusions: A close relationship between EMG and systemic turn points could be confirmed for the applied movement of a small muscle group. The determination of local single muscle thresholds using EMG provides additional muscle-specific information about performance-limiting properties of muscles involved in endurance-type incremental exercise.
Markus Tilp, Lukas Kitzberger, Gudrun Schappacher-Tilp, Philipp Birnbaumer, and Peter Hofmann
Alice Iannaccone, Andrea Fusco, Antanas Skarbalius, Audinga Kniubaite, Cristina Cortis, and Daniele Conte
Purpose: Assessing the relationship between external load (EL) and internal load (IL) in youth male beach handball players. Methods: A total of 11 field players from the Lithuanian U17 beach handball team were monitored across 14 training sessions and 7 matches. The following EL variables were assessed by means of inertial movement units: PlayerLoad™, accelerations, decelerations, changes of direction, and jumps and total of inertial movements. IL was assessed objectively and subjectively using the summated heart rate zones and training load calculated via session rating of perceived exertion, respectively. Spearman correlations (ρ) were used to assess the relationship between EL and IL. The interindividual variability was investigated using linear mixed models with random intercepts with IL as dependent variable, PlayerLoad™ as the independent variable, and players as random effect. Results: The lowest significant (P < .05) relationship was for high jumps with objective (ρ = .56) and subjective (ρ = .49) IL. The strongest relationship was for PlayerLoad™ with objective (ρ = .9) and subjective (ρ = .84) IL. From the linear mixed model, the estimated SD of the random intercepts was 19.78 arbitrary units (95% confidence interval, 11.75–33.31); SE = 5.26, and R 2 = .47 for the objective IL and 6.03 arbitrary units (95% confidence interval, 0.00–7330.6); SE = 21.87; and R 2 = .71 for the subjective IL. Conclusions: Objective and subjective IL measures can be used as a monitoring tool when EL monitoring is not possible. Coaches can predict IL based on a given EL by using the equations proposed in this study.
Matej Vajda and Eva Piatrikova
Purpose: To assess the relationship between flat-water tests and canoe slalom performance on 4 different grades of water terrain difficulty. Methods: Nineteen elite canoe slalom athletes racing in category K1 men (n = 7), K1 women (n = 5), or C1 men (n = 7) completed flat-water tests: (1) a sprint with a turn to the preferred side, (2) a sprint with a turn to the nonpreferred side, (3) a sprint with a turn to both sides, and (4) a 12 × 15-m all-out shuttle test. Canoe slalom performance was measured in competitions with 4 different grades of water terrain difficulty. Results: There were relationships between 12 × 15-m all-out shuttle test and performance across different water terrain grades (P < .001; r = .706–.871)); however, the magnitude of the relationship decreased with increasing water terrain grade difficulty. Similar trends were observed for the sprint with a turn to the preferred side (r = .588–.884), sprint with a turn to the nonpreferred side (r = .544–.864), and sprint with a turn to both sides (r = .638–.909). In addition, small to moderate differences were observed between preferred and nonpreferred side in K1 women (P = .050, ES = 0.37), K1 men (P = .019, ES = 0.66), and C1 men (P = .003, ES = 0.69). Conclusion: The novel battery of flatwater tests can be used to measure the performance-related physical fitness of canoe slalom athletes. Sprint with a turn to the preferred side and sprint with a turn to the nonpreferred side can also be used to assess the imbalance between an athlete’s preferred and nonpreferred side. Accordingly, to our findings, practitioners could consider adapting the training program in preparation for important competitions specifically to water terrain difficulty grades where these competitions will be organized.
Teun van Erp, Taco van der Hoorn, Marco J.M. Hoozemans, Carl Foster, and Jos J. de Koning
Purpose: To determine if workload and seasonal periods (preseason vs in season) are associated with the incidence of injuries and illnesses in female professional cyclists. Methods: Session rating of perceived exertion was used to quantify internal workload and was collected from 15 professional female cyclists, from 33 athlete seasons. One week (acute) workload, 4 weeks (chronic) workload, and 3 acute:chronic workload models were analyzed. Two workload models are based on moving averages of the ratios, the acute:chronic workload ratio (ACWR), and the ACWR uncoupled (ACWRuncoup). The difference between both is the chronic load; in ACWR, the acute load is part of the chronic load, and in ACWRuncoup, the acute and chronic load are uncoupled. The third workload model is based on exponentially weighted moving averages of the ratios. In addition, the athlete season is divided into the preseason and in season. Results: Generalized estimating equations analysis was used to assess the associations between the workload ratios and the occurrence of injuries and illnesses. High values of acute workload (P = .048), ACWR (P = .02), ACWRuncoup (P = .02), exponentially weighted moving averages of the ratios (P = .01), and the in season (P = .0001) are significantly associated with the occurrence of injury. No significant associations were found between the workload models, the seasonal periods, and the occurrence of illnesses. Conclusions: These findings suggest the importance of monitoring workload and workload ratios in female professional cyclists to lower the risk of injuries and therefore improve their performances. Furthermore, these results indicate that, in the preseason, additional stressors occur, which could lead to an increased risk of injuries.
Soraya Martín-Manjarrés, Carlos Rodríguez-López, María Martín-García, Sara Vila-Maldonado, Cristina Granados, Esmeralda Mata, Ángel Gil-Agudo, Irene Rodríguez-Gómez, and Ignacio Ara
People with spinal cord injury (SCI) tend to be more sedentary and increase fat accumulation, which could have a negative influence on metabolic flexibility. The aim of this study was to investigate the capacity to oxidize fat in a homogenous sample of men with thoracic SCI compared with healthy noninjured men during an arm cycling incremental test. Forty-one men, 21 with SCI and 20 noninjured controls, performed an incremental arm cycling test to determine peak fat oxidation (PFO) and the intensity of exercise that elicits PFO (Fatmax). PFO was expressed in absolute values (g/min) and relative to whole-body and upper-body lean mass ([mg·min−1]·kg−1) through three different models (adjusting by cardiorespiratory fitness and fat mass). Gross mechanical efficiency was also calculated. PFO was higher in SCI than in noninjured men (0.27 ± 0.07 vs. 0.17 ± 0.07 g/min; 5.39 ± 1.30 vs. 3.29 ± 1.31 [mg·min−1]·kg−1 whole-body lean mass; 8.28 ± 2.11 vs. 5.08 ± 2.12 [mg·min−1]·kg−1 upper-body lean mass). Fatmax was found at a significantly higher percentage of VO2peak in men with SCI (33.6% ± 8.2% vs. 23.6% ± 6.4%). Differences persisted and even increased in the fully adjustment model and at any intensity. Men with SCI showed significantly higher gross mechanical efficiency at 35 and 65 W than the noninjured group. Men with SCI showed higher fat oxidation when compared with noninjured men at any intensity, even increased after full adjustment for lean mass, fat mass, and cardiorespiratory fitness. These findings suggest that SCI men could improve their metabolic flexibility and muscle mass for greater efficiency, not being affected by their fat accumulation.
Pierpaolo Sansone, Alessandro Ceravolo, and Antonio Tessitore
Purpose: To quantify external, internal, and perceived training loads and their relationships in youth basketball players across different playing positions. Methods: Fourteen regional-level youth male players (age: 15.2 [0.3] y) were monitored during team-based training sessions across 10 in-season weeks. The players were monitored with BioHarness-3 devices, to measure external (Impulse Load, in Newtons per second) and internal (summated-heart-rate zones [SHRZ], in arbitrary units [AU]) loads, and with the session rating of perceived exertion (sRPE, in AU) method to quantify perceived training load. Multiple linear mixed models were performed to compare training loads between playing positions (backcourt and frontcourt). Repeated-measures correlations were performed to assess the relationships between the load models, for all players and within playing positions. Results: External load (backcourt: 13,599  N·s; frontcourt: 14,934  N·s) and sRPE (backcourt: 345  AU; frontcourt: 505  AU) were higher in the frontcourt (P < .05, effect size: moderate), while SHRZ was similar between positions (backcourt: 239  AU; frontcourt: 247  AU) (P > .05; effect size: trivial). The correlations were as follows: large between the external load and SHRZ (r = .57, P < .001), moderate between SHRZ and sRPE (r = .45, P < .001), and small between the external load and sRPE (r = .26, P = .02). The correlation magnitudes were equivalent for external load–SHRZ (large) and SHRZ–sRPE (moderate) across positions, but different for the external load–sRPE correlation (small in backcourt; moderate in frontcourt). Conclusions: In youth basketball, small–large commonalities were found between the training dose (external load) and players’ responses (internal and perceived loads). Practitioners should carefully manage frontcourt players’ training loads because they accumulate greater external and perceived loads than backcourt players do.
Suzanna Russell, David G. Jenkins, Shona L. Halson, Laura E. Juliff, Mark J. Connick, and Vincent G. Kelly
Purpose: Mental fatigue is emerging as an important consideration for elite sporting performance, yet it is rarely monitored. The present study assessed changes in mental fatigue in professional team-sport athletes across 2 seasons and examined the relationship between mental fatigue and other athlete self-report measures of well-being. Methods: Elite netballers contracted to all teams competing in Australia’s premier professional netball competition during the 2018 and 2019 seasons (N = 154) participated. Using 5-point Likert scales, mental fatigue, fatigue (physical), tiredness, sleep quality, stress, mood, and motivation were assessed daily across 2 seasons composed of 14 round and finals series. Results: The ratings of mental fatigue significantly changed during both seasons. In 2018, lower ratings of mental fatigue were reported in round 1 versus 3, 4, 6, 8, and 14; round 7 versus 6; and round 6 versus 10 (P < .05). In 2019, lower ratings of mental fatigue were identified for round 1 versus 3, 9, 10 to 14, and semifinal; round 2 versus 10 to 13; and 5 versus 10 to 12 (P < .05). Ordinal regression revealed significant differences between mental fatigue and physical fatigue (P < .001), tiredness (P < .001), stress (P < .001), mood (P < .001), and motivation (P < .05). Conclusions: The present study found mental fatigue to significantly fluctuate across a season in elite netballers. Moreover, perceived mental fatigue differed from physical fatigue, tiredness, stress, mood, and motivation. The data impress the need for mental fatigue to be included as an independent measure of athlete well-being. Monitoring of mental fatigue can allow practitioners to implement strategies to manage its influence on performance.
Fernando G. Beltrami, Christian Froyd, Alexis R. Mauger, Alan J. Metcalfe, and Timothy D. Noakes
Objective: To investigate whether a cycling test based on decremental loads (DEC) could elicit higher maximal oxygen uptake (