Anthropometric Parameters

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Eleuterio Ferrannini - One of the best experts on this subject based on the ideXlab platform.

Andrew P. Hills - One of the best experts on this subject based on the ideXlab platform.

  • The range of non-traditional Anthropometric Parameters to define obesity and obesity-related disease in children: a systematic review
    European Journal of Clinical Nutrition, 2020
    Co-Authors: Priyanga Ranasinghe, Ranil Jayawardena, Nishadi Gamage, V. Pujitha Wickramasinghe, Andrew P. Hills
    Abstract:

    Obesity is defined as an abnormal/excessive accumulation of body fat, associated with health consequences. Although overall obesity does confer a significant threat to the health of individuals, the distribution of body fat, especially abdominal/central obesity is of greater importance. For practical reasons, proxy Anthropometric measurements have been developed to identify central obesity, however, major limitations are noted in these traditional measurements. The present study aims to evaluate the literature, to identify and describe non-traditional Anthropometric measurements of overweight and obesity in children. The current systematic review was conducted in accordance with the PRISMA guidelines, and the search was undertaken in the PubMed^® database, using MeSH (Medical Subject Headings) terms. Data extracted from each study were: (a) details of the study, (b) Anthropometric parameter(s) evaluated in the study and its details, (c) study methods, (d) objectives of the study and/or comparisons, and (e) main findings/conclusions of the study. The search yielded a total of 3697 articles, of which 31 studies were deemed eligible to be included. The literature search identified 13 non-traditional Anthropometric Parameters. Data on non-traditional Anthropometric Parameters were derived from 24 countries. Majority were descriptive cross-sectional studies ( n  = 29), while sample size varied from 65 to 23,043. Non-traditional Anthropometric Parameters showed variable correlation with obesity and/or related metabolic risk factors. Some Parameters involved complex calculations, while others were based on a single Anthropometric measurement or derived from traditional measures. Most studies lacked comparison with a ‘gold standard’ assessment of body fat, hence further research is required to determine their accuracy and precision.

  • Novel Anthropometric Parameters to define obesity and obesity-related disease in adults: a systematic review
    Nutrition Reviews, 2019
    Co-Authors: Ranil Jayawardena, Priyanga Ranasinghe, Thilina Ranathunga, Y. Mathangasinghe, Sudharshani Wasalathanththri, Andrew P. Hills
    Abstract:

    Context: Obesity is defined as an abnormal or excessive accumulation of body fat. Traditionally, it has been assessed using a wide range of Anthropometric, biochemical, and radiological measurements, with each having its advantages and disadvantages. Objective: A systematic review of the literature was conducted to identify novel Anthropometric measurements of obesity in adults. Data Sources: Using a combination of MeSH terms, the PubMed database was searched. Data Extraction: The current systematic review was conducted in accordance with the PRISMA guidelines. The data extracted from each study were (1) details of the study, (2) Anthropometric parameter(s) evaluated, (3) study methods, (4) objectives of the study and/or comparisons, and (5) main findings/conclusions of the study. Data Analysis: The search yielded 2472 articles, of which 66 studies were deemed eligible to be included. The literature search identified 25 novel Anthropometric Parameters. Data on novel Anthropometric Parameters were derived from 26 countries. Majority were descriptive cross-sectional studies (n = 43), while 22 were cohort studies. Age range of the study populations was 17-103 years, while sample size varied from 45 to 384 612. Conclusions: The novel Anthropometric Parameters identified in the present study showed variable correlation with obesity and/or related metabolic risk factors. Some Parameters involved complex calculations, while others were derived from traditional Anthropometric measurements. Further research is required in order to determine the accuracy and precision.

Anna Solini - One of the best experts on this subject based on the ideXlab platform.

Zahra Moussavi - One of the best experts on this subject based on the ideXlab platform.

  • Snoring sounds’ statistical characteristics depend on Anthropometric Parameters
    Journal of Biomedical Science and Engineering, 2020
    Co-Authors: Ali Azarbarzin, Zahra Moussavi
    Abstract:

    Snoring is common in people with obstructive sleep apnea (OSA). Although not every snorer has OSA or vice-versa, many studies attempt to use snoring sounds for classification of people into two groups of OSA and simple snorers. This paper discusses the relationship between snorers’ Anthropometric Parameters and statistical characteristics of snoring sound (SS) and also reports on classification accuracies of methods using SS features for screening OSA from simple snorers when Anthropometric Parameters are either matched or unmatched. Tracheal respiratory sounds were collected from 60 snorers simultaneously with full-night Polysomnography (PSG). Energy, formant frequency, Skewness and Kurtosis were calculated from the SS segments. We also defined and calculated two features: Median Bifrequency (MBF), and projected MBF (PMBF). The statistical relationship between the extracted features and Anthropometric Parameters such as height, Body Mass Index (BMI), age, gender, and <i>Apnea</i>-Hypopnea Index (AHI) were investigated. The results showed that the SS features were not only sensitive to AHI but also to height, BMI and gender. Next, we performed two experiments to classify patients with Obstructive Sleep Apnea (OSA) and simple snorers: Experiment A: a small group of participants (22 OSA and 6 simple snorers) with matched height, BMI, and gender were selected and classified using Naïve Bayes classifier, and Experiment B: the same number of participants with unmatched height, BMI, and gender were chosen for classification. A sensitivity of 93.2% (87.5%) and specificity of 88.4% (86.3%) was achieved for the matched (unmatched) groups

  • EMBC - Do Anthropometric Parameters change the characteristics of snoring sound
    Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2011
    Co-Authors: Ali Azarbarzin, Zahra Moussavi
    Abstract:

    Snoring sounds is commonly known to be associated with obstructive sleep apnea (OSA). There are many studies trying to distinguish between the snoring sounds of non-OSA and those of OSA patients. However, OSA is only one of the conditions that affect the structure of upper airway. In this study, we investigated the effect of Anthropometric Parameters on the snoring sounds. Since snoring sounds are non-Gaussian signals by nature, we derived its Higher Order Statistical (HOS) features and investigated the statistical significance of the Anthropometric Parameters on each of these features. Data were collected from 40 patients with different levels of OSA. Tracheal respiratory sounds collected by a microphone placed over suprasternal notch, were recorded simultaneously with full-night Polysomnography (PSG) data during sleep. The snoring segments were identified semi-automatically from respiratory sounds using an unsupervised snore detection algorithm. The bispectrum of each SS segment was estimated. We calculated two common HOS measures, Skewness and Kurtosis, plus a new feature called Projected Median Bifrequency (PMBF) from the SS segments. Then, we investigated the statistical relationship between these features and Anthropometric Parameters such as height, Body Mass Index (BMI), age, gender, and Apnea-Hypopnea Index (AHI). The result showed that gender, BMI, height, and AHI are the Parameters that do change the characteristics of snoring sounds significantly.

  • Do Anthropometric Parameters change the characteristics of snoring sound?
    2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011
    Co-Authors: Ali Azarbarzin, Zahra Moussavi
    Abstract:

    Snoring sounds is commonly known to be associated with obstructive sleep apnea (OSA). There are many studies trying to distinguish between the snoring sounds of non-OSA and those of OSA patients. However, OSA is only one of the conditions that affect the structure of upper airway. In this study, we investigated the effect of Anthropometric Parameters on the snoring sounds. Since snoring sounds are non-Gaussian signals by nature, we derived its Higher Order Statistical (HOS) features and investigated the statistical significance of the Anthropometric Parameters on each of these features. Data were collected from 40 patients with different levels of OSA. Tracheal respiratory sounds collected by a microphone placed over suprasternal notch, were recorded simultaneously with full-night Polysomnography (PSG) data during sleep. The snoring segments were identified semi-automatically from respiratory sounds using an unsupervised snore detection algorithm. The bispectrum of each SS segment was estimated. We calculated two common HOS measures, Skewness and Kurtosis, plus a new feature called Projected Median Bifrequency (PMBF) from the SS segments. Then, we investigated the statistical relationship between these features and Anthropometric Parameters such as height, Body Mass Index (BMI), age, gender, and Apnea-Hypopnea Index (AHI). The result showed that gender, BMI, height, and AHI are the Parameters that do change the characteristics of snoring sounds significantly.

Isa Galvão Rodrigues - One of the best experts on this subject based on the ideXlab platform.

  • Predictive models for estimating visceral fat: The contribution from Anthropometric Parameters
    PLOS ONE, 2017
    Co-Authors: Cláudia Porto Sabino Pinho, Alcides Da Silva Diniz, Ilma Kruze Grande De Arruda, Ana Paula D.l. Leite, Marina De Moraes Vasconcelos Petribú, Isa Galvão Rodrigues
    Abstract:

    Background Excessive adipose visceral tissue (AVT) represents an independent risk factor for cardiometabolic alterations. The search continues for a highly valid marker for estimating visceral adiposity that is a simple and low cost tool able to screen individuals who are highly at risk of being viscerally obese. The aim of this study was to develop a predictive model for estimating AVT volume using Anthropometric Parameters. Objective Excessive adipose visceral tissue (AVT) represents an independent risk factor for cardiometabolic alterations. The search continues for a highly valid marker for estimating visceral adiposity that is a simple and low cost tool able to screen individuals who are highly at risk of being viscerally obese. The aim of this study was to develop a predictive model for estimating AVT volume using Anthropometric Parameters. Methods A cross-sectional study involving overweight individuals whose AVT was evaluated (using computed tomography–CT), along with the following Anthropometric Parameters: body mass index (BMI), abdominal circumference (AC), waist-to-hip ratio (WHpR), waist-to-height ratio (WHtR), sagittal diameter (SD), conicity index (CI), neck circumference (NC), neck-to-thigh ratio (NTR), waist-to-thigh ratio (WTR), and body adiposity index (BAI). Results 109 individuals with an average age of 50.3±12.2 were evaluated. The predictive equation developed to estimate AVT in men was AVT = -1647.75 +2.43(AC) +594.74(WHpR) +883.40(CI) (R2 adjusted: 64.1%). For women, the model chosen was: AVT = -634.73 +1.49(Age) +8.34(SD) + 291.51(CI) + 6.92(NC) (R2 adjusted: 40.4%). The predictive ability of the equations developed in relation to AVT volume determined by CT was 66.9% and 46.2% for males and females, respectively (p

  • predictive models for estimating visceral fat the contribution from Anthropometric Parameters
    PLOS ONE, 2017
    Co-Authors: Cláudia Porto Sabino Pinho, Alcides Da Silva Diniz, Ilma Kruze Grande De Arruda, Ana Paula D.l. Leite, Marina De Moraes Vasconcelos Petribú, Isa Galvão Rodrigues
    Abstract:

    Background Excessive adipose visceral tissue (AVT) represents an independent risk factor for cardiometabolic alterations. The search continues for a highly valid marker for estimating visceral adiposity that is a simple and low cost tool able to screen individuals who are highly at risk of being viscerally obese. The aim of this study was to develop a predictive model for estimating AVT volume using Anthropometric Parameters. Objective Excessive adipose visceral tissue (AVT) represents an independent risk factor for cardiometabolic alterations. The search continues for a highly valid marker for estimating visceral adiposity that is a simple and low cost tool able to screen individuals who are highly at risk of being viscerally obese. The aim of this study was to develop a predictive model for estimating AVT volume using Anthropometric Parameters. Methods A cross-sectional study involving overweight individuals whose AVT was evaluated (using computed tomography–CT), along with the following Anthropometric Parameters: body mass index (BMI), abdominal circumference (AC), waist-to-hip ratio (WHpR), waist-to-height ratio (WHtR), sagittal diameter (SD), conicity index (CI), neck circumference (NC), neck-to-thigh ratio (NTR), waist-to-thigh ratio (WTR), and body adiposity index (BAI). Results 109 individuals with an average age of 50.3±12.2 were evaluated. The predictive equation developed to estimate AVT in men was AVT = -1647.75 +2.43(AC) +594.74(WHpR) +883.40(CI) (R2 adjusted: 64.1%). For women, the model chosen was: AVT = -634.73 +1.49(Age) +8.34(SD) + 291.51(CI) + 6.92(NC) (R2 adjusted: 40.4%). The predictive ability of the equations developed in relation to AVT volume determined by CT was 66.9% and 46.2% for males and females, respectively (p<0.001). Conclusions A quick and precise AVT estimate, especially for men, can be obtained using only AC, WHpR, and CI for men, and age, SD, CI, and NC for women. These equations can be used as a clinical and epidemiological tool for overweight individuals.