Measurement of anaerobic threshold during dynamic exercise in healthy subjects: comparison among visual analysis and mathematical models

Crescêncio, Júlio César; Martins, Luiz Eduardo Barreto; Murta, Leonardo; Antloga, Cleide Marques; Kozuki, R.T.; Santos, Marilene; Neto, J. Andrade; Maciel, Benedito Carlos; Gallo, Lourenço

doi:10.1109/cic.2003.1291278

Cited by 9 publications

(28 citation statements)

References 6 publications

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“…Other studies have identified AT using non-invasive methods by analysis of cardiorespiratory variables, developing mathematical models applied to V . CO 2 , such as multi-segmental linear regression (18) and linear-linear and linearquadratic bi-segmental regressions (8).…”

Section: Resultsmentioning

confidence: 99%

“…The interest in the identification of the critical intensity workload above which lactate accumulation occurs has a long history (4,5). Invasive methods require repeated blood lactate concentration measurements during physical exercise (6,7) while non-invasive methods are based on the analysis of changes in the response patterns of ventilatory and metabolic variables, such as V-slope (1,5) and the graphic visual method (8,9). Some studies (8,9) have used the graphic visual method for the estimate of the disproportionate increase in ventilatory and metabolic variables, during the incremental dynamic exercise, as a gold standard for the quantification of AT.…”

Section: Introductionmentioning

confidence: 99%

“…Invasive methods require repeated blood lactate concentration measurements during physical exercise (6,7) while non-invasive methods are based on the analysis of changes in the response patterns of ventilatory and metabolic variables, such as V-slope (1,5) and the graphic visual method (8,9). Some studies (8,9) have used the graphic visual method for the estimate of the disproportionate increase in ventilatory and metabolic variables, during the incremental dynamic exercise, as a gold standard for the quantification of AT. Additionally, other investigators have identified AT by analysis of heart rate (HR) (3,(10)(11)(12)(13) and of its variability (14), as well as by surface electromyography analysis (15)(16)(17).…”

Section: Introductionmentioning

confidence: 99%

“…Some investigators have proposed the use of mathematical and statistical models as a means to characterize responses of biological systems of the organism during physical exercise (8)(9)(10)18,19).…”

Section: Introductionmentioning

confidence: 99%

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Comparison of anaerobic threshold determined by visual and mathematical methods in healthy women

Higa

Silva

Neves

et al. 2007

Braz J Med Biol Res

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Several methods are used to estimate anaerobic threshold (AT) during exercise. The aim of the present study was to compare AT obtained by a graphic visual method for the estimate of ventilatory and metabolic variables (gold standard), to a bi-segmental linear regression mathematical model of Hinkley's algorithm applied to heart rate (HR) and carbon dioxide output (V . CO 2 ) data. Thirteen young (24 ± 2.63 years old) and 16 postmenopausal (57 ± 4.79 years old) healthy and sedentary women were submitted to a continuous ergospirometric incremental test on an electromagnetic braking cycloergometer with 10 to 20 W/min increases until physical exhaustion. The ventilatory variables were recorded breath-to-breath and HR was obtained beat-tobeat over real time. Data were analyzed by the nonparametric Friedman test and Spearman correlation test with the level of significance set at 5%. Power output (W), HR (bpm), oxygen uptake (V . . CO 2 and HR was adequate and promising for AT detection in young and middle-aged women, representing a semiautomatic, non-invasive and objective AT measurement.O

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of anaerobic threshold determined by visual and mathematical methods in healthy women

Higa

Silva

Neves

et al. 2007

Braz J Med Biol Res

View full text Add to dashboard Cite

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“…5,12,17 Nevertheless, determining the VT can be challenging in clinical conditions for several reasons. Many patients exhibit an abnormal ventilatory pattern, making it difficult to discern a clear breakpoint; the exercise protocol can influence the VT; and even experienced reviewers frequently differ in terms of detecting the VT. [18][19][20] Computerized metabolic systems use varying software programs to detect the VT automatically, but the algorithms used are often unknown. Furthermore, most of these systems will determine a VT when a VT may not exist or may not be discernible to a clinician.…”

Section: Résumémentioning

confidence: 99%

Comparaison des méthodes de détermination des seuils ventilatoires: implications pour la stratification du risque chirurgical

Vainshelboim

Rao

Chan

et al. 2017

Can J Anesth/J Can Anesth

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Purpose The ventilatory threshold (VT) is an objective physiological marker of the capacity of aerobic endurance that has good prognostic applications in preoperative settings. Nevertheless, determining the VT can be challenging due to physiological and methodological issues, especially in evaluating surgical risk. The purpose of the current study was to compare different methods of determining VT and to highlight the implications for assessing perioperative risk. Methods Our study entailed analysis of 445 treadmill cardiopulmonary exercise tests from 140 presurgical candidates with an aortic abdominal aneurysm (C3.0 to B5.0 cm) and a mean (standard deviation [SD]) age of 72 (8) yr. We used three methods to determine the VT in 328 comparable tests, namely, self-detected metabolic system (MS), experts' visual (V) readings, and software using a log-log transformation (LLT) of ventilation vs oxygen uptake. Differences and agreement between the three methods were assessed using analysis of variance (ANOVA), coefficient of variation (CV), typical error limits of agreement (LoA), and interclass correlation coefficients (ICC).Results Overall, ANOVA revealed significant differences between the methods [MS = 14.1 (4.3) mLO 2 Ákg -1 Ámin-1 ; V = 14.6 (4.4) mLO 2 Ákg -1 Ámin -1 ; and LLT = 12.3 (3.3) mLO 2 Ákg -1 Ámin -1 ; P \ 0.001]. The assessment of agreement between methods provided the following results: ICC = 0.85; 95% confidence interval (CI), 0.82 to 0.87; P \ 0.001; typical error, 2.1-2.8 mLO 2 Ákg -1 Ámin -1 ; and, 95% LoA and CV ranged from 43 to 55% and 15.9 to 19.6%, respectively. Conclusions The results show clinically significant variations between the methods and underscore the challenges of determining VT for perioperative risk stratification. The findings highlight the importance of meticulous evaluation of VT for predicting surgical outcomes. Future studies should address the prognostic perioperative utility of computed mathematical models combined with an expert's review. This trial was registered at ClinicalTrials.gov, identifier: NCT00349947.

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A new technique to analyse threshold-intensities based on time dependent change-points in the ratio of minute ventilation and end-tidal partial pressure of carbon-dioxide production

Ozkaya

Balci

et al. 2021

Respiratory Physiology & Neurobiology

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Measurement of anaerobic threshold during dynamic exercise in healthy subjects: comparison among visual analysis and mathematical models

Abstract: The anaerobic threshold (AT)

Cited by 9 publications

References 6 publications

Comparison of anaerobic threshold determined by visual and mathematical methods in healthy women

Comparison of anaerobic threshold determined by visual and mathematical methods in healthy women

Comparaison des méthodes de détermination des seuils ventilatoires: implications pour la stratification du risque chirurgical

A new technique to analyse threshold-intensities based on time dependent change-points in the ratio of minute ventilation and end-tidal partial pressure of carbon-dioxide production

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