Determination of Lactate Thresholds in Maximal Running Test by Heart Rate Variability Data Set

Nascimento, Eduardo Marcel Fernandes; Kiss, Maria Augusta P. D.; Santos, Tony Meireles; Lambert, Mike

doi:10.5812/asjsm.58480

Cited by 6 publications

(23 citation statements)

References 28 publications

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“…SD2 marker has been shown to correlate with the PNS and SNS, and this variable shows a nonlinear pattern in intensities close to heavy and severe domains [2, 11]. The applicability and efficacy of the Poincaré plot in the estimation of the AeT and AnT have been confirmed in previous studies in running [11] and cycling [9, 15, 23].…”

Section: Introductionmentioning

confidence: 68%

“…The Poincaréplot analysis provides the calculation of the standard deviation of instantaneous (SD1) and continuous long-term RR intervals (SD2) [1]. The SD1 marker has been shown to correlate strongly with vagal tone (PNS), and previous studies have pointed to an abrupt point of change in their behavior in intensities related to AeT [2, 5, 9, 11]. SD2 marker has been shown to correlate with the PNS and SNS, and this variable shows a nonlinear pattern in intensities close to heavy and severe domains [2, 11].…”

Section: Introductionmentioning

confidence: 99%

“…The SD1 marker has been shown to correlate strongly with vagal tone (PNS), and previous studies have pointed to an abrupt point of change in their behavior in intensities related to AeT [2, 5, 9, 11]. SD2 marker has been shown to correlate with the PNS and SNS, and this variable shows a nonlinear pattern in intensities close to heavy and severe domains [2, 11]. The applicability and efficacy of the Poincaré plot in the estimation of the AeT and AnT have been confirmed in previous studies in running [11] and cycling [9, 15, 23].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the aim of the study is to investigate the application of the Dmax method originally proposed by Cheng et al [25], on HRV dataset to estimate the AeT and AnT, during a maximal incremental running test (MIRT) in male runners. Firstly, the hypothesis is that the HRVT identified by SD1 marker (HRVT DMAX1 ) could be used to estimate the AeT, since this marker has been shown to correlate with the PNS [2, 9, 11, 15]. Secondly, the hypothesis is that the HRVT identified by SD2 marker (HRVT DMAX2 ) could be used to estimate AnT, since this marker has been shown to correlate with a significant PNS and mainly with the SNS [11].…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, the hypothesis is that the HRVT identified by SD1 marker (HRVT DMAX1 ) could be used to estimate the AeT, since this marker has been shown to correlate with the PNS [2, 9, 11, 15]. Secondly, the hypothesis is that the HRVT identified by SD2 marker (HRVT DMAX2 ) could be used to estimate AnT, since this marker has been shown to correlate with a significant PNS and mainly with the SNS [11]. The reproducibility of the HRVT DMAX1 and HRVT DMAX2 , as well as AeT and AnT, will be verified.…”

Section: Introductionmentioning

confidence: 99%

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Applicability of Dmax Method on Heart Rate Variability to Estimate the Lactate Thresholds in Male Runners

Nascimento

Antunes

Salvador

et al. 2019

Journal of Sports Medicine

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Introduction. The purpose of this study was to evaluate the application of the Dmax method on heart rate variability (HRV) to estimate the lactate thresholds (LT), during a maximal incremental running test (MIRT). Methods. Nineteen male runners performed two MIRTs, with the initial speed at 8 km·h−1 and increments of 1 km·h−1 every 3 minutes, until exhaustion. Measures of HRV and blood lactate concentrations were obtained, and lactate (LT1 and LT2) and HRV (HRVTDMAX1 and HRVTDMAX2) thresholds were identified. ANOVA with Scheffe’s post hoc test, effect sizes (d), the bias ± 95% limits of agreement (LoA), standard error of the estimate (SEE), Pearson’s (r), and intraclass correlation coefficient (ICC) were calculated to assess validity. Results. No significant differences were observed between HRVTDMAX1 and LT1 when expressed for speed (12.1 ± 1.4 km·h−1 and 11.2 ± 2.1 km·h−1; p=0.55; d = 0.45; r = 0.46; bias ± LoA = 0.8 ± 3.7 km·h−1; SEE = 1.2 km·h−1 (95% CI, 0.9–1.9)). Significant differences were observed between HRVTDMAX2 and LT2 when expressed for speed (12.0 ± 1.2 km·h−1 and 14.1 ± 2.5 km·h−1; p=0.00; d = 1.21; r = 0.48; bias ± LoA = −1.0 ± 1.8 km·h−1; SEE = 1.1 km·h−1 (95% CI, 0.8–1.6)), respectively. Reproducibility values were found for the LT1 (ICC = 0.90; bias ± LoA = −0.7 ± 2.0 km·h−1), LT2 (ICC = 0.97; bias ± LoA = −0.1 ± 1.1 km·h−1), HRVTDMAX1 (ICC = 0.48; bias ± LoA = −0.2 ± 3.4 km·h−1), and HRVTDMAX2 (ICC = 0.30; bias ± LoA = 0.3 ± 3.5 km·h−1). Conclusions. The Dmax method applied over a HRV dataset allowed the identification of LT1 that is close to aerobic threshold, during a MIRT.

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Section: Introductionmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Applicability of Dmax Method on Heart Rate Variability to Estimate the Lactate Thresholds in Male Runners

Nascimento

Antunes

Salvador

et al. 2019

Journal of Sports Medicine

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Heart rate variability kinetics during different intensity domains of cycling exercise in healthy subjects

Nascimento

Salvador

Antunes

et al. 2021

European Journal of Sport Science

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The purpose of this study was to verify the heart rate variability (HRV) and heart rate (HR) kinetics during the fundamental phase in different intensity domains of cycling exercise. Fourteen males performed five exercise sessions: (1) maximal incremental cycling test; (2) two rest-to-exercise transitions for each intensity domain, that is, heavy (Δ30) and severe (Δ60) domains. HRV markers (SD1 and SD2) and HR kinetics in the fundamental phase were analyzed by first-order exponential fitting. There were no significant differences in amplitude values between SD1 Δ30 (8.98 ± 3.52 ms) and SD1 Δ60 (9.44 ± 3.24 ms) and SD2 Δ30 (24.93 ± 9.16 ms) and SD2 Δ60 (25.98 ± 7.29 ms). Significant difference was observed between HR Δ30 (52 ± 7 bpm) and HR Δ60 (63 ± 8 bpm). The time constant (τ) values were significantly different between SD1 Δ30 (17.61 ± 6.26 s) and SD1 Δ60 (13.86 ± 5.90 s), but not between SD2 Δ30 (20.06 ± 3.73 s) and SD2 Δ60 (19.47 ± 6.03 s) or HR Δ30 (56.75 ± 18.22 s) and HR Δ60 (58.49 ± 15.61 s). However, the τ values for HR Δ30 were higher and significantly different in relation to SD1 Δ30 and SD2 Δ30 , as well as for HR Δ60 in relation to SD1 Δ60 and SD2 Δ60 . The kinetics of the autonomic variable (SD1 marker) was accelerated by the increased intensity. Moreover, significant differences were found for the τ values, with faster HRV markers than HR, in both intensities of Δ30 and Δ60, which suggests that these variables indicate distinct and specific cardiac autonomic response times during different intensity domains in cycling. HIGHLIGHTS. The application of HRV to optimize exercise prescription at different effort intensities is extremely important to obtain assertive and effective results. . Analysis of the kinetic responses of HRV is a useful tool for the evaluation of exercise performance and health status. . A faster kinetics was found for HRV markers in comparison to HR, for both intensities analysed, which suggests that these variables indicate distinct and specific cardiac autonomic response times during different intensity domains in cycling.

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Heart Rate Variability-Derived Thresholds for Exercise Intensity Prescription in Endurance Sports: A Systematic Review of Interrelations and Agreement with Different Ventilatory and Blood Lactate Thresholds

et al. 2023

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Background Exercise intensities are prescribed using specific intensity zones (moderate, heavy, and severe) determined by a ‘lower’ and a ‘higher’ threshold. Typically, ventilatory (VT) or blood lactate thresholds (LT), and critical power/speed concepts (CP/CS) are used. Various heart rate variability-derived thresholds (HRVTs) using different HRV indices may constitute applicable alternatives, but a systematic review of the proximity of HRVTs to established threshold concepts is lacking. Objective This systematic review aims to provide an overview of studies that determined HRVTs during endurance exercise in healthy adults in comparison with a reference VT and/or LT concept. Methods A systematic literature search for studies determining HRVTs in healthy individuals during endurance exercise and comparing them with VTs or LTs was conducted in Scopus, PubMed and Web of Science (until January 2022). Studies claiming to describe similar physiological boundaries to delineate moderate from heavy (HRVTlow vs. VTlow and/or LTlow), and heavy from severe intensity zone (HRVThigh vs. VThigh and/or LThigh) were grouped and their results synthesized. Results Twenty-seven included studies (461 participants) showed a mean difference in relative HR between HRVTlow and VTlow of − 0.6%bpm in weighted means and 0.02%bpm between HRVTlow and LTlow. Bias between HR at HRVTlow and VTlow was 1 bpm (limits of agreement (LoA): − 10.9 to 12.8 bpm) and 2.7 bpm (LoA: − 20.4 to 25.8 bpm) between HRVTlow and LTlow. Mean difference in HR between HRVThigh and VThigh was 0.3%bpm in weighted means and 2.9%bpm between HRVThigh and LThigh while bias between HR at HRVThigh and VThigh was − 4 bpm (LoA: − 17.9 to 9.9 bpm) and 2.5 bpm (LoA: − 12.1 to 17.1 bpm) between HRVThigh and LThigh. Conclusion HRVTlow seems to be a promising approach for the determination of a ‘lower’ threshold comparable to VTlow and potentially for HRVThigh compared to VThigh, although the latter needs further empirical evaluation. LoA for both intensity zone boundaries indicates bias of HRVTs on an individual level. Taken together, HRVTs can be a promising alternative for prescribing exercise intensity in healthy, male athletes undertaking endurance activities but due to the heterogeneity of study design, threshold concepts, standardization, and lack of female participants, further research is necessary to draw more robust and nuanced conclusions.

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Determination of Lactate Thresholds in Maximal Running Test by Heart Rate Variability Data Set

Cited by 6 publications

References 28 publications

Applicability of Dmax Method on Heart Rate Variability to Estimate the Lactate Thresholds in Male Runners

Applicability of Dmax Method on Heart Rate Variability to Estimate the Lactate Thresholds in Male Runners

Heart rate variability kinetics during different intensity domains of cycling exercise in healthy subjects

Heart Rate Variability-Derived Thresholds for Exercise Intensity Prescription in Endurance Sports: A Systematic Review of Interrelations and Agreement with Different Ventilatory and Blood Lactate Thresholds

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