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ABSTRACT: Borszcz, FK, de Aguiar, RA, Costa, VP, Denadai, BS, and de Lucas, RD. Agreement between maximal lactate steady state and critical power in different sports: A systematic review and Bayesian's meta-regression. J Strength Cond Res 38(6): e320-e339, 2024-This study aimed to systematically review the literature and perform a meta-regression to determine the level of agreement between maximal lactate steady state (MLSS) and critical power (CP). Considered eligible to include were peer-reviewed and "gray literature" studies in English, Spanish, and Portuguese languages in cyclical exercises. The last search was made on March 24, 2022, on PubMed, ScienceDirect, SciELO, and Google Scholar. The study's quality was evaluated using 4 criteria adapted from the COSMIN tool. The level of agreement was examined by 2 separate meta-regressions modeled under Bayesian's methods, the first for the mean differences and the second for the SD of differences. The searches yielded 455 studies, of which 36 studies were included. Quality scale revealed detailed methods and small samples used and that some studies lacked inclusion/exclusion criteria reporting. For MLSS and CP comparison, likely (i.e., coefficients with high probabilities) covariates that change the mean difference were the MLSS time frame and delta criteria of blood lactate concentration, MLSS number and duration of pauses, CP longest predictive trial duration, CP type of predictive trials, CP model fitting parameters, and exercise modality. Covariates for SD of the differences were the subject's maximal oxygen uptake, CP's longest predictive trial duration, and exercise modality. Traditional MLSS protocol and CP from 2- to 15-minute trials do not reflect equivalent exercise intensity levels; the proximity between MLSS and CP measures can differ depending on test design, and both MLSS and CP have inherent limitations. Therefore, comparisons between them should always consider these aspects.

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We investigated fatigue and performance rates as decision-making criteria in pacing control during CrossFit®. Thirteen male regional-level competitors completed conditions of all-out (maximum physical work from beginning to end) and controlled-split (controlled physical work in the first two rounds but maximum work in the third round) pacing throughout the Fight Gone Bad workout separated by one week. We assessed benchmarks, countermovement jumps and ratings of fatigue after each round. Benchmarks were lower in round 1 (99 vs. 114, p < .001) but higher in rounds 2 (98 vs. 80, p < .001) and 3 (97 vs. 80, p < .001) for controlled-split compared with all-out pacing. Reductions in countermovement jumps were higher after rounds 1 (-12.6% vs. 1.6%, p < .001) and 2 (-12.7% vs. -4.0%, p = .014) but similar after round 3 (-13.2% vs. -11.3%, p = .571) for all-out compared with controlled-split pacing. Ratings of fatigue were higher after rounds 1 (7 vs. 5 a.u., p < .001) and 2 (8 vs. 7 a.u, p = .023) but similar after round 3 (9 vs. 9 a.u., p = .737) for all-out compared with controlled-split pacing. During all-out pacing, countermovement jump reductions after round 2 correlated with benchmark drops across rounds 1 and 2 (r = .78, p = .002) and rounds 1 and 3 (r = -.77, p = .002) and with benchmark workout changes between pacing strategies (r = -.58, p = .036), suggesting that the larger the countermovement jump reductions the higher the benchmark drops across rounds and workouts. Therefore, benchmarks, countermovement jumps and ratings of fatigue may assess exercise-induced fatigue as decision-making criteria to improve pacing strategy during workouts performed for as many repetitions as possible.

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This study aimed to compare the intensity of deoxygenated haemoglobin concentration ([HHb]) and tissue saturation index (TSI) breakpoints ([HHb]-BP and TSI-BP) with maximal lactate steady state (MLSS) and critical power (CP), and to describe their association with 2000-m rowing ergometer performance. Fourteen male rowers performed on a rowing ergometer: I) a discontinuous incremental test with 3-min stages (INC3); II) a continuous incremental test with 1-min stages (INC1); III) constant workload tests to determine MLSS; and IV) performance tests of 500 m, 1000 m, 2000 m and 6000 m to determine CP.CP (257 ± 39 W; 3.79 ± 4.1 L · min-1) was higher than [HHb]-BP3 (205 ± 26 W; 3.48 ± 2.9 L · min-1), [HHb]-BP1 (207 ± 27 W; 3.27 ± 3.2 L · min-1), and TSI-BP3 (218 ± 31 W; 3.51 ± 3.0 L · min-1), but not higher than TSI-BP1 (222 ± 34 W; 3.43 ± 3.2 L · min-1). MLSS (187 ± 26 W; 3.33 ± 3.2 L · min-1) was lower than TSI-BP3 and TSI-BP1 for power output, but not different in any comparison for ⩒O2. The limits of agreement for power output and ⩒O2 suggest poor agreement among these thresholds. The low level of agreement compromises the use of [HHb]-BP and TSI-BP for estimating MLSS and CP; therefore, these thresholds should not be considered interchangeable.

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In swimming, the speed-time relationship provides the critical speed (CS) and the maximum distance that can be performed above CS (D'). During intermittent severe intensity exercise, a complete D' depletion coincides with task failure, while a sub-CS intensity is required for D' reconstitution. Therefore, determining the balance D' remaining at any time during intermittent exercise (D'BAL) could improve training prescription. This study aimed to 1) test the D'BAL model for swimming; 2) determine an equation to estimate the time constant of the reconstitution of D' (τD'); and 3) verify if τD' is constant during two interval training sessions with the same work intensity and duration and recovery intensity, but different recovery duration. Thirteen swimmers determined CS and D' and performed two high-intensity interval sessions at a constant speed, with repetitions fixed at 50 m. The duration of passive recovery was based on the work/relief ratio of 2:1 (T2:1) and 4:1 (T4:1). There was a high variability between sessions for τD' (coefficient of variation of 306%). When τD' determined for T2:1 was applied in T4:1 and vice versa, the D'BAL model was inconsistent to predict the time to exhaustion (coefficient of variation of 29 and 28%). No linear or nonlinear relationships were found between τD' and CS, possibly due to the high within-subject variability of τD'. These findings suggest that τD' is not constant during two high-intensity interval sessions with the same recovery intensity. Therefore, the current D'BAL model was inconsistent to track D' responses for swimming sessions tested herein.

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The aim of this study was threefold: (a) to compare the maximal lactate steady state (MLSS) with critical power (CP); (b) to describe the relationship of MLSS with rowing performances; and (c) to verify the agreement of MLSS with several exercise intensity thresholds in rowers. Fourteen male rowers (mean [SD]: age = 26 [13] years; height = 1.82 [0.05] m; body mass = 81.0 [7.6] kg) performed on a rowing ergometer: (I) discontinuous incremental test with 3 min stages and 30-s recovery intervals (INC3min); (II) continuous incremental test with 60-s stages (INC1min); (III) two to four constant workload tests to determine MLSS; and (IV) performance tests of 500, 1000, 2000 and 6000 m to determine CP. Twenty-seven exercise intensity thresholds based on blood lactate, heart rate and ventilatory responses were determined by incremental tests, and then compared with MLSS. CP (257 [38] W) was higher than MLSS (187 [25] W; p < 0.001), with a very large mean difference (37%), large typical error of estimate (14%) and moderate correlation (r = 0.48). Despite the correlations between MLSS and most intensity thresholds (r > 0.70), all presented low correspondence (TEE > 5%), with a lower bias found between MLSS and the first intensity thresholds (-12.5% to 4.1%). MLSS was correlated with mean power during 500 m (r = 0.65), 1000 m (r = 0.86) and 2000 m (r = 0.78). In conclusion, MLSS intensity is substantially lower than CP and presented low agreement with 27 incremental-derived thresholds, questioning their use to estimate MLSS during rowing ergometer exercise.Highlights MLSS was substantially lower than CP in rowing exercise with a mean difference of 37%, much larger than the difference commonly found in running and cycling exercise (i.e., ?10%).A clear disagreement was reported between MLSS and 27 physiological thresholds determined in different incremental tests.There is a positive association of MLSS with 500, 1000 and 2000 m rowing ergometer performance tests.

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The present study aimed to compare maximal oxygen uptake of a step incremental test with time to exhaustion verification tests (TLIM) performed on the same or different day. Nineteen recreationally trained cyclists (age: 23 ± 2.7 years; maximal oxygen uptake: 48.0 ± 5.8 mL·kg-1·min-1) performed 3 maximal tests as follows: (i) same day: an incremental test with 3-min stages followed by a TLIM at 100% of peak power output of the incremental test (TLIM-SAME) interspaced by 15 min; and (ii) different day: a TLIM at 100% of peak power output of the incremental test (TLIM-DIFF). The maximal oxygen uptake was determined for the 3 tests. The maximal oxygen uptake was not different among the tests (incremental: 3.83 ± 0.41; TLIM-SAME: 3.72 ± 0.42; TLIM-DIFF: 3.75 ± 0.41 L·min-1; P = 0.951). Seven subjects presented a variability greater than ±3% in both verification tests compared with the incremental test. The same-day verification test decreased the exercise tolerance (240 ± 38 vs. 310 ± 36 s) compared with TLIM-DIFF (P < 0.05). In conclusion, the incremental protocol is capable of measuring maximal oxygen uptake because similar values were observed in comparison with verification tests. Although the need for the verification phase is questionable, the additional tests are useful to evaluate individual variability. Novelty Step incremental test is capable of measuring maximal oxygen uptake with similar values during TLIM on the same or different day. Although the necessity of the verification phase is questionable, it can allow the determination of variability in maximal oxygen uptake.

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PURPOSE: Although ischaemic preconditioning (IPC), induced by cycles of transient limb ischaemia and reperfusion, seems to improve exercise performance, the optimal duration of ischaemia-reperfusion cycles is not established. The present study investigated the effect of ischaemia-reperfusion duration within each IPC cycle on performance in a 2000-m rowing ergometer test. METHODS: After incremental and familiarization tests, 16 trained rowers (mean ± SD: age, 24 ± 11 years; weight, 74.1 ± 5.9 kg; [Formula: see text] peak, 67.2 ± 7.4 mL·kg-1·min-1) were randomly submitted to a 2000-m rowing test preceded by intermittent bilateral cuff inflation of the lower limbs with three cycles of ischaemia-reperfusion, lasting 5 min (IPC-5) or 10 min (IPC-10) at 220 or 20 mmHg (control). Power output, [Formula: see text], heart rate, blood lactate concentration, pH, ratings of perceived exertion (RPE), and near-infrared spectroscopy-derived measurements of the vastus lateralis muscle were continuously recorded. RESULTS: No differences among treatments were found in the 2000-m test (control: 424 ± 17; IPC-5: 425 ± 16; IPC-10: 424 ± 17 s; P = 0.772). IPC-10 reduced the tissue saturation index and oxy-haemoglobin concentration during exercise compared with control. The power output during the last 100-m segment was significantly lower with IPC-10. The IPC treatments increased the heart rate over the first 500 m and decreased the pH after exercise. No alterations were observed in [Formula: see text], blood lactate, or RPE among the trials. CONCLUSION: In conclusion, IPC does not improve the 2000-m rowing ergometer performance of trained athletes regardless of the length of ischaemia-reperfusion cycles.

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Rapid force capacity, identified by rate of rise in contractile force at the onset of contraction, i.e., the rate of force development (RFD), has been considered an important neuromuscular parameter of physical fitness in elderly individuals. Randomized control studies conducted in adults have found that resistance training may elicit different outcomes in terms of RFD and muscle strength. Thus, the main purpose of this study was to review systematically the literature for studies regarding the influence of resistance training on muscle strength and RFD in elderly persons. A literature search was performed in major electronic databases from inception to March 2017. Studies including health individuals with a mean age≥60years, describing the effect of resistance training on RFD and muscle strength were found eligible. The outcomes were calculated as the difference in percentage change between control and experimental groups (% change) and data were presented as mean±95% confidence limits. Meta-analyses were performed using a random-effects model and, in addition, simple and multiple meta-regression analyses were used to identify effects of age, training type, sessions per week and training duration on % change in RFD and muscle strength. Thirteen training effects were collected from 10 studies included in the meta-analysis. The resistance training program had a moderate beneficial effect on both muscle strength (% change=18.40%, 95% CL 13.69-23.30, p<0.001) and RFD (% change=26.68, 95% CL 14.41-35.52, p<0.001). Results of the meta-regression revealed that the variables age, training type (i.e., strength and explosive), training duration (4-16weeks) and sessions per week had no significant effects on muscle strength and RFD improvement. Moreover, there was no significant relationship (p=0.073) between the changes in muscle strength and RFD. It can be concluded that explosive training and heavy strength training are effective resistance training methods aiming to improve both muscle strength and RFD after short-to-medium training period. However, muscle strength and RFD seem to adapt differently to resistance training programs, suggesting caution for their interchangeable use in clinical assessments of the elderly.

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BACKGROUND: Several strategies have been used to improve running economy (RE). Defined as the oxygen uptake required at a given submaximal running velocity, it has been considered a key aerobic parameter related to endurance running performance. In this context, concurrent strength and endurance training has been considered an effective method, although conclusions on the optimal concurrent training cannot yet be drawn. OBJECTIVE: To evaluate the effect of concurrent training on RE in endurance running athletes and identify the effects of subject characteristics and concurrent training variables on the magnitude of RE improvement. METHODS: We conducted a computerized search of the PubMed and Web of Science databases, and references of original studies were searched for further relevant studies. The analysis comprised 20 effects in 16 relevant studies published up to August 2015. The outcomes were calculated as the difference in percentage change between control and experimental groups (% change) and data were presented as mean ± 95 % confidence limit. Meta-analyses were performed using a random-effects model and, in addition, simple and multiple meta-regression analyses were used to identify effects of age, training status, number of sessions per week, training duration, type of strength training, and neuromuscular performance on % change in RE. RESULTS: The concurrent training program had a small beneficial effect on RE (% change = -3.93 ± 1.19 %; p < 0.001). In addition, explosive (% change = -4.83 ± 1.53; p < 0.001) and heavy weight (% change = -3.65 ± 2.74; p = 0.009) training programs produced similar improvements in RE, while isometric training (% change = -2.20 ± 4.37; p = 0.324) in selected studies did not induce a significant effect. The multiple linear meta-regression analysis showed that all the differences between % changes could be explained by including the above-mentioned characteristics of subjects and weight training program elements. This model showed that the magnitude of the % change in RE was larger for longer training duration (ß = -0.83 ± 0.72, p = 0.02). CONCLUSION: Explosive training and heavy weight training are effective concurrent training methods aiming to improve RE within a few weeks. However, long-term training programs seem to be necessary when the largest possible improvement in RE is desired.

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PURPOSE: Although high-intensity interval training (HIT) seems to promote greater improvements in aerobic parameters than continuous training, the influence of exercise intensity on [Formula: see text] on-kinetics remains under investigation. METHODS: After an incremental test, twenty-one recreationally trained cyclists performed several time-to-exhaustion tests to determine critical power (CP), and the highest intensity (I HIGH), and the lowest exercise duration (T LOW) at which [Formula: see text] is attained during constant exercise. Subjects also completed a series of step transitions to moderate- and heavy-intensity work rates to determine pulmonary [Formula: see text] on-kinetics. Surface electromyography (EMG) of vastus lateralis muscle and blood lactate accumulation (∆BLC) was measured during heavy exercise. Subjects were assigned to one of two 4-week work-matched training groups: the lower [105 % CP: n = 11; 4 × 5 min at 105 % CP (218 ± 39 W), 1 min recovery] or the upper [I HIGH: n = 10; 8 × 100 % I HIGH (355 ± 60 W), 1:2 work:recovery ratio] intensity of the severe exercise domain. RESULTS: The two interventions were similarly effective in reducing the phase II [Formula: see text] time constant during moderate (105 % CP: 34 ± 13 to 25 ± 8 s; I HIGH: 31 ± 9 to 23 ± 6 s) and heavy exercise (105 % CP: 25 ± 7 to 18 ± 5 s; I HIGH: 27 ± 7 to 16 ± 5 s) and in reducing the amplitude of [Formula: see text] slow component, EMG amplitude, and ∆BLC during heavy exercise. CONCLUSION: In conclusion, the short-term adjustments in response to step transitions to moderate and heavy exercise were independent of training intensity within the severe exercise domain.

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