Influence of phase I duration on phase II VO2 kinetics parameter estimates in older and young adults.

School of Kinesiology, University of Western Ontario, London, Ontario, Canada N6A 3K7.
AJP Regulatory Integrative and Comparative Physiology (Impact Factor: 3.53). 04/2011; 301(1):R218-24. DOI: 10.1152/ajpregu.00060.2011
Source: PubMed

ABSTRACT Older adults (O) may have a longer phase I pulmonary O(2) uptake kinetics (Vo(2)(p)) than young adults (Y); this may affect parameter estimates of phase II Vo(2)(p). Therefore, we sought to: 1) experimentally estimate the duration of phase I Vo(2)(p) (EE phase I) in O and Y subjects during moderate-intensity exercise transitions; 2) examine the effects of selected phase I durations (i.e., different start times for modeling phase II) on parameter estimates of the phase II Vo(2)(p) response; and 3) thereby determine whether slower phase II kinetics in O subjects represent a physiological difference or a by-product of fitting strategy. Vo(2)(p) was measured breath-by-breath in 19 O (68 ± 6 yr; mean ± SD) and 19 Y (24 ± 5 yr) using a volume turbine and mass spectrometer. Phase I Vo(2)(p) was longer in O (31 ± 4 s) than Y (20 ± 7 s) (P < 0.05). In O, phase II τVo(2)(p) was larger (P < 0.05) when fitting started at 15 s (49 ± 12 s) compared with fits starting at the individual EE phase I (43 ± 12 s), 25 s (42 ± 10 s), 35 s (42 ± 12 s), and 45 s (45 ± 15 s). In Y, τVo(2)(p) was not affected by the time at which phase II Vo(2)(p) fitting started (τVo(2)(p) = 31 ± 7 s, 29 ± 9 s, 30 ± 10 s, 32 ± 11 s, and 30 ± 8 s for fittings starting at 15 s, 25 s, 35 s, 45 s, and EE phase I, respectively). Fitting from EE phase I, 25 s, or 35 s resulted in the smallest CI τVo(2)(p) in both O and Y. Thus, fitting phase II Vo(2)(p) from (but not constrained to) 25 s or 35 s provides consistent estimates of Vo(2)(p) kinetics parameters in Y and O, despite the longer phase I Vo(2)(p) in O.

  • [Show abstract] [Hide abstract]
    ABSTRACT: Key PointsThe relationship between age and physiological function remains poorly defined and there are no physiological markers that can be used to reliably predict the age of an individual.This could be due to a variety of confounding genetic and lifestyle factors, and in particular to ill-defined and low levels of physical activity.This study assessed the relationship between age and a diverse range of physiological functions in a cohort of highly active older individuals (cyclists) aged 55–79 years in whom the effects of lifestyle factors would be ameliorated.Significant associations between age and function were observed for many functions. was most closely associated with age, but even here the variance in age for any given level was high, precluding the clear identification of the age of any individual.The data suggest that the relationship between human ageing and physiological function is highly individualistic and modified by inactivity.AbstractDespite extensive research, the relationship between age and physiological function remains poorly characterised and there are currently no reliable markers of human ageing. This is probably due to a number of confounding factors, particularly in studies of a cross-sectional nature. These include inter-subject genetic variation, as well as inter-generational differences in nutrition, healthcare and insufficient levels of physical activity as well as other environmental factors. We have studied a cohort of highly and homogeneously active older male (n = 84) and female (n = 41) cyclists aged 55–79 years who it is proposed represent a model for the study of human ageing free from the majority of confounding factors, especially inactivity. The aim of the study was to identify physiological markers of ageing by assessing the relationship between function and age across a wide range of indices. Each participant underwent a detailed physiological profiling which included measures of cardiovascular, respiratory, neuromuscular, metabolic, endocrine and cognitive functions, bone strength, and health and well-being. Significant associations between age and function were observed for many functions. The maximal rate of oxygen consumption ( showed the closest association with age (r = −0.443 to −0.664; P < 0.001), but even here the variance in age for any given level was high, precluding the clear identification of the age of any individual. The results of this cross-sectional study suggest that even when many confounding variables are removed the relationship between function and healthy ageing is complex and likely to be highly individualistic and that physical activity levels must be taken into account in ageing studies.
    The Journal of Physiology 01/2015; 593(3). DOI:10.1113/jphysiol.2014.282863 · 4.54 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Cardiovascular risk, predicted by peak O2 uptake (V˙O2peak), is increased in type 1 diabetes. We examined the contribution of central and peripheral mechanisms to V˙O2peak in physically active adults with type 1 diabetes.
    Medicine &amp Science in Sports &amp Exercise 06/2014; DOI:10.1249/MSS.0000000000000419 · 4.46 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: To improve the signal-to-noise ratio of breath-by-breath pulmonary O2 uptake (VO2p) data it is common practice to perform multiple step-transitions which are subsequently processed to yield an ensemble-averaged profile. The effect of different data processing techniques on phase II VO2p kinetic parameter estimates (VO2p amplitude, time delay [TD], and phase II time constant [τVO2p]) and model confidence (95% confidence interval [CI95]) was examined. Young (n = 9) and older (n = 9) men performed four step-transitions from a 20W baseline to work rate corresponding to 90% of their estimated lactate threshold on a cycle ergometer. Breath-by-breath VO2p was measured using mass spectrometry and volume turbine. Mono-exponential kinetic modelling of phase II VO2p data was performed on data processed using the following techniques: A) raw data (trials time-aligned, breaths of all trials combined and sorted in time); B) raw data+interpolation (trials time-aligned, combined, sorted and linearly-interpolated to s-by-s); C) raw data+interpolation+5-s bin averaged; D) individual trial interpolation+ensemble-averaged (trials time-aligned, linearly-interpolated to s-by-s (technique 1: points joined by straight line segments), ensemble-averaged); E) ‘D’+5-s bin-averaged; F) individual trial interpolation+ensemble-averaged (trials time-aligned, linearly-interpolated to s-by-s (technique 2: points copied until subsequent point appears), ensemble-averaged); G) ‘F’+5-s bin-averaged. All of the model parameters were unaffected by data processing technique, however the CI95 for τVO2p condition ‘D’ (4 s) was lower (p<0.05) than CI95 reported for all other conditions (5–10 s). Data processing technique had no effect on parameter estimates of the phase II VO2p response. However, the narrowest interval for CI95 occurred when individual trials were linearly-interpolated and ensemble-averaged.This article is protected by copyright. All rights reserved
    Experimental physiology 07/2014; 99(11). DOI:10.1113/expphysiol.2014.080812 · 2.87 Impact Factor