Research

Why use a Charder device for conducting research?

Charder's Body Composition Analyzers are accurate and consistent, making them a great choice for research institutions that need an easy-to-use measurement solution that provides validated results.

We have a special program available to academic institutions that want to use our devices for research purposes - contact us to learn more!

  • 1. Easy-to-use

    The 8-point touch electrode system combines ease of use with consistency, streamlining the measurement process while improving efficiency of data collection! Straightforward voice guidance and clear on-screen instructions make it easier for assistants to conduct each measurement, allowing them to devote more time on analysis!

    Easy-to-use
  • 2. Data management

    When used with Charder Insight PC software, measurement results can be automatically transferred, stored, and viewed clearly, with options to export to CSV for statistical analysis of large amounts of data!

    Data management
  • 3. Reproducible results

    Receive clinically validated and reproducible results, ensuring the integrity of measurement data and the ability to follow-up with regular monitoring of subjects.

    Reproducible

Research papers

We've conducted decades of dedicated research in the field of BIA, including utilization of Artificial Neural Networks for reproducible, accurate results, with findings published in various international journals. Utilizing algorithms formed and validated using "gold standards" such as CT and DXA, our results provide you with medical-grade accuracy. A sampling of published research conducted by the Charder Research Center can be found below, and we are happy to discuss research possibilities or questions you may have!
  • Comparison of FFM with DXA

    Comparison of FFM with DXA

    A study published by the International Journal of Gerontology compared the accuracy of BIA against DXA. The study found that results analyzed by Charder's body composition model had a r=.942 correlation with DXA for fat-free mass.

    "In summary, the multiple segments FFM estimated by BIA were highly relative to that of determined by DXA for elderly in Taiwan."

    New Application of Bioelectrical Impedance Analysis by the Back Propagation Artificial Neural Network Mathematically Predictive Model of Tissue Composition in the Lower Limbs of Elderly People. International Journal of Gerontology 6 (2012) 20-26

  • Artificial Neural Network FFM results compared with DXA

    Artificial Neural Network FFM results compared with DXA

    A study published by Nutrition Journal compared the accuracy of a new BIA predictive model utilizing Back Propagation Artificial Neural Networks (BP-ANN) with DXA for Fat-Free Mass. The study found that the BP-ANN model obtained a correlation coefficient of r2=0.987.

    "The results showed a better agreement existed between FFMANN and FFMDXA than that between FFMLR and FFMDXA."

    The novel application of artificial neural network on bioelectrical impedance analysis to assess the body composition in elderly. Nutrition Journal 2013, 21:2 1

  • Artificial Neural Network results for Fat Mass and Fat-Free Mass compared with DXA

    Artificial Neural Network results for Fat Mass and Fat-Free Mass compared with DXA

    A study published by the International Journal of Gerontology compared the accuracy of a new BIA predictive model utilizing Back Propagation Artificial Neural Networks (BP-ANN) with DXA results for lower limbs. The study found that the Charder's new predictive model obtained a correlation coefficient of r2=0.962.

    "In summary, the greater predictive accuracy and precision made the application of BIA with the BP-ANN mathematical model more feasible for the clinical measurement of FM and FFM in the lower limbs of elderly people."

    New Application of Bioelectrical Impedance Analysis by the Back Propagation Artificial Neural Network Mathematically Predictive Model of Tissue Composition in the Lower Limbs of Elderly People. International Journal of Gerontology 6 (2012) 20-26

  • Whole-body and segmental FFM in elite athletic populations compared with DXA

    Whole-body and segmental FFM in elite athletic populations compared with DXA

    A study published by Scientific Research and Essays (SCI) compared the accuracy of BIA with DXA in elite football players. The study found that Charder's body composition model obtained a correlation coefficient of r=0.95 for whole body fat-free mass when compared to DXA.

    "In summary, the greater performance in prediction of segments body composition in our developed predictive equation by BIA measurement has shown the possibility of application for monitoring athlete body composition especially the limb and trunk."

    The bioelectrical impedance analysis with newly predictive equations for measuring segments body composition of elite male football players in Taiwan. Scientific Research and Essays Vol. 6 (24), pp. 5131-5136, 23 October 2011. DOI:10.5897/SRE11.041

  • Prediction of skeletal muscle function using BIVA

    Prediction of skeletal muscle function using BIVA

    A study published in PLOS ONE evaluated if a clear relationship would be found between BIVA results and muscle function. The study found that handgrip strength could be predicted utilizing BIVA parameters, distinguishing between different levels of strength.

    "Our study showed that BIVA parameters measured by a standing impedance analyzer and anthropometric variables can predict muscle function as measured by HGS with good performance in healthy Asian adults. Our results may facilitate clinical applications of standing BIVA technology in assessing skeletal muscle function."

    "In summary, the greater performance in prediction of segments body composition in our developed predictive equation by BIA measurement has shown the possibility of application for monitoring athlete body composition especially the limb and trunk."

    Lee L-W, Lu H-K, Chen Y-Y, Lai C-L, Chu L-P, Hsieh M-C, et al. (2020) Prediction and discrimination of skeletal muscle function by bioelectrical impedance vector analysis using a standing impedance analyzer in healthy Taiwanese adults. PLoS ONE 15(6): e0231604.

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