An integrative perspective of the anaerobic threshold

Sales, Marcelo Magalhães; Sousa, Caio Victor; Aguiar, Samuel da Silva; Knechtle, Beat; Nikolaidis, Pantelis Theodoros; Alves, Polissandro Mortoza; Simões, Herbert Gustavo

doi:10.1016/j.physbeh.2017.12.015

Cited by 34 publications

(21 citation statements)

References 36 publications

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“…At this point, there is an accentuated upswing in sympathetic nervous activity, reflecting a significant increase in catecholamines [15] and resulting in an increase in glycogenolysis and consequently elevated glycaemia. This characterizes the glycemic threshold [24], which in turn causes an increase in the use of blood glucose by the skeletal muscle, resulting in exacerbated production of anaerobic glycolisys metabolites (H + , lactate, CO 2 ) and marking thus the lactate and ventilatory thresholds [22,23].…”

Section: Discussionmentioning

confidence: 99%

Reproducibility of Heart Rate Variability Threshold in Untrained Individuals

et al. 2018

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The aim of this study was to evaluate the reproducibility of the heart rate variability threshold (HRVT) by different HRV indexes and determination criteria. 68 untrained participants, 17 women (24.09±4.91 years old; 21.54±1.97 kg∙m−2) and 51 men (24.52±3.52 years old; 26.51±6.31 kg∙m−2), were evaluated on 2 different days (test and retest). The HRVT was determined during an incremental exercise test using 2 indexes (SD1 and RMSSD) and criteria (HRTV1, first intensity of physical effort with index<3 ms, and HRVT2, first intensity of physical effort, in which the index presents a difference<1 ms between 2 consecutive intensities). There was no significant difference (p<0.05) between the test and retest for any of the variables evaluated. All variables, except for the rate of perceived exertion at HRVT2, presented moderate to high intraclass correlation coefficient (HRVT1: 0.55–0.85 and HRVT2:0.58–0.69). All variables at HRVT1 and the heart rate at HRVT2 showed coefficient of variation ~ 10%. The HRVT, regardless of criteria and HRV index used, showed satisfactory reproducibility. Thus, these criteria can be used to assess clinically autonomic cardiac modulation and aerobic capacity, and to analyze the effect of different interventions.

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Section: Discussionmentioning

confidence: 99%

Reproducibility of Heart Rate Variability Threshold in Untrained Individuals

et al. 2018

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“…Despite different methods to assess AT including metabolic markers (lactate, glucose), ventilatory responses, autonomic markers (e.g. HRV-derived markers such as RMSSD) or neuroendocrine markers (catecholamines), they all determine AT within close ranges [ 9 ]. However, as the physiological downstream is faster in some systems than in others, these different methods assessing the anaerobic threshold may show slightly different workloads at the AT.…”

Section: Introductionmentioning

confidence: 99%

Estimation of anaerobic threshold by cardiac repolarization instability: a prospective validation study

Schüttler

Krammer

Stülpnagel

et al. 2021

BMC Sports Sci Med Rehabil

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Background Assessing lactate (LT) or anaerobic thresholds (AT) in athletes is an important tool to control training intensities and to estimate individual performance levels. Previously we demonstrated that ECG-based assessment of cardiac repolarization instability during exercise testing allows non-invasive estimation of AT in recreational athletes. Here, we validate this method in professional and amateur team sports athletes. Methods We included 65 team sports athletes (32 professionals and 33 amateur athletes; 51 men, 14 women, mean age 22.3 ± 5.2 years) undergoing a standardized incremental cycle exercise test. During exercise testing a high-resolution ECG (1000 Hz) was recorded in Frank-leads configuration and beat-to-beat vector changes of cardiac repolarization (dT°) were assessed by previously established technologies. Repolarization-based AT (ATdT°) was estimated by its typical dT°-signal pattern. Additionally, LT was detected in accordance to methods established by Mader (LTMader) and Dickhuth (LTDickhuth). Results All athletes performed exercise testing until exhaustion with a mean maximum workload of 262.3 ± 60.8 W (241.8 ± 64.4 W for amateur athletes and 283.4 ± 49.5 W for professional athletes). Athletes showed ATdT° at 187.6 ± 44.4 W, LTDickhuth at 181.1 ± 45.6 W and LTMader at 184.3 ± 52.4 W. ATdT° correlated highly significantly with LTDickhuth (r = 0.96, p < 0.001) and LTMader (r = 0.98, p < 0.001) in the entire cohort of athletes as well as in the subgroups of professional and amateur athletes (p < 0.001 for all). Conclusions ATdT°, defined by the maximal discordance between dT° and heart rate, can be assessed reliably and non-invasively via the use of a high-resolution ECG in professional and amateur athletes.

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“…Lactate concentrations have been measured since the 1960s when the enzymatic method for measuring lactate concentrations from capillary blood samples was developed (Faude et al, 2009). Research has mainly focused on LT concepts in road running, which have been shown to correlate strongly to race performance (Faude et al, 2009; Sales et al, 2017; Svedahl and MacIntosh, 2003), but to date little is known about LT and performance prediction in trail running (Scheer et al, 2018a).…”

Section: Introductionmentioning

confidence: 99%

Predicting Competition Performance in Short Trail Running Races with Lactate Thresholds

Scheer

Vieluf

Janssen

et al. 2019

Journal of Human Kinetics

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Trail running is a popular sport, yet factors related to performance are still not fully understood. Lactate thresholds have been thoroughly investigated in road running and correlate strongly with race performance, but to date few data are available about the value in trail running performance prediction. We examined 25 trail runners (age 31.2 ± 5.1 years, BMI 22.2 ± 1.82 kg/m2) with an initial graded exercise test for measurement of VO2max (59.5 ± 5.2 ml.kg‐1.min‐ 1) and lactate thresholds (LT): LTAET (LT aerobic) 1.03 ± 0.59 mmol/l; 11.2 ± 1.1 km/h), IAT (individual lactate threshold) (2.53 ± 0.59 mmol/l; 15.4 ± 1.6 km/h) and LT4 (lactate threshold at 4 mmol/l) (16.2 ± 1.9 km/h). All runners subsequently participated in a 31.1 km XS trail race and 9 runners in a 21 km XXS trail race. Race performance times correlated negatively with the XS trail run (LTAET: r = ‐0.65, p < 0.01; LT4: r = ‐0.87, p < 0.01; IAT: r = ‐0.84, p < 0.01) and regression analysis showed that race performance could be predicted by: LT4: ‐324.15×LT4+13195.23 (R2 = .753, F1,23 = 70.02, p < 0.01). A subgroup analysis showed higher correlations with race performance for slower than faster runners. No correlations were found with the XXS race. Lactate thresholds can be of value in predicting trail race performance and help in designing training plans.

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An integrative perspective of the anaerobic threshold

Cited by 34 publications

References 36 publications

Reproducibility of Heart Rate Variability Threshold in Untrained Individuals

Reproducibility of Heart Rate Variability Threshold in Untrained Individuals

Estimation of anaerobic threshold by cardiac repolarization instability: a prospective validation study

Predicting Competition Performance in Short Trail Running Races with Lactate Thresholds

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