Modeling lactate threshold in young squad athletes: influence of sex, maximal oxygen uptake, and cost of running.

PURPOSE: This study aimed to investigate: 1. The influence of sex and age on the accuracy of the classical model of endurance performance, including maximal oxygen uptake ([Formula: see text]), its fraction (LT2%), and cost of running (CR), for calculating running speed at lactate threshold 2 (vLT2) in young athletes. 2. The impact of different CR determination methods on the accuracy of the model. 3. The contributions of [Formula: see text], LT2%, and CR to vLT2 in different sexes. METHODS: 45 male and 55 female young squad athletes from different sports (age: 15.4 ± 1.3 years; [Formula: see text]: 51.4 ± 6.8 [Formula: see text]) performed an incremental treadmill test to determine [Formula: see text], LT2%, CR, and vLT2. CR was assessed at a fixed running speed (2.8 [Formula: see text]), at lactate threshold 1 (LT1), and at 80% of [Formula: see text], respectively. RESULTS: Experimentally determined and modeled vLT2 were highly consistent independent of sex and age (ICC [Formula: see text] 0.959). The accuracy of vLT2 modeling was improved by reducing random variation using individualized CR at 80% [Formula: see text] (± 4%) compared to CR at LT1 (± 7%) and at a fixed speed (± 8%). 97% of the total variance of vLT2 was explained by [Formula: see text], LT2%, and CR. While [Formula: see text] and CR showed the highest unique (96.5% and 31.9% of total [Formula: see text], respectively) and common (- 31.6%) contributions to the regression model, LT2% made the smallest contribution (7.5%). CONCLUSION: Our findings indicate: 1. High accuracy of the classical model of endurance performance in calculating vLT2 in young athletes independent of age and sex. 2. The importance of work rate selection in determining CR to accurately predict vLT2. 3. The largest contribution of [Formula: see text] and CR to vLT2, the latter being more important in female athletes than in males, and the least contribution of LT2%.

Consequences of police-related personal protective equipment and physical training status on thermoregulation and exercise energy expenditure.

BACKGROUND: The aim of this study was to examine the impact of personal protective equipment (PPE) on human thermoregulation and its alteration in groups of different training status. METHODS: Forty-five men performed a maximum voluntary contraction test in an upright pull position to determine lower body strength and a graded treadmill test to determine maximum oxygen uptake (V̇O2max). Body composition was estimated via bioelectric impedance analysis. According to specific cutoff values, participants were assigned to a group of endurance-trained, strength-trained, endurance- and strength-trained, or untrained individuals. Subsequently, they completed two graded exercise tests until volitional exhaustion, once wearing sports wear (SPW) and once wearing PPE (20.9 kg). Participants were weighed before and afterward to investigate sweat loss and sweat rate. Body temperature was measured continuously from the tympanic membrane. Energy expenditure was derived from breathing gas analysis. RESULTS: Sweat rate was 91% higher in PPE than in SPW but not significantly different between groups (P>0.05). Body temperature was significantly higher in PPE during submaximal (+1.14±0.45 °C) and maximal exercise intensity (0.68±0.57 °C) and was poorely related to V̇O2max and body composition. Energy expenditure significantly differed between both garments (+37% in PPE) and groups (P<0.05). Additionally, energy expenditure significantly correlated with body weight (r=0.84 in SPW and r=0.68 in PPE). CONCLUSIONS: Strength training alone does not seem to have any or negligible effects on thermoregulation. Endurance training and weight management might lead to rather small improvements in heat tolerance.

How Fit Are Special Operations Police Officers? A Comparison With Elite Athletes From Olympic Disciplines.

The diverse tasks of special operations police (SOP) units place high physical demands on every officer. Being fit for duty requires a wide range of motor abilities which must be trained regularly and in a structured manner. But SOP operators have to plan and manage large proportions of their training alone, which makes it difficult to control. Therefore, this study aimed to highlight strengths and deficits of the SOP operators' fitness by comparing them to elite athletes, and to define future training goals. Retrospective data of 189 male SOP operators were used, who completed several isometric strength tests, a graded exercise test to determine maximal oxygen uptake, and countermovement jumps to determine leg muscle power. On the basis of a literature search, performance data were then compared to a total of 3,028 elite male athletes from 36 Summer Olympic disciplines. Pooled means and standard deviations were calculated for each discipline and effect sizes were used to analyze their similarities and differences to the SOP unit. On average, SOP operators were taller, heavier, and stronger than elite athletes. But both the ability to convert this strength into explosive movement and aerobic power was significantly less developed. From this point of view, SOP operators should consider polarized endurance training to work efficiently on improving aerobic performance. In addition, regular plyometric training seems necessary to improve leg muscle power and agility.

Reverse lactate threshold test accurately predicts maximal lactate steady state and 5 km performance in running.

This study evaluated the accuracy of the reverse lactate threshold (RLT) and the onset of blood lactate accumulation (OBLA; 4 mmol·L-1) to determine the running speed at the maximal lactate steady state (MLSS) and 5 km running performance in a field test approach. Study 1: 16 participants performed an RLT test, and 2 or more constant-speed tests, lasting 30 minutes each, to determine running speed at the MLSS. Study 2: 23 participants performed an RLT test and a 5000 m all-out run as an indicator of performance. The RLT test consisted of an initial lactate-priming segment, in which running speed was increased stepwise up to ~5% above the estimated MLSS, followed by a reverse segment in which speed was decreased by 0.1 m·s-1 every 180 s. RLT was determined using the highest lactate equivalent ([La-]/running speed) during the reverse segment. OBLA was determined during the priming segment and was set at a value of 4 mmol∙L1. The mean difference in MLSS was +0.06 ± 0.05 m·s-1 for RLT, and +0.13 ± 0.23 m·s-1 for OBLA. OBLA showed a good concordance with the MLSS (ICC = 0.83), whereas RLT revealed excellent concordance with the MLSS with an ICC = 0.98. RLT showed a very high correlation with 5000 m speed (r = 0.97). The RLT exhibited exceptional agreement to MLSS and 5000 m running performance. Due to this high accuracy, especially concerning the small intraindividual differences, the RLT test may be superior to common threshold concepts. Further research is needed to evaluate its sensitivity during the training process.

The effect of physical training modality on exercise performance with police-related personal protective equipment.

PURPOSE: To investigate the influence of aerobic capacity, muscle strength, and body composition on performance and metabolic demands of men wearing personal protective equipment (PPE). METHODS: 45 men were assigned to one of four groups which significantly differed in upright pull isometric strength (MVC ≤ 1325 N or ≥ 1531 N) and maximum oxygen uptake (VO2max ≤ 51.9 mL min-1·kg-1 or ≥ 56.0 mL min-1·kg-1): endurance-trained (low MVC, high VO2max), strength-trained (high MVC, low VO2max), endurance- and strength-trained (high MVC, high VO2max), and untrained (low MVC, low VO2max). Each participant underwent two test series consisting of a repeated 10 m dummy drag and a graded exercise test wearing either sportswear or PPE of a German riot police unit weighing 20.9 kg (statistics: two-way repeated measures ANOVA, stepwise multiple linear regressions). RESULTS: With PPE, dummy drag and running performance were impaired by 14 ± 9% and 58 ± 7%. Groups with high MVC dragged the dummy significantly faster than groups with low MVC (17.5 ± 1.8 s/17.6 ± 1.4 s vs. 23.4 ± 5.6 s/22.3 ± 3.5 s). Running distance was significantly higher in groups with high VO2max (4.5 ± 0.8 km/4.4 ± 0.7 km vs. 3.1 ± 0.5 km/2.8 ± 0.5 km). Body composition variables partially correlated with performance (R ranging from -0.70 to 0.41), but were not significant predictors of the regression models in PPE. CONCLUSIONS: Individuals who showed a certain degree of aerobic endurance, as well as muscle strength, performed consistently well during the test series. Therefore, none of these variables should be trained in isolation but optimized in combination to be capable in a variety of operational tasks.

Power profile, physiological characteristics and their correlation in elite canoe polo players.

BACKGROUND: This study aimed to examine the physical capabilities of elite canoe polo players and to identify interrelationships between anthropometric or physiological characteristics and performance on a kayak ergometer. METHODS: Eight male participants (age 24.6±4.8 years, weight 84.1±5.3 kg, height 180.0±5.9 cm) completed four all-out time trials (15 s, 180 s, 420 s, 900 s) to determine peak power output (PPO), mean power output (MPO), maximal oxygen uptake (VO2max), maximal rate of lactate accumulation (VLamax), and maximal lactate steady-state (cMLSS). Critical power (CP) and work that can be performed above CP (W') were assessed using a linear power model. Further, the 30-second end-test power (EP) and work done above EP (WEP) were derived from the 180 s time trial. Finally, indices were calculated from the metabolic and power data. The level of significance was set at P≤0.05. RESULTS: Weak to moderate correlations were found for body weight and height compared to PPO and MPOs. VO2max correlated strongly with MPO180 and MPO420. VLamax correlated moderately with PPO and MPO15. While the calculations of CP, EP, and cMLSS correlated moderately to strongly, their means differed significantly. W' and WEP also differed significantly with a mean difference of 10.2±2.5 kJ. CONCLUSIONS: Canoe polo players are similar to sprint paddlers in their constitution, although VO2max and PPO are lower. The high correlation between physiological and power parameters also shows that simple tests that do not require blood or gas sampling can be established quickly in daily training practice.

Modifications of the Dmax method in comparison to the maximal lactate steady state in young male athletes.

OBJECTIVE: To investigate the influence of different approaches for first-rise determination on the accuracy of Dmax as an estimate of the maximal lactate steady state (MLSS). METHODS: Seventeen male cyclists and 18 male runners with different levels of endurance performance completed graded exercise tests either on a cycle ergometer or treadmill to determine Dmax, calculated by the final data point and five modifications of the first rise in blood lactate concentration. Two or more constant load tests over 30 min were performed to determine MLSS. Differences between the modifications of the first rise in blood lactate concentration as well as the corresponding Dmax variants and MLSS were tested, using one-way repeated measure ANOVA with Bonferroni post-hoc tests, and illustrated, using the Bland-Altman method. The absolute agreement was observed, using intra-class correlation coefficients, based on a single measure, absolute agreement, 2-way mixed effects model. RESULTS: The peak power output/running velocity of the groups averaged 275 ± 43 W and 4.3 ± 0.4 m · s-1, respectively. The mean power output/running velocity at MLSS was 229 ± 38 W and 3.77 ± 0.38 m · s-1. For both running and cycling the original Dmax described by Cheng et al. was significantly lower than MLSS (p < 0.01). All modifications showed good agreement with MLSS (ICC ≥0.75). According to the Bland-Altman method the mean differences of the modifications compared to MLSS in cycling ranged from -7 (43) to 2 (41) W. In running the mean differences ranged from -0.12 (0.34) to -0.08 (0.35) m· s-1. CONCLUSION: We suggest using the first rise in blood lactate concentration for calculating Dmax instead of the first data point of a lactate curve as originally described. The approach of first rise determination has no substantial influence on the accuracy of Dmax compared to MLSS in cycling and running.

Higher Accuracy of the Lactate Minimum Test Compared to Established Threshold Concepts to Determine Maximal Lactate Steady State in Running.

This study evaluated the accuracy of the lactate minimum test, in comparison to a graded-exercise test and established threshold concepts (OBLA and mDmax) to determine running speed at maximal lactate steady state. Eighteen subjects performed a lactate minimum test, a graded-exercise test (2.4 m·s-1 start,+0.4 m·s-1 every 5 min) and 2 or more constant-speed tests of 30 min to determine running speed at maximal lactate steady state. The lactate minimum test consisted of an initial lactate priming segment, followed by a short recovery phase. Afterwards, the initial load of the subsequent incremental segment was individually determined and was increased by 0.1 m·s-1 every 120 s. Lactate minimum was determined by the lowest measured value (LMabs) and by a third-order polynomial (LMpol). The mean difference to maximal lactate steady state was+0.01±0.14 m·s-1 (LMabs), 0.04±0.15 m·s-1 (LMpol), -0.06±0.31 m·s1 (OBLA) and -0.08±0.21 m·s1 (mDmax). The intraclass correlation coefficient (ICC) between running velocity at maximal lactate steady state and LMabs was highest (ICC=0.964), followed by LMpol (ICC=0.956), mDmax (ICC=0.916) and OBLA (ICC=0.885). Due to the higher accuracy of the lactate minimum test to determine maximal lactate steady state compared to OBLA and mDmax, we suggest the lactate minimum test as a valid and meaningful concept to estimate running velocity at maximal lactate steady state in a single session for moderately up to well-trained athletes.