A Novel Waveform to Extract Exercise Gas Exchange Response Dynamics: The Chirp Waveform

Authors

Michele Girardi, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical CenterFollow
Michael A. Roman, University of CalgaryFollow
Janos Porszasz, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical CenterFollow
William W. Stringer, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical CenterFollow
Carrie Ferguson, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical CenterFollow
Harry B. Rossiter, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical CenterFollow
Richard Casaburi, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical CenterFollow

Abstract

Characterizing exercise gas exchange response dynamics reveals important information about physiological control processes and cardiopulmonary dysfunction. However, current methods for extracting exercise response dynamics typically use multiple step-wise transitions, limiting applicability of this technique. PURPOSE: We designed a new protocol (chirp waveform) to extract exercise gas exchange response dynamics in a single visit. We tested the hypothesis that gas exchange response dynamics extracted from chirp forcing would be similar to those extracted from step-wise transitions. METHODS: Thirty-one participants (14 young healthy, 7 older healthy, and 10 patients with chronic obstructive pulmonary disease) visited the laboratory on three occasions. On visit 1, participants performed a ramp incremental test to determine the gas exchange threshold (GET). On visits 2-3, participants performed either a chirp or step-wise protocol in a randomized order. Chirp forcing consisted of sinusoidal fluctuations in work rate with constant amplitude and progressive shortening of sine periods. Square protocol consisted of 3 square-wave transitions each of 6 min duration. Work rate amplitude (from 20 W to ~95% of the individual’s GET) and exercise duration (30 min) were the same in both protocols. The input-output relationship was characterized using a first-order linear transfer function containing a system gain (K) and time constant (τ) [G(s)= K/(τ×s+1)]. Parameter identification was performed in Matlab using the Matlab System Identification toolbox. Agreement between measures was established using Bland-Altman analysis and Rothery’s Concordance Coefficient (RCC). RESULTS: No systematic bias (mean difference of chirp minus square-wave; Δmean) and good reliability was found for V̇O₂ K [Δmean: 0.25(1.03) mL/min/W, p=0.179; RCC: 0.773, p=0.004], V̇O₂ τ [Δmean: 0.30(7.08) s, p=0.815; RCC: 0.837, p2 K [Δmean: -0.19(1.57) mL/min/W, p=0.512; RCC: 0.827, pp=0.009] and good reliability (RCC: 0.794, p2 τ. CONCLUSION: The chirp waveform allows extraction of gas exchange response dynamics similar to those obtained from standard methods, thus overcoming the need for multiple tests.

Recommended Citation

Girardi, Michele; Roman, Michael A.; Porszasz, Janos; Stringer, William W.; Ferguson, Carrie; Rossiter, Harry B.; and Casaburi, Richard (2023) "A Novel Waveform to Extract Exercise Gas Exchange Response Dynamics: The Chirp Waveform," International Journal of Exercise Science: Conference Proceedings: Vol. 14: Iss. 3, Article 110.
Available at: https://digitalcommons.wku.edu/ijesab/vol14/iss3/110