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      GEDAE-LaB: A Free Software to Calculate the Energy System Contributions during Exercise

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          Abstract

          Purpose

          The aim of the current study is to describe the functionality of free software developed for energy system contributions and energy expenditure calculation during exercise, namely GEDAE-LaB.

          Methods

          Eleven participants performed the following tests: 1) a maximal cycling incremental test to measure the ventilatory threshold and maximal oxygen uptake (

          O 2max); 2) a cycling workload constant test at moderate domain (90% ventilatory threshold); 3) a cycling workload constant test at severe domain (110%
          O 2max). Oxygen uptake and plasma lactate were measured during the tests. The contributions of the aerobic (A MET), anaerobic lactic (LA MET), and anaerobic alactic (AL MET) systems were calculated based on the oxygen uptake during exercise, the oxygen energy equivalents provided by lactate accumulation, and the fast component of excess post-exercise oxygen consumption, respectively. In order to assess the intra-investigator variation, four different investigators performed the analyses independently using GEDAE-LaB. A direct comparison with commercial software was also provided.

          Results

          All subjects completed 10 min of exercise at moderate domain, while the time to exhaustion at severe domain was 144 ± 65 s. The A MET, LA MET, and AL MET contributions during moderate domain were about 93, 2, and 5%, respectively. The A MET, LA MET, and AL MET contributions during severe domain were about 66, 21, and 13%, respectively. No statistical differences were found between the energy system contributions and energy expenditure obtained by GEDAE-LaB and commercial software for both moderate and severe domains ( P > 0.05). The ICC revealed that these estimates were highly reliable among the four investigators for both moderate and severe domains (all ICC ≥ 0.94).

          Conclusion

          These findings suggest that GEDAE-LaB is a free software easily comprehended by users minimally familiarized with adopted procedures for calculations of energetic profile using oxygen uptake and lactate accumulation during exercise. By providing availability of the software and its source code we hope to facilitate future related research.

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          Most cited references22

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          Influence of exercise intensity on the on- and off-transient kinetics of pulmonary oxygen uptake in humans.

          H Rossiter, F Ozyener, B Whipp … (2001)
          1. The maximal oxygen uptake (V(O(2),peak)) during dynamic muscular exercise is commonly taken as a crucial determinant of the ability to sustain high-intensity exercise. Considerably less attention, however, has been given to the rate at which V(O(2)) increases to attain this maximum (or to its submaximal requirement), and even less to the kinetic features of the response following exercise. 2. Six, healthy, male volunteers (aged 22 to 58 years), each performed 13 exercise tests: initial ramp-incremental cycle ergometry to the limit of tolerance and subsequently, on different days, three bouts of square-wave exercise each at moderate, heavy, very heavy and severe intensities. Pulmonary gas exchange variables were determined breath by breath throughout exercise and recovery from the continuous monitoring of respired volumes (turbine) and gas concentrations (mass spectrometer). 3. For moderate exercise, the V(O(2)) kinetics were well described by a simple mono-exponential function, following a short cardiodynamic phase, with the on- and off-transients having similar time constants (tau(1)); i.e. tau(1,on) averaged 33 +/- 16 s (+/- S.D.) and tau(1,off) 29 +/- 6 s. 4. The on-transient V(O(2)) kinetics were more complex for heavy exercise. The inclusion of a second slow and delayed exponential component provided an adequate description of the response; i.e. tau(1,on) = 32 +/- 17 s and tau(2,on) = 170 +/- 49 s. The off-transient V(O(2)) kinetics, however, remained mono-exponential (tau(1,off) = 42 +/- 11 s). 5. For very heavy exercise, the on-transient V(O(2)) kinetics were also well described by a double exponential function (tau(1,on) = 34 +/- 11 s and tau(2,on) = 163 +/- 46 s). However, a double exponential, with no delay, was required to characterise the off-transient kinetics (i.e. tau(1,off) = 33 +/- 5 s and tau(2,off) = 460 +/- 123 s). 6. At the highest intensity (severe), the on-transient V(O(2)) kinetics reverted to a mono-exponential profile (tau(1,on) = 34 +/- 7 s), while the off-transient kinetics retained a two-component form (tau(1,off) = 35 +/- 11 s and tau(2,off) = 539 +/- 379 s). 7. We therefore conclude that the kinetics of V(O(2)) during dynamic muscular exercise are strikingly influenced by the exercise intensity, both with respect to model order and to dynamic asymmetries between the on- and off-transient responses.
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            Energy system interaction and relative contribution during maximal exercise.

            P Gastin (2000)
            There are 3 distinct yet closely integrated processes that operate together to satisfy the energy requirements of muscle. The anaerobic energy system is divided into alactic and lactic components, referring to the processes involved in the splitting of the stored phosphagens, ATP and phosphocreatine (PCr), and the nonaerobic breakdown of carbohydrate to lactic acid through glycolysis. The aerobic energy system refers to the combustion of carbohydrates and fats in the presence of oxygen. The anaerobic pathways are capable of regenerating ATP at high rates yet are limited by the amount of energy that can be released in a single bout of intense exercise. In contrast, the aerobic system has an enormous capacity yet is somewhat hampered in its ability to delivery energy quickly. The focus of this review is on the interaction and relative contribution of the energy systems during single bouts of maximal exercise. A particular emphasis has been placed on the role of the aerobic energy system during high intensity exercise. Attempts to depict the interaction and relative contribution of the energy systems during maximal exercise first appeared in the 1960s and 1970s. While insightful at the time, these representations were based on calculations of anaerobic energy release that now appear questionable. Given repeated reproduction over the years, these early attempts have lead to 2 common misconceptions in the exercise science and coaching professions. First, that the energy systems respond to the demands of intense exercise in an almost sequential manner, and secondly, that the aerobic system responds slowly to these energy demands, thereby playing little role in determining performance over short durations. More recent research suggests that energy is derived from each of the energy-producing pathways during almost all exercise activities. The duration of maximal exercise at which equal contributions are derived from the anaerobic and aerobic energy systems appears to occur between 1 to 2 minutes and most probably around 75 seconds, a time that is considerably earlier than has traditionally been suggested.
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              How anaerobic is the Wingate Anaerobic Test for humans?

              Ralph Beneke, I Bleif, C Pollmann … (2002)
              The Wingate Anaerobic Test (WAnT) is generally used to evaluate anaerobic cycling performance, but knowledge of the metabolic profile of WAnT is limited. Therefore the energetics of WAnT was analysed with respect to working efficiency and performance. A group of 11 male subjects [mean (SD), age 21.6 (3.8) years, height 178.6 (6.6) cm, body mass 82.2 (12.1) kg] performed a maximal incremental exercise test and a WAnT. Lactic and alactic anaerobic energy outputs were calculated from net lactate production and the fast component of the kinetics of post-exercise oxygen uptake. Aerobic metabolism was determined from oxygen uptake during exercise. The WAnT mean power of 683 (96.0) W resulted from a total energy output above the value at rest of 128.1 (23.2) kJ x 30 s(-1) [mean metabolic power=4.3 (0.8) kW] corresponding to a working efficiency of 16.2 (1.6)%. The WAnT working efficiency was lower (P < 0.01) than the corresponding value of 24.1 (1.7)% at 362 (41) W at the end of an incremental exercise test. During WAnT the fractions of the energy from aerobic, anaerobic alactic and lactic acid metabolism were 18.6 (2.5)%, 31.1 (4.6)%, and 50.3 (5.1)%, respectively. Energy from metabolism of anaerobic lactic acid explained 83% and 81% of the variance of WAnT peak and mean power, respectively. The results indicate firstly that WAnT requires the use of more anaerobically derived energy than previously estimated, secondly that anaerobic metabolism is dominated by glycolysis, thirdly that WAnT mechanical efficiency is lower than that found in aerobic exercise tests, and fourthly that the latter finding partly explains discrepancies between previously published and the present data about the metabolic profile of WAnT.
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                Author and article information

                Contributors
                Jonathan Peterson: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 4 January 2016
                Publication date Collection: 2016
                Volume: 11
                Issue: 1
                Electronic Location Identifier: e0145733
                Affiliations
                [1 ]Endurance Performance Research Group (GEDAE-USP), School of Physical Education and Sport, University of São Paulo, São Paulo, Brazil
                [2 ]Sports Science Research Group, Department of Physical Education and Sports Science (CAV), Federal University of Pernambuco, Pernambuco, Brazil
                [3 ]Systems and Software Group, Department of Computer Science, University of São Paulo, São Paulo, Brazil
                East Tennessee State University, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: RB AELS JM AG. Performed the experiments: SB TG LAP AFG JM. Analyzed the data: RB JM SB TG LAP AFG AELS AG. Contributed reagents/materials/analysis tools: RB JM AFG. Wrote the paper: RB JM SB TG LAP AFG AELS AG.

                Article
                Publisher ID: PONE-D-15-34859
                DOI: 10.1371/journal.pone.0145733
                PMC ID: 4699761
                PubMed ID: 26727499
                SO-VID: 1b17d503-f6ba-4cdd-b605-692f120d7a20
                Copyright © © 2016 Bertuzzi et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                Date received : 7 August 2015
                Date accepted : 8 December 2015
                Page count
                Figures: 1, Tables: 5, Pages: 13
                Funding
                This study was supported by NAPSoL, CAPES, and CNPq. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Research Article
                Custom metadata
                Data Availability All relevant data regarding the GEDAE_LaB are available on http://www.gedaelab.org/ and the code is available on Github at the URL https://github.com/physusp/physusp/.

                ScienceOpen disciplines: Uncategorized
                Data availability:
                ScienceOpen disciplines: Uncategorized

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