[Purpose] This study aimed 1) to assess whether a prediction model for whole body
skeletal muscle mass that is based on a sedentary population is applicable to young male
athletes, and 2) to develop a new skeletal muscle mass prediction model for young male
athletes. [Subjects and Methods] The skeletal muscle mass of 61 male athletes was measured
using magnetic resonance imaging (MRI) and estimated using a previous prediction model
(Sanada et al., 2006) with B-mode ultrasonography. The prediction model was not suitable
for young male athletes, as a significant difference was observed between the means of the
estimated and MRI-measured skeletal muscle mass. Next, the same subjects were randomly
assigned to a development or validation group, and a new model specifically relevant to
young male athletes was developed based on MRI and ultrasound data obtained from the
development group. [Results] A strong correlation was observed between the skeletal muscle
mass estimated by the new model and the MRI-measured skeletal muscle mass (r=0.96) in the
validation group, without significant difference between their means. No bias was found in
the new model using Bland-Altman analysis (r=−0.25). [Conclusion] These results validate
the new model and suggest that ultrasonography is a reliable method for measuring skeletal
muscle mass in young male athletes.
We propose higher-order detrending moving-average cross-correlation analysis (DMCA) to assess the long-range cross-correlations in cardiorespiratory and cardiovascular interactions. Although the original (zeroth-order) DMCA employs a simple moving-average detrending filter to remove non-stationary trends embedded in the observed time series, our approach incorporates a Savitzky–Golay filter as a higher-order detrending method. Because the non-stationary trends can adversely affect the long-range correlation assessment, the higher-order detrending serves to improve accuracy. To achieve a more reliable characterization of the long-range cross-correlations, we demonstrate the importance of the following steps: correcting the time scale, confirming the consistency of different order DMCAs, and estimating the time lag between time series. We applied this methodological framework to cardiorespiratory and cardiovascular time series analysis. In the cardiorespiratory interaction, respiratory and heart rate variability (HRV) showed long-range auto-correlations; however, no factor was shared between them. In the cardiovascular interaction, beat-to-beat systolic blood pressure and HRV showed long-range auto-correlations and shared a common long-range, cross-correlated factor.
This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.