Lowest Standard Error Of Estimate Research Articles

Classification of Male Athletes Based on Critical Power.

This study aimed to classify male athletes based on their performance levels derived from running critical power (CP) using the 9/3-minute Stryd CP test, enabling customized training strategies and goal setting. Twenty-four trained athletes underwent the 9/3-minute running CP test on a certified 400-m athletics track. Hierarchical cluster analysis using Ward's method categorized athletes based on CP into distinct performance tiers. Three clusters were identified with centroids of 3.87±0.12, 4.45±0.17, and 5.14±0.29 W/kg. Five performance tiers were defined through ordinary least square linear regression based on power (W/kg): Tier 1: Fair (2.9 to 3.6 W/kg), Tier 2: Tourist (3.6 to 4.2 W/kg), Tier 3: Regional (4.2 to 4.8 W/kg), Tier 4: National (4.8 to 5.5 W/kg), Tier 5: International (5.5 to 6.1 W/kg). Low semi-partial R-squared (SpR 2) values (0.02 to 0.05) indicated minimal homogeneity loss when merging clusters. R-squared (R 2) explained 89% to 96% of CP variance, emphasizing cluster analysis effectiveness. The linear regression model demonstrated a strong fit (r 2+=+0.997) with a significant intercept (3.22 W/kg), slope (0.63 W/kg/tier), and a low standard error of estimate (0.045 W/kg). This classification offers insights into male athlete performance levels based on CP, facilitating targeted training programs for varying performance levels.

Prediction of Half-Marathon Power Target using the 9/3-Minute Running Critical Power Test

Running power output allows for controlling variables that have been previously overlooked by relying solely on speed, such as surface, gradient and weight. The ability to measure this external load variable now enables the analysis of concepts that have predominantly been studied in cycling, such as the Critical Power (CP), in the context of running. This study aims to predict the CP target at which trained athletes run a half-marathon and determine whether races of this distance can serve as a valid alternative to update the CP record. A group of nine trained athletes performed the 9/3-minute Stryd CP test and participated in a half-marathon race in two separate testing sessions conducted in the field. The average power during a half-marathon race is a valid alternative method for determining the CP in trained athletes, as evidenced by the agreement (95% CI: -0.11 to 0.37 W/kg) and trivial systematic bias (0.13 W/kg) between methods. The linear regression model half-marathon power = 0.97 + 0.75·CP (W/kg) showed low standard error of estimate (0.29 W/kg) and significant large association between methods (r = 0.88; p = 0.002). Coaches and athletes should be aware that the CP target for a half-marathon race is 97.3% of the CP determined by the 9/3-minute Stryd CP test.

Prediction of Half-Marathon Power Target using the 9/3-Minute Running Critical Power Test.

Running power output allows for controlling variables that have been previously overlooked by relying solely on speed, such as surface, gradient and weight. The ability to measure this external load variable now enables the analysis of concepts that have predominantly been studied in cycling, such as the Critical Power (CP), in the context of running. This study aims to predict the CP target at which trained athletes run a half-marathon and determine whether races of this distance can serve as a valid alternative to update the CP record. A group of nine trained athletes performed the 9/3-minute Stryd CP test and participated in a half-marathon race in two separate testing sessions conducted in the field. The average power during a half-marathon race is a valid alternative method for determining the CP in trained athletes, as evidenced by the agreement (95% CI: -0.11 to 0.37 W/kg) and trivial systematic bias (0.13 W/kg) between methods. The linear regression model half-marathon power = 0.97 + 0.75·CP (W/kg) showed low standard error of estimate (0.29 W/kg) and significant large association between methods (r = 0.88; p = 0.002). Coaches and athletes should be aware that the CP target for a half-marathon race is 97.3% of the CP determined by the 9/3-minute Stryd CP test.

Non-linear Regression Models for Predicting Biogas Yields from Selected Bio-wastes

The benefits of biogas as alternative energy to other fossil fuel sources, due to its renewability, environmentally friendly nature, health benefits, etc., cannot be overemphasized. There are numerous models for predicting biogas production rate from bio-materials, including the modified Gompertz equation. These models are primarily dependent on specific biomass parameters. When any of these parameters, like the slurry volume, changes, another round of experiments must be conducted and curve fitted before biogas yield predictions can be made. This could be time-consuming and costly. Using experimentally published data, simple empirical models can be developed for predicting biogas yields over a range of input parameters. This will eliminate the need for always performing experiments before biogas yield predictions can be made. In light of this, scarce literature provides explicit models for predicting biogas yield over a range of parameters based on published data. This study developed non-linear regression models using published data on parameters that affect biogas yields, like the slurry volume, carbon-to-nitrogen ratio, temperature, total solids, volatile solids, hydraulic retention time, and pH. The data covered seven readily available bio-wastes, including cow dung, cow dung with plant waste, cow dung with poultry dung, poultry dung with grass, pig dung, and plant wastes. On validation of the models, the results showed that the models had a relatively low standard error of estimates, Akaike information criterion, Schwarz criterion, and Hannan-Quinn information criterion. Furthermore, the coefficients of determination, R2, were between 94.62 and 98.93%. The percentage average absolute deviation (% AAD) for each model was less than 7 %. The non-linear models were found to adequately predict the biogas yields within the limits of the available data set.

Stature and its Estimation Utilizing Hand and Foot Measurements in North Indian Population

Background: Stature estimation from body measurements is an important part of forensic and medico-legal cases especially in victim identifcation, where mutilated and amputated body parts are encountered at the crime scene. Hand and foot dimensions are the important features in context of stature estimation. The present study was conducted to find out the correlation of length and breadth of hand and foot in the stature of the North Indians. Methods: Anthropometric data from 413 males and 413 females were collected. Simple and multiple linear regression methods were used for estimation of stature from hand length, hand breadth, foot length and foot breadth measurements. Results: Statistically signifcant results (p <0.001) are obtained with each measurement when the derived model is tested for stature estimation. Hand length in both sexes showed highest correlation with the stature whereas foot breadth showed the lowest. Multiple regression models showed low standard error of estimate (SEE). Conclusion: The results of this study showed that, from a forensic stand point, measurements of the hands and feet may be employed to estimate the stature of North Indian populations.

Stroke volume and cardiac output during 6 minute-walk tests are strong predictors of maximal oxygen uptake in people after stroke.

The 6-minute walk test (6MWT) is a field test commonly used to predict peak oxygen consumption (VO2peak) in people after stroke. Inclusion of cardiodynamic variables measured by impedance cardiography (ICG) during a 6MWT has been shown to improve prediction of VO2peak in healthy adults but these data have not been considered in people after stroke. This study investigates whether the prediction of VO2peak can be improved by the inclusion of cardiovascular indices derived by impedance cardiography (ICG) during the 6MWT in people after stroke. This was a cross-sectional study. Patients diagnosed with stroke underwent in random order, a maximal cardiopulmonary exercise test (CPET) and 6MWT in separate dates. Heart rate (HR), stroke volume (SV) and cardiac output (CO) were measured by ICG during all tests. Oxygen consumption was recorded by a metabolic cart during the CPET. Recorded data were subjected to multiple regression analyses to generate VO2peak prediction equations. Fifty-nine patients, mean age 50.0±11.7 years were included in the analysis. The mean distance covered in the 6MWT (6MWD) was 294±13 m, VO2peak was 19.2±3.2 ml/min/kg. Mean peak HR, SV and CO recorded during 6MWT were 109±6 bpm, 86.3±8.8 ml, 9.4±1.2 L/min and during CPET were 135±14 bpm, 86.6±9 ml, 11.7±2 L/min respectively. The prediction equation with inclusion of cardiodynamic variables: 16.855 + (-0.060 x age) + (0.196 x BMI) + (0.01 x 6MWD) + (-0.416 x SV6MWT) + (3.587 x CO 6MWT) has a higher squared multiple correlation (R2) and a lower standard error of estimate (SEE) and SEE% compared to the equation using 6MWD as the only predictor. Inclusion of SV and CO measured during the 6MWT in stroke patients further improved the VO2peak prediction power compared to using 6MWD as a lone predictor.

Contribution of Body Mass Index Stratification for the Prediction of Maximal Oxygen Uptake.

The purpose of this study was to investigate whether modeling within separate body mass index (BMI) stratifications improves the accuracy of maximal oxygen uptake (VO2max) prediction compared to a model developed regardless of adults' BMIs. A total of 250 Taiwanese adults (total group, TOG) aged 22-64 years participated in this study, and were stratified into a normal group (NOG: 135), an overweight group (OVG: 69), and an obesity group (OBG: 46), according to the BMI classification recommended by the Taiwan Ministry of Health and Welfare. VO2max was directly measured on an electromagnetic bicycle ergometer. Using the participant's heart rate in the 3-min incremental step-in-place test and demographic parameters, VO2max prediction models established for four groups were TOG model, NOG model, OVG model, and OBG model, respectively. Compared with the TOG model, the OVG and OBG models had higher coefficients of determination and lower standard error of estimates (SEEs), or %SEEs. The validities of the NOG (r = 0.780), OVG (r = 0.776), and OBG (r = 0.791) models for BMI subgroups increased by 1.79%, 4.64%, and 8.22% respectively, and the reliabilities (NOG model: ICC = 0.755; OVG model: ICC = 0.765; OBG model: ICC = 0.779) increased by 3.18%, 3.27%, and 9.63%, respectively. These results suggested using separate models established in BMI stratifications can effectively improve the prediction of VO2max.

Do we need different predictive equations for the acute and late phases of critical illness? A prospective observational study with repeated indirect calorimetry measurements.

BackgroundPredictive equations (PEs) for estimating resting energy expenditure (REE) that have been developed from acute phase data may not be applicable in the late phase and vice versa. This study aimed to assess whether separate PEs are needed for acute and late phases of critical illness and to develop and validate PE(s) based on the results of this assessment.MethodsUsing indirect calorimetry, REE was measured at acute (≤5 days; n = 294) and late (≥6 days; n = 180) phases of intensive care unit admission. PEs were developed by multiple linear regression. A multi-fold cross-validation approach was used to validate the PEs. The best PEs were selected based on the highest coefficient of determination (R2), the lowest root mean square error (RMSE) and the lowest standard error of estimate (SEE). Two PEs developed from paired 168-patient data were compared with measured REE using mean absolute percentage difference.ResultsMean absolute percentage difference between predicted and measured REE was <20%, which is not clinically significant. Thus, a single PE was developed and validated from data of the larger sample size measured in the acute phase. The best PE for REE (kcal/day) was 891.6(Height) + 9.0(Weight) + 39.7(Minute Ventilation)−5.6(Age) – 354, with R2 = 0.442, RMSE = 348.3, SEE = 325.6 and mean absolute percentage difference with measured REE was: 15.1 ± 14.2% [acute], 15.0 ± 13.1% [late].ConclusionsSeparate PEs for acute and late phases may not be necessary. Thus, we have developed and validated a PE from acute phase data and demonstrated that it can provide optimal estimates of REE for patients in both acute and late phases.Trial registrationClinicalTrials.gov NCT03319329.

Optimization of the extraction of antioxidants from Moringa leaves: A comparative study between ultrasound‐ and ultra‐homogenizer‐assisted extractions

This work investigated the effect of the solvent composition and solid/solvent ratio on the extraction of antioxidants from Moringa oleifera Lam leaves using experimental design and compared the efficiency between the ultrasound- (UAE) and ultra-homogenizer (HAE)-assisted extractions. The phytochemical composition of the optimized extracts was investigated by ultra-high-performance liquid chromatography coupled with a high-resolution mass spectrometer (UHPLC-QTOF-MS/MS). The experimental results adequately fitted to quadratic models as indicated by the coefficient of determination (0.71–0.97), low standard errors of estimate, and lack of fit >0.05. The optimum conditions of extraction for both methods were identified as 4:200 solid:solvent (m/v) and a 1:1 water:ethanol (v/v) ratio. Thirty compounds were identified by UHPLC-QTOF-MS/MS, among which quercetin, rutin, kaempferol, alkaloids, and lignins. Both techniques studied were efficient in extracting antioxidants from Moringa leaves. The HAE equipment has a longer extraction time but is more accessible than the UAE equipment. Novelty Impact Statement UAE and HAE parameters were optimized for the recovery of antioxidants from Moringa leaves. HAE and UAE allowed obtaining extracts rich in antioxidant compounds. Impacts of solvent composition and solid/solvent ratio were assessed using RSM, and the optimal conditions found were: 4g of sample for 200 ml of solvent with 1:1 water:ethanol ratio. Quercetin, rutin, kaempferol, alkaloids, and lignins were detected in Moringa extracts using UHPLC-QTOF-MS/MS.

Magnitude, trend, and causes of under-five mortality from Kilite-Awlaelo health demographic surveillance database, northern Ethiopia, 2009\u20132017

BackgroundGlobally, neonatal and child mortality remains still high. Under-five mortality accounts for four-fifth of child and young adolescent deaths. In Ethiopia, though there has been a remarkable progress over the past years, under-five mortality is still high. Evidence from population-based longitudinal studies on under-five mortality is limited. Thus, this study aims to investigate the magnitude, trend, and causes of under-five mortality in the Kilite-Awlaelo Health Demographic Surveillance System, Northern Ethiopia.MethodsKilite-Awlaelo health and demographic surveillance system was established in 2009 in the northern part of Ethiopia. Population-based longitudinal study design was carried out through extracting data for nine consecutive years (2009–2017). After smoothing the data revealed a visually decreasing trend. Linear, quadratic, exponential, and autoregressive time-series models were checked. Accordingly, the exponential trend model provided the best fit with the lowest standard error of estimate, lowest sum square error and highest adjusted R2 value. Cause-specific mortality was determined by cross tabulating cause of death with specific age death.ResultsThe overall under-five mortality rate was 35.62 per 1000 livebirths. The under-five mortality rate of rural and urban residents was 37.58 and 12.99 deaths per 1000 livebirths respectively. The exponential trend model showed the under-five mortality rate was declining exponentially. Bacterial sepsis 67(20.6%), prematurity 37(11.08%), intestinal infection disease 30(8.98%), acute lower respiratory infections 26(7.78%), and birth asphyxia 24(7.19%) were the major causes of under-five mortality.ConclusionThe overall under-five mortality rate for the surveillance period was comparatively lower. A statistically significant difference in under-five mortality rate was observed between urban and rural residents. A statistically significant declining trend in the under-five mortality rate was observed. Bacterial sepsis, prematurity, intestinal infection disease, acute lower respiratory infections, and birth asphyxia were the major causes of under-five mortality. We recommend the huge discrepancy in under-five mortality rate between urban and rural dwellers could be narrowed to some level by increasing healthcare access for rural residents.

Curve Estimation Modeling for Predictions of the Novel Coronavirus (Covid-19) Epidemic in Nigeria

Based on the official Nigeria Centre for Disease Control (NCDC) data, the current research paper modeled the confirmed cases of the novel coronavirus disease 2019 (COVID-19) in Nigeria. Ten different curve regression models including linear, logarithmic, inverse, quadratic, cubic, compound, power, S-curve, growth, and exponential were used to fit the obtained official data. The cubic (R2 = 0.999) model gave the best fit for the entire country. However, the growth and exponential had the lowest standard error of estimate (0.958) and thus may best be used. The equations for these models were e0.78897+0.0944x and 2.2011e0.0944x respectively. In terms of confirmed cases in individual State, quadratic, cubic, compound, growth, power and exponential models generally best describe the official data for many states except for the state of Kogi which is best fitted with S-curve and inverse models. The error between the model and the official data curve is quite small especially for compound, power, growth and exponential models. The computed models will help to realized forward prediction and backward inference of the epidemic situation in Nigeria, and the relevant analysis help Federal and State governments to make vital decisions on how to manage the lockdown in the country.

Updated lower limb stature estimation equations for a South African population group

One of the main steps in the identification of an unknown person, from their skeletal remains, is the estimation of stature. Measurements of intact long bones of the upper and lower extremities are widely used for this purpose because of the high correlation that exists between these bones and stature. In 1987, Lundy and Feldesman presented regression equations for stature estimation for the black South African population group based on measurements of bones from the Raymond A. Dart Collection of Human Skeletons. Local anthropologists have questioned the validity of these equations. Living stature measurement and magnetic resonance imaging scanograms of 58 adult volunteers (28 males and 30 females) representing the modern black South African population group were obtained. Physiological length of the femur (FEPL) and physiological length of the tibia (TPL) were measured on each scanogram and substituted into appropriate equations of Lundy and Feldesman (S Afr J Sci. 1987;83:54–55) to obtain total skeletal height (TSHL&amp;F). Measured total skeletal height (TSHMeas) for each subject from scanograms was compared with TSHL&amp;F. Both FEPL and TPL presented with significantly high positive correlations with TSHMeas. A comparison between TSHL&amp;F and TSHMeas using a paired t-test, showed a statistically significant difference – an indication of non-validity of Lundy and Feldesman’s equations. New regression equations for estimation of living stature were formulated separately for male and female subjects. The standard error of estimate was low, which compared well with those reported for other studies that used long limb bones.&#x0D; Significance:&#x0D; &#x0D; Statistically significant differences were observed between measured and estimated skeletal height, thus confirming non-validity of Lundy and Fieldsman’s (1987) equations for lower limb bones.&#x0D; New regression equations for living stature estimation were formulated for femur and tibia lengths, and the low standard error of estimates of equations compared well to results from other studies.&#x0D;

Efficient removal of Cd (II) from contaminated water and soils using nanoparticles from nitrogen fertilizer industry waste.

Cadmium (Cd) is used extencively in many industries and can cause environmenal pollution and severe damage to human health. As millions of tons of lime-based solid by-product from nitrogen fertilizer industry (NFIB) are produced each year, the main purpose of this study was to develop a novel, efficient and cheap nanoscale sorbent from NFIB for remediation of Cd (II) contaminated soil and water to protect and preserve public and ecosystem health. A novel nanoscale adsorbent was developed from the nitrogen fertilizer industry byproduct (NFIB) and was characterized using X-ray diffraction(XRD) and scanning electron microscope (SEM). Batch sorption equilibrium and kinetic experiments were conducted to evaluate the efficiency of nano- NFIB (nNFIB) in sequestering Cd(II) in contaminated soil and water. The results of adorption equilibrium and kinetics experiments revealed that Langmuir and power function models best described Cd adsorption on bulk NFIB and nNFIB as evidenced by high R2(determination coefficient) and low SE(standard error of estimates) values. The Langmuir maximum adsorption capacity (q푞max) of nNFIB for Cd(II) was 100mgg-1 which is twenty times higher than that of Bulk NFIB. The distinguishing features of NIFB nanoparticles involve efficient removal of Cd(II) from contaminated water (>90%) and enhancement of Cd (II) immobilization (146%) in cotaminated soil.Fourier Transmission Infrared (FTIR) spectra of Cd(II) contaminated water and soil before and after nNFIB application revealed the important rule of calcite nanoparticles in Cd(II) sequestration. The accessibility, low cost, and Cd sequestration efficiency of nNFIB nominate it to be an economic and a promised adsorbent for environmental remediation.

Development & cross-validation of anthropometric predictive models to estimate the appendicular skeletal muscle mass in middle-aged women in Sri Lanka.

Background & objectives:Attempts have been made to estimate appendicular skeletal muscle mass (ASMM) using anthropometric indices and most of these are country specific. This study was designed to develop and cross-validate simple predictive models to estimate the ASMM based on anthropometry in a group of healthy middle-aged women in Sri Lanka.Methods:The study was conducted on a randomly selected group of community-dwelling women aged 30-60 years. ASMM (kg) quantified with dual-energy X-ray absorptiometry (DXA) (ASMMDXA) was used as the reference standard. Anthropometric measurements such as body weight (kg), height (m), limb circumferences (cm) and skinfold thickness (mm) which showed significant correlations with ASMMDXA, were used to develop the models. The models were developed using a group of 165 women (aged 30-60 yr) and were cross-validated using a separate sample of women (n=167) (mean age: 48.9±8.56 yr), selected randomly.Results:Nine anthropometry-based models were developed using weight, height, skinfold thicknesses, circumferences, body mass index, menopausal status (MS) and age as independent variables. Four models which were based on height, weight, triceps skinfold thickness (TSFT), age and MS met all the validation criteria with high correlations (ranged 0.89-0.92) and high predictive values explaining high variance (80-84%) with low standard error of estimate (1.10-1.24 kg).Interpretation & conclusions:The four models (ASMM 1-ASMM 4) developed based on height, weight, TSFT, age and MS showed a high accuracy in estimating the ASMM in middle-aged women.

Dendrometric evaluation of a clonal population of Tectona grandis in forest-livestock system

ABSTRACT: Production of Tectona grandis (teak) in integrated systems with livestock or agriculture demonstrates high potential of financial return. However, studies on the development of teak are still scarce, especially in the northern region state of Mato Grosso. In this study we sought to evaluate dendometric variables of a clonal population of teak in a forest-livestock integration system (LFIS), during a period of 53 months in the city of Alta Floresta, Mato Grosso, Brazil. For this purpose, three installments were samples, a total of 360 individuals, and for each the diameter was measured at 1.3 meters from the base so as to calculate the total volume, the current annual increment (CAI) and mean annual increment (MAI), and five adjusted regression models. The Hoerl model provided the highest adjusted coefficient of determination (R2aj), lowest standard error of estimate (Syx), coefficient of variation (CV %), and from this the growth curves were developed. Clonal stands of teakin the forest-livestock system presented increases in DBH, height and volume were superior in relation to other scientific studies with teak, indicating their viability in integrated systems with pastures in the region.

Evaluation of atmospheric correction models and Landsat surface reflectance product in Daerah Istimewa Yogyakarta, Indonesia

The atmospheric disturbance in remote sensing imagery greatly influences the object’s spectral response in the imagery. This, in turn, will impact the object characterization. The atmospheric effects on remote sensing imagery can be reduced through atmospheric correction. There are various types of atmospheric correction methods and each of them has its own working principles. Daerah Istimewa Yogyakarta (DIY) Province, Indonesia, was chosen to be study area for this research. The research objectives are to evaluate the atmospheric correction method, which consist of Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), Quick Atmospheric Correction (QUAC), Dark Object Subtraction (DOS), Second Simulation of the Satellite Signal in the Solar Spectrum (6S), Atmospheric Correction (ATCOR2), and Landsat 8 Surface Reflectance Code (LaSRC) by NASA. The compared objects consist of water, vegetation, and soil objects. The evaluation was based on Standard Error of Estimate (SEE), accuracy, and curve pattern. The result shows that the best atmospheric correction varies on each object. The spectral response curve pattern shows similarity but each object has its own accurate atmospheric method based on SEE result. The FLAASH, 6s, and ATCOR2 method show the lowest SEE result for mature vegetation leaves, beach sand, sand suns, and, lagoon, while QUAC method shows the lowest SEE result for young vegetation leaves, paddy plants, grass, and reservoir.

Stature Reconstruction from Percutaneous Anthropometry of Long Bones of Upper Extremity of Nigerians in the University of Lagos

Anthropometric databases are essential in biological profiling. Stature reconstruction could play an integral role in helping forensic anthropologists in establishing the identity of human remains at crime and/or disaster scenes. This study aimed to derive simple and multiple linear regression equations for reconstruction of stature from upper limb segments in a living adult Nigerian population. The sample included 230 adult medical students at the University of Lagos, Nigeria (100 males and 130 females), aged 18 to 36 years. Direct anthropometric methods were employed to measure all variables. Stature, arm and forearm lengths were measured. Results showed a strong positive correlation between long bones and stature ( p < 0.01). Both simple and multiple regression equations were derived and showed low standard errors of estimate and high coefficients of determination (R2). This study presents a simple and reliable approach to predict stature, which may help forensic scientists in disaster victim identification and in medico-legal jurisprudence.

Predictive factors of peak aerobic capacity using simple measurements of anthropometry and musculoskeletal fitness in paraplegic men.

Protocols for evaluating cardiovascular fitness for wheelchair-dependent persons are still scarcely accessible due to the requirements such as specialized equipment and expertise in community or public healthcare settings. This study aimed to assess the potential of secondary utilization of musculoskeletal fitness components for estimating peak oxygen uptake (VO2peak) in paraplegic men. Thirty-three paraplegic men (T1-L3) aged 23-63 years underwent anthropometry (height, weight and calculated body mass index and body surface area) and upper-body musculoskeletal fitness tests (back-scratch, arm-curls and handgrip strength tests) and performed a graded exercise test with an arm-crank ergometer on two non-consecutive days. To determine the relationship between VO2peak and various kinanthropometric parameters and derive a regression model that predicts the VO2peak, uni- and multivariate analyses were conducted, respectively. Maximal numbers of arm curls on either arm (r=0.486, P=0.004) and the back-scratch distance (r=0.426, P=0.013) were moderately correlated with VO2peak. Moreover, among the indices of handgrip strength, average handgrip strength of both hands divided by the body surface area showed a strong correlation coefficient with VO2peak (r=0.674, P<0.001). Multivariate linear regression analysis indicated that muscular endurance and strength were the main predictors for estimating VO2peak. Considering shoulder flexibility, age, and anthropometric variables, the regression model showed the highest adjusted R2 of 0.811 and lowest standard error of estimate of 3.54 mL·kg-1·min-1 (P<0.001). The Bland-Altman plots indicated good agreement between actual and estimated VO2peak. The mean absolute prediction error was 11.9%. Musculoskeletal fitness and anthropometric components may be predictive factors of a new conceptual modality estimating concomitant cardiorespiratory fitness beyond their traditional health-related indications.

Sexual Dimorphism and Estimation of Height from Body Length Anthropometric Parameters among the Hausa Ethnic Group of Nigeria

The study was carried out to investigate the sexual dimorphism in length and other anthropometric parameters. To also generate formulae for height estimation using anthropometric measurements of some length parameters among Hausa ethnic group of Kaduna State, Nigeria. A cross sectional study was conducted and a total of 500 subjects participated in this study which was mainly secondary school students between the age ranges of 16-27 years, anthropometric measurements were obtained using standard protocols. It was observed that there was significant sexual dimorphism in all the parameters except for body mass index. In all the parameters males tend to have significantly (P < 0.05) higher mean values except biaxillary distances. Height showed positive and strongest correlations with demispan length, followed by knee height, thigh length, sitting height, hand length, foot length, humeral length, forearm length and weight respectively. There were weak and positive correlations between height and neck length as well as biaxillary length. The demi span length showed the strongest correlation coefficient and low standard error of estimate indicating the strong estimation ability than other parameters. The combination of two parameters tends to give better estimations and low standard error of estimates, so also combining the three parameters gives better estimations with a lower standard error of estimates. The better correlation coefficient was also observed with the double and triple parameters respectively. Male Hausa tend to have larger body proportion compared to female. Height showed positive and strongest correlations with demispan length. Body length anthropometric proved to be useful in estimation of stature among Hausa ethnic group of Kaduna state Nigeria.

Validation of anthropometric models in the estimation of appendicular lean soft tissue in young athletes

Magnetic resonance imaging and computer tomography are gold standards in the measurement of muscle tissue (MT), but are expensive. Dual Energy X-Ray Absorptiometry (DXA) is also costly but safer and allows for the measurement of Appendicular Lean Soft Tissue (ALST), a strong predictor of MT. Alternatively, there are anthropometric models that predict the ALST of Portuguese athletes with low cost/risk that have not been validated in other populations. The aim of this study was to validate anthropometric Portuguese models that predict ALST in young athletes or, if the validation fails, to propose new models. The ALSTDXA of 174 young athletes was determined by DXA. Two anthropometric models (ALSTmod1 and ALSTmod2) measuring ALST among Portuguese athletes were tested. To validate the coefficient of determination, the difference (bias) and concordance correlation coefficient between predicted and actual values were computed. Finally, association between mean and difference of methods was verified. Validation failed and, for this reason, new multiple regression models were proposed and validated using PRESS statistics. The Portuguese models explained ~96% of the ALSTDXA variability. The difference between ALSTmod1 and ALSTDXA (-0.7kg) was less than that found for the ALSTmod2 and ALSTDXA (-2.3kg), with limits of agreement from 3.6 to -2.1 and from 6.1 to -1.5kg, respectively. The new models included three predictive equations for ALST. Only ASLTmod1 was valid; however, it was prone to bias, depending on the magnitude of ALST values. The newly proposed models present validity with greater concordance (r²PRESS=0.98), lower standard error of estimate (SEEPRESS [kg]=0.91) and more homogeneous predicted extreme values.