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The effect of test modality on dynamic exercise biomarkers in children, adolescents, and young adults.

Ronen Bar-YosephJanos PorszaszShlomit Radom-AizikAnnamarie StehliPearl LawDan M Cooper
Published in: Physiological reports (2020)
Cardiopulmonary exercise testing (CPET) modalities, treadmill (TM), and cycle ergometer (CE), influence maximal gas exchange and heart rate (HR) responses. Little is known regarding CPET modality effect on submaximal biomarkers during childhood and adolescence. Ninety-four healthy participants (7-34 y.o., 53% female) performed TM and CE CPET to address two major gaps: (1) the effect of modality on submaximal CPET biomarkers, and (2) estimation of work rate in TM CPET. Breath-by-breath gas exchange enabled calculation of linear regression slopes such as V ˙ O2 /ΔHR and Δ V ˙ E/Δ V ˙ CO2 . Lean body mass (LBM) was measured with dual X-ray absorptiometry. We tested a novel TM CPET estimate of work rate based on TM velocity2 , incline, and body mass (VIM). Like the linear relationship between V ˙ O2 and work rate in CE CPET, V ˙ O2 increased linearly with TM VIM. TM Δ V ˙ O2 /ΔHR was highly correlated with CE (r = 0.92), and each increased substantially with LBM (P < 0.0001 for TM and CE). Δ V ˙ O2 /ΔHR was to a small (~8.7%) but significant extent larger in TM (1.6 mL/min/beat, P = 0.04). In contrast, TM and CE Δ V ˙ E/Δ V ˙ CO2 decreased significantly with LBM, supporting earlier observations from CE CPET. For both CE and TM, males had significantly higher Δ V ˙ O2 /ΔHR but lower Δ V ˙ E/Δ V ˙ CO2 than females. Novel TM CPET biomarkers such as ΔVIM/ΔHR and ∆ V ˙ O2 /ΔVIM paralleled effects of LBM observed in CE CPET. TM and CE CPET submaximal biomarkers are not interchangeable, but similarly reflect maturation during critical periods. CPET analysis that utilizes data actually measured (rather than estimated) may improve the clinical value of TM and CE CPET.
Keyphrases
  • heart rate
  • energy transfer
  • blood pressure
  • physical activity
  • high intensity
  • heart rate variability
  • resistance training
  • body composition
  • magnetic resonance imaging
  • deep learning
  • neural network
  • big data