Literature DB >> 17626831

Modeling upper and lower limb muscle volume by bioelectrical impedance analysis.

Alexander Stahn1, Elmarie Terblanche, Günther Strobel.   

Abstract

Most studies employing bioelectrical impedance analysis (BIA) for estimating appendicular skeletal muscle mass using descriptive BIA models rely on statistical rather than biophysical principles. The aim of the present study was to evaluate the feasibility of estimating arm and leg muscle volume (MV) based on multiple bioimpedance measurements and using a recently proposed mathematical model and to compare this technique to conventional segmental BIA at high and low frequencies. MV of the arm and leg, respectively, was determined in 15 young, healthy, active men [age 22 +/- 2 (SD) yr, total body fat 15.6 +/- 5.1%] by magnetic resonance imaging (MRI) and BIA using a conventional and new bioimpedance model. MRI-determined MV for leg and arm was 6,268 +/- 1,099 and 1,173 +/- 172 cm(3), respectively. Estimated MV by the new BIA model [leg: 6,294 +/- 1,155 cm(3) (50 kHz), 6,278 +/- 1,103 cm(3) (500 kHz); arm: 1,216 +/- 172 cm(3) (50 kHz), 1,155 +/- 157 cm(3) (500 kHz)] was not statistically different from MRI-determined MV (leg: P= 0.958; arm: P= 0.188). The new BIA model was superior to conventional BIA and performed best at 500 kHz for estimating leg MV as indicated by the lower relative total error [new: 3.6% (500 kHz), 5.2% (50 kHz); conventional: 7.6% (500 kHz) and 8.3% (50 kHz)]. In contrast, the new BIA model, both at 50 and 500 kHz, did not improve the accuracy for estimating arm MV [new: 10.8% (500 kHz), 10.6% (50 kHz); conventional: 11.8% (500 kHz), 11.4% (50 kHz)]. It was concluded that modeling of multiple BIA measurements has advantages for the determination of lower limb muscle volume in healthy, active adult men.

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Year:  2007        PMID: 17626831     DOI: 10.1152/japplphysiol.01163.2006

Source DB:  PubMed          Journal:  J Appl Physiol (1985)        ISSN: 0161-7567


  8 in total

1.  Muscle strength and its relationship with skeletal muscle mass indices as determined by segmental bio-impedance analysis.

Authors:  Omid Alizadehkhaiyat; David H Hawkes; Graham J Kemp; Anthony Howard; Simon P Frostick
Journal:  Eur J Appl Physiol       Date:  2013-11-01       Impact factor: 3.078

2.  Assessment of body composition in dialysis patients by arm bioimpedance compared to MRI and 40K measurements.

Authors:  M Carter; F Zhu; P Kotanko; M Kuhlmann; L Ramirez; S B Heymsfield; G Handelman; N W Levin
Journal:  Blood Purif       Date:  2009-03-09       Impact factor: 2.614

3.  Proximal electrode placement improves the estimation of body composition in obese and lean elderly during segmental bioelectrical impedance analysis.

Authors:  Yosuke Yamada; Yoshihisa Masuo; Keiichi Yokoyama; Yukako Hashii; Soichi Ando; Yasuko Okayama; Taketoshi Morimoto; Misaka Kimura; Shingo Oda
Journal:  Eur J Appl Physiol       Date:  2009-06-17       Impact factor: 3.078

4.  Guidelines to electrode positioning for human and animal electrical impedance myography research.

Authors:  Benjamin Sanchez; Adam Pacheck; Seward B Rutkove
Journal:  Sci Rep       Date:  2016-09-02       Impact factor: 4.379

5.  Lean regional muscle volume estimates using explanatory bioelectrical models in healthy subjects and patients with muscle wasting.

Authors:  Damien Bachasson; Alper Carras Ayaz; Jessie Mosso; Aurélie Canal; Jean-Marc Boisserie; Ericky C A Araujo; Olivier Benveniste; Harmen Reyngoudt; Benjamin Marty; Pierre G Carlier; Jean-Yves Hogrel
Journal:  J Cachexia Sarcopenia Muscle       Date:  2020-12-29       Impact factor: 12.910

6.  Validity of ultrasound muscle thickness measurements for predicting leg skeletal muscle mass in healthy Japanese middle-aged and older individuals.

Authors:  Yohei Takai; Megumi Ohta; Ryota Akagi; Emika Kato; Taku Wakahara; Yasuo Kawakami; Tetsuo Fukunaga; Hiroaki Kanehisa
Journal:  J Physiol Anthropol       Date:  2013-09-25       Impact factor: 2.867

7.  Wearable Multi-Frequency and Multi-Segment Bioelectrical Impedance Spectroscopy for Unobtrusively Tracking Body Fluid Shifts during Physical Activity in Real-Field Applications: A Preliminary Study.

Authors:  Federica Villa; Alessandro Magnani; Martina A Maggioni; Alexander Stahn; Susanna Rampichini; Giampiero Merati; Paolo Castiglioni
Journal:  Sensors (Basel)       Date:  2016-05-11       Impact factor: 3.576

8.  Estimating fat mass in heart failure patients.

Authors:  Tobias Daniel Trippel; Julian Lenk; Hanns-Christian Gunga; Wolfram Doehner; Stephan von Haehling; Goran Loncar; Frank Edelmann; Burkert Pieske; Alexander Stahn; Hans-Dirk Duengen
Journal:  Arch Med Sci Atheroscler Dis       Date:  2016-08-16
  8 in total

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