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DOI: 10.1055/a-2495-5364
The prevalence of gas exchange data processing methods: a semi-automated scoping review
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Abstract
Cardiopulmonary exercise testing involves collecting variable breath-by-breath data and sometimes requiring data processing of outlier removal, interpolation, and averaging before later analysis. These data processing choices, such as averaging duration, affect calculated values such as ˙VO2max. However, assessing the implications of data processing without knowing popular methods worth comparing is difficult. In addition, such details aid study reproduction. We conducted a semi-automated scoping review of articles with exercise testing that collected data breath-by-breath from three databases. Of the 8,344 articles, 376 (mean: 4.5% and 95% confidence interval: 4.1–5.0%) and 581 (mean: 7.0% and 95% confidence interval: 6.4–7.5%) described outlier removal and interpolation, respectively. A random subset of 1,078 articles revealed (mean: 60.9% and 95% confidence interval: 57.9–63.7%) the reported averaging methods. The commonly documented outlier cutoffs were±3 or 4 SD (39.1 and 51.6%, respectively). The dominating interpolation duration and procedure were 1 s (93.9%) and linear interpolation (92.5%). Averaging methods commonly described were 30 (30.9%), 60 (12.4%), 15 (11.6%), 10 (11.0%), and 20 (8.1%) second bin averages. This shows that studies collecting breath-by-breath data often lack detailed descriptions of data processing methods, particularly for outlier removal and interpolation. While averaging methods are more commonly reported, improved documentation across all processing steps will enhance reproducibility and facilitate future research comparing data processing choices.
Keywords
averaging - outliers - interpolation - cardiopulmonary exercise testing - breath-by-breath - reproducibilityPublication History
Article published online:
20 January 2025
© 2024. Thieme. All rights reserved.
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References
- 1 Pescatello LS. ACSM’s guidelines for exercise testing and prescription. 9th edn Philadelphia: Wolters Kluwer/Lippincott Williams & Wilkins Health; 2014: 162
- 2 Jamnick NA, Pettitt RW, Granata C. et al. An Examination and Critique of Current Methods to Determine Exercise Intensity. Sports Med 2020; 50: 1729-1756
- 3 Robergs RA, Dwyer D, Astorino T. Recommendations for Improved Data Processing from Expired Gas Analysis Indirect Calorimetry. Sports Med 2010; 40: 95-111
- 4 Robergs RA, Burnett AF. Methods Used to Process Data from Indirect Calorimetry and Their Application to ˙VO2 Max. J Exerc Physiol Online 2003; 6: 44-57
- 5 Sousa A, Figueiredo P, Oliveira N. et al. Comparison Between Swimming ˙VO2peak and ˙VO2max at Different Time Intervals. Open Sports Sci J 2010; 3: 22-24
- 6 Johnson JS, Carlson JJ, VanderLaan RL. et al. Effects of Sampling Interval on Peak Oxygen Consumption in Patients Evaluated for Heart Transplantation. Chest 1998; 113: 816-819
- 7 Sell KM, Ghigiarelli JJ, Prendergast JM. et al. Comparison of ˙VO2 peak and ˙VO2 max at Different Sampling Intervals in Collegiate Wrestlers. J Strength Cond Res 2021; 35: 2915-2917
- 8 Midgley AW, McNaughton LR, Carroll S. Effect of the ˙VO2 time-averaging interval on the reproducibility of ˙VO2 max in healthy athletic subjects. Clin Physiol Funct Imaging 2007; 27: 122-125
- 9 Astorino TA. Alterations in ˙VO2 max and the ˙VO2 plateau with manipulation of sampling interval. Clin Physiol Funct Imaging 2009; 29: 6067
- 10 Astorino TA, Robergs RA, Ghiasvand F. et al. Incidence of the oxygen plateau at ˙VO2max during exercise testing to volitional fatigue. J Exerc Physiol Online 2000; 3: 112
- 11 Martin-Rincon M, González-Henríquez JJ, Losa-Reyna J. et al. Impact of data averaging strategies on ˙VO2 max assessment: mathematical modeling and reliability. Scand J Med Sci Sports 2019; 29: 1473-1488
- 12 Martin-Rincon M, Calbet JAL. Progress Update and Challenges on ˙VO2max Testing and Interpretation. Front Physiol 2020; 11: 1070
- 13 Scheadler CM, Garver MJ, Hanson NJ. The Gas Sampling Interval Effect on ˙VO2peak Is Independent of Exercise Protocol. Med Sci Sports Exerc 2017; 49: 1911-1916
- 14 de Jesus K, Guidetti L, de Jesus K. et al. Which Are the Best ˙VO2 Sampling Intervals to Characterize Low to Severe Swimming Intensities?. Int J Sports Med 2014; 35: 1030-1036
- 15 Hill DW, Stephens LP, Blumoff-Ross SA. et al. Effect of sampling strategy on measures of ˙VO2peak obtained using commercial breath-by-breath systems. Eur J Appl Physiol 2003; 89: 564-569
- 16 Smart NA, Jeffriess L, Giallauria F. et al. Effect of duration of data averaging interval on reported peak ˙VO2 in patients with heart failure. Int J Cardiol 2015; 182: 530-533
- 17 Matthews JI, Bush BA, Morales FM. Microprocessor Exercise Physiology Systems vs a Nonautomated System. Chest 1987; 92: 696-703
- 18 Poole DC, Jones AM. Measurement of the maximum oxygen uptake ˙VO2 max: ˙VO2 peak is no longer acceptable. J Appl Physiol 2017; 122: 997-1002
- 19 Nolte S, Rein R, Quittmann OJ. Data Processing Strategies to Determine Maximum Oxygen Uptake: a Systematic Scoping Review and Experimental Comparison with Guidelines for Reporting. Sports Med 2023; 53: 2463-2475
- 20 Lamarra N, Whipp BJ, Ward SA. et al. Effect of interbreath fluctuations on characterizing exercise gas exchange kinetics. J Appl Physiol 1987; 62: 20032012
- 21 Keir DA, Murias JM, Paterson DH. et al. Breath-by-breath pulmonary O2 uptake kinetics: effect of data processing on confidence in estimating model parameters. Exp Physiol 2014; 99: 15111522
- 22 Benson AP, Bowen TS, Ferguson C. et al. Data collection, handling, and fitting strategies to optimize accuracy and precision of oxygen uptake kinetics estimation from breath-by-breath measurements. J Appl Physiol 2017; 123: 227242
- 23 Francescato MP, Cettolo V, Bellio R. Confidence intervals for the parameters estimated from simulated O2 uptake kinetics: effects of different data treatments. Exp Physiol 2014; 99: 187195
- 24 Francescato MP, Cettolo V. The 1-s interpolation of breath-by-breath O2 uptake data to determine kinetic parameters: the misleading procedure. Sport Sci Health 2019; 16: 193
- 25 Francescato MP, Cettolo V, Bellio R. Interpreting the confidence intervals of model parameters of breath-by-breath pulmonary O2 uptake. Exp Physiol 2015; 100: 475475
- 26 Goodman SN, Fanelli D, Ioannidis JPA. What does research reproducibility mean. Sci Transl Med 2016; 8: 341ps12
- 27 Open Science Collaboration. Estimating the reproducibility of psychological science. Science (1979) 2015; 349: aac4716
- 28 Tricco AC, Lillie E, Zarin W. et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med 2018; 169: 467-473
- 29 Peters MDJ, Marnie C, Tricco AC. et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evid Synth 2020; 18: 2119-2126
- 30 Page MJ, McKenzie JE, Bossuyt PM. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. PLoS Med 2021; 18: e1003583
- 31 Foster ED, Deardorff A. Open Science Framework (OSF). J Med Libr Assoc 2017; 105
- 32 2022
- 33 2023
- 34 NCBI. 2022 https://www.ncbi.nlm.nih.gov/pmc/tools/id-converter-api/ [stand: 6.4.2022].
- 35 Unpaywall: an open database of 20 million free scholarly articles. https://unpaywall.org/ [stand: 24.7.2024]
- 36 Haddaway NR, Page MJ, Pritchard CC. et al. PRISMA2020 : An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis. Campbell Systematic Reviews 2022; 18: e1230
- 37 Bojanowski P, Grave E, Joulin A. et al. Enriching word vectors with subword information. arXiv Preprint 2016; arXiv:160704606
- 38 Pedregosa F, Varoquaux G, Gramfort A. et al. Scikit-learn: machine learning in Python. J Mach Learn Res 2011; 12: 28252830
- 39 Brown LD, Cai TT, DasGupta A. Interval Estimation for a Binomial Proportion. Statist Sci 2001; 16
- 40 Breese BC, Saynor ZL, Barker AR. et al. Relationship between (non)linear phase II pulmonary oxygen uptake kinetics with skeletal muscle oxygenation and age in 11–15 year olds. Exp Physiol 2019; 104: 1929-1941
- 41 Hartman ME, Ekkekakis P, Dicks ND. et al. Dynamics of pleasure-displeasure at the limit of exercise tolerance: conceptualizing the sense of exertional physical fatigue as an affective response. J Exp Biol 2018; 222: jeb.186585
- 42 Hassinen M, Lakka TA, Savonen K. et al. Cardiorespiratory Fitness as a Feature of Metabolic Syndrome in Older Men and Women. Diabetes Care 2008; 31: 1242-1247
- 43 Deboeck G, Niset G, Lamotte M. et al. Exercise testing in pulmonary arterial hypertension and in chronic heart failure. Eur Respir J 2004; 23: 747-751
- 44 R Core Team. 2021
- 45 Blair SN, Kohl HW, Barlow CE. et al. Changes in physical fitness and all-cause mortality: a prospective study of healthy and unhealthy men. JAMA 1995; 273: 10931098
- 46 Robergs RA. An exercise physiologist’s “contemporary” interpretations of the” ugly and creaking edifices” of the ˙VO2max concept. J Exerc Physiol Online 2001; 4: 144
- 47 Myers J, Walsh D, Buchanan N. et al. Can maximal cardiopulmonary capacity be recognized by a plateau in oxygen uptake?. Chest 1989; 96: 1312-1316
- 48 Myers J, Walsh D, Sullivan M. et al. Effect of sampling on variability and plateau in oxygen uptake. J Appl Physiol 1990; 68: 404-410
- 49 Yoon B-K, Kravitz L, Robergs R. ˙VO2max, protocol duration, and the ˙VO2 plateau. Med Sci Sports Exerc 2007; 39: 11861192
- 50 Jones RH, Molitoris BA. A statistical method for determining the breakpoint of two lines. Anal Biochem 1984; 141: 287-290
- 51 Beaver WL, Wasserman K, Whipp BJ. A new method for detecting anaerobic threshold by gas exchange. J Appl Physiol 1986; 60: 2020-2027
- 52 Orr GW, Green HJ, Hughson RL. et al. A computer linear regression model to determine ventilatory anaerobic threshold. J Appl Physiol 1982; 52: 1349-1352
- 53 Pethick J, Winter SL, Burnley M. Physiological evidence that the critical torque is a phase transition, not a threshold. Med Sci Sports Exerc 2020; 52: 2390-2401
- 54 Ozkaya O, Balci GA, As H. et al. Grey zone: a Gap Between Heavy and Severe Exercise Domain. J Strength Cond Res 2022; 36: 113-120
- 55 Zhang Z, Martin CF. Convergence and Gibbs’ phenomenon in cubic spline interpolation of discontinuous functions. J Comput Appl Math 1997; 87: 359-371
- 56 Pollock ML, Foster C, Schmidt D. et al. Comparative analysis of physiologic responses to three different maximal graded exercise test protocols in healthy women. Am Heart J 1982; 103: 363373
- 57 Pollock ML, Bohannon RL, Cooper KH. et al. A comparative analysis of four protocols for maximal treadmill stress testing. Am Heart J 1976; 92: 39-46
- 58 Standvoss K, Kazezian V, Lewke BR. et al. Shortcut citations in the methods section: frequency, problems, and strategies for responsible reuse. PLoS Biol 2024; 22: e3002562
- 59 Mattioni Maturana F. 2024 https://fmmattioni.github.io/whippr/
- 60 Hesse A. 2023 https://github.com/ahesse2567/gasexchanger
- 61 Nolte S. spiro: An R package for analyzing data from cardiopulmonary exercise testing. J Open Source Softw 2023; 8: 5089