Assessment of body composition in Sri Lankan children: Validation of a bioelectrical impedance prediction equation

Objective: To develop bioelectrical impedance analysis (BIA) equations to predict total body water (TBW) and fat-free mass (FFM) of Sri Lankan children. Subjects/Methods: Data were collected from 5- to 15-year-old healthy children. They were randomly assigned to validation (M/F: 105/83) and cross-validation (M/F: 53/41) groups. Height, weight and BIA were measured. TBW was assessed using isotope dilution method (D2 O). Multiple regression analysis was used to develop preliminary equations and cross-validated on an independent group. Final prediction equation was constructed combining the two groups and validated by PRESS (prediction of sum of squares) statistics. Impedance index (height2/impedance; cm2/Ω), weight and sex code (male = 1; female = 0) were used as variables. Results: Independent variables of the final prediction equation for TBW were able to predict 86.3% of variance with root means-squared error (RMSE) of 2.1l. PRESS statistics was 2.1l with press residuals of 1.2l. Independent variables were able to predict 86.9% of variance of FFM with RMSE of 2.7 kg. PRESS statistics was 2.8 kg with press residuals of 1.4 kg. Bland Altman technique showed that the majority of the residuals were within mean bias±1.96 s.d. Conclusions: Results of this study provide BIA equation for the prediction of TBW and FFM in Sri Lankan children. To the best of our knowledge there are no published BIA prediction equations validated on South Asian populations. Results of this study need to be affirmed by more studies on other closely related populations by using multi-component body composition assessment.

Ground motion prediction equation considering combined dataset of recorded and simulated ground motions

Himalayan region is one of the most active seismic regions in the world and many researchers have highlighted the possibility of great seismic event in the near future due to seismic gap. Seismic hazard analysis and microzonation of highly populated places in the region are mandatory in a regional scale. Region specific Ground Motion Predictive Equation (GMPE) is an important input in the seismic hazard analysis for macro- and micro-zonation studies. Few GMPEs developed in India are based on the recorded data and are applicable for a particular range of magnitudes and distances. This paper focuses on the development of a new GMPE for the Himalayan region considering both the recorded and simulated earthquakes of moment magnitude 5.3-8.7. The Finite Fault simulation model has been used for the ground motion simulation considering region specific seismotectonic parameters from the past earthquakes and source models. Simulated acceleration time histories and response spectra are compared with available records. In the absence of a large number of recorded data, simulations have been performed at unavailable locations by adopting Apparent Stations concept. Earthquakes recorded up to 2007 have been used for the development of new GMPE and earthquakes records after 2007 are used to validate new GMPE. Proposed GMPE matched very well with recorded data and also with other highly ranked GMPEs developed elsewhere and applicable for the region. Comparison of response spectra also have shown good agreement with recorded earthquake data. Quantitative analysis of residuals for the proposed GMPE and region specific GMPEs to predict Nepal-India 2011 earthquake of Mw of 5.7 records values shows that the proposed GMPE predicts Peak ground acceleration and spectral acceleration for entire distance and period range with lower percent residual when compared to exiting region specific GMPEs. Crown Copyright (C) 2013 Published by Elsevier Ltd. All rights reserved.

Estimating total body fat using a skinfold prediction equation in Brazilian children

Background: The double burden of obesity and underweight is increasing in developing countries and simple methods for the assessment of fat mass in children are needed. Aim: To develop and validate a new anthropometric predication equation for assessment of fat mass in children. Subjects and methods: Body composition was assessed in 145 children aged 9.8 +/- 1.3 (SD) years from Sao Paulo, Brazil using dual energy X-ray absorptiometry (DEXA) and skinfold measurements. The study sample was divided into development and validation sub-sets to develop a new prediction equation for FM (PE). Results: Using multiple linear regression analyses, the best equation for predicting FM (R-2 - 0.77) included body weight, triceps skinfold, height, gender and age as independent variables. When cross-validated, the new PE was valid in this sample (R-2 = 0.80), while previously published equations were not. Conclusion: The PE was more valid for Brazilian children that existing equations, but further studies are needed to assess the validity of this PE in other populations.

Development and validation of anthropometric prediction equations for estimation of lean body mass and appendicular lean soft tissue in Indian men and women

Lean body mass (LBM) and muscle mass remains difficult to quantify in large epidemiological studies due to non-availability of inexpensive methods. We therefore developed anthropometric prediction equations to estimate the LBM and appendicular lean soft tissue (ALST) using dual energy X-ray absorptiometry (DXA) as a reference method. Healthy volunteers (n= 2220; 36% females; age 18-79 y) representing a wide range of body mass index (14-44 kg/m2) participated in this study. Their LBM including ALST was assessed by DXA along with anthropometric measurements. The sample was divided into prediction (60%) and validation (40%) sets. In the prediction set, a number of prediction models were constructed using DXA measured LBM and ALST estimates as dependent variables and a combination of anthropometric indices as independent variables. These equations were cross-validated in the validation set. Simple equations using age, height and weight explained > 90% variation in the LBM and ALST in both men and women. Additional variables (hip and limb circumferences and sum of SFTs) increased the explained variation by 5-8% in the fully adjusted models predicting LBM and ALST. More complex equations using all the above anthropometric variables could predict the DXA measured LBM and ALST accurately as indicated by low standard error of the estimate (LBM: 1.47 kg and 1.63 kg for men and women, respectively) as well as good agreement by Bland Altman analyses. These equations could be a valuable tool in large epidemiological studies assessing these body compartments in Indians and other population groups with similar body composition.

Development and validation of anthropometric prediction equations for estimation of body fat in Indonesian men

Body composition of 292 males aged between 18 and 65 years was measured using the deuterium oxide dilution technique. Participants were divided into development (n=146) and cross-validation (n=146) groups. Stature, body weight, skinfold thickness at eight sites, girth at five sites, and bone breadth at four sites were measured and body mass index (BMI), waist-to-hip ratio (WHR), and waist-to-stature ratio (WSR) calculated. Equations were developed using multiple regression analyses with skinfolds, breadth and girth measures, BMI, and other indices as independent variables and percentage body fat (%BF) determined from deuterium dilution technique as the reference. All equations were then tested in the cross-validation group. Results from the reference method were also compared with existing prediction equations by Durnin and Womersley (1974), Davidson et al (2011), and Gurrici et al (1998). The proposed prediction equations were valid in our cross-validation samples with r=0.77- 0.86, bias 0.2-0.5%, and pure error 2.8-3.6%. The strongest was generated from skinfolds with r=0.83, SEE 3.7%, and AIC 377.2. The Durnin and Womersley (1974) and Davidson et al (2011) equations significantly (p<0.001) underestimated %BF by 1.0 and 6.9% respectively, whereas the Gurrici et al (1998) equation significantly (p<0.001) overestimated %BF by 3.3% in our cross-validation samples compared to the reference. Results suggest that the proposed prediction equations are useful in the estimation of %BF in Indonesian men.

Predictive validity of accelerometer prediction equations for energy expenditure (EE) during overland walking and running in children and adolescents

Purpose The aim of this study was to assess the predictive validity of three accelerometer prediction equations (Freedson et aL, 1997; Trost et aL, 1998; Puyau et al., 2002) for energy expenditure (EE) during overland walking and running in children and adolescents. Methods 45 healthy children and adolescents aged 10-18 completed the following protocol, each task 5-mins in duration, with a 5-min rest period in between; walking normally; walking briskly; running easily and running fast. During each task participants wore MTI (WAM 7164) Actigraphs on the left and right hips. VO2 was monitored breath by breath using the Cosmed K4b2 portable indirect calorimetry system. For each prediction equation, difference scores were calculated as EE measured minus EE predicted. The percentage of 1-min epochs correctly categorized as light (<3 METs), moderate (3-5.9 METs), and vigorous (≥6 METS) was also calculated. Results The Freedson and Trost equations consistently overestimated MET level. The level of overestimation was statistically significant across all tasks for the Freedson equation, and was significant for only the walking tasks for the Trost equation. The Puyau equation consistently underestimated AEE with the exception of the walking normally task. In terms of categorisation, the Freedson equation (72.8% agreement) demonstrated better agreement than the Puyau (60.6%). Conclusions These data suggest that the three accelerometer prediction equations do not accurately predict EE on a minute-by-minute basis in children and adolescents during overland walking and running. However, the cut points generated by these equations maybe useful for classifying activity as either, light, moderate, or vigorous.

Development of height-weight based equation for assessment of body composition in Sri Lankan children

Objective To develop a height and weight based equation to estimate total body water (TBW) in Sri Lankan children. Methods Cross sectional descriptive study done involving 5–15 year old healthy children. Height and weight were measured. TBW was assessed using isotope dilution method (D2O) and fat free mass (FFM) calculated. Multiple regression analysis was used to develop prediction equation and validated using PRESS statistical technique. Height, weight and sex code (male=1; female=0) were used as prediction variables. Results This study provides height and weight equation for the prediction of TBW in Sri Lankan children. To the best of our knowledge there are no published height weight prediction equations validated on South Asian populations. Conclusion Results of this study need to be affirmed by more studies on other closely related populations by using multicomponent body composition.

1RM prediction: a data mining case study

In the last few years there has been a heightened interest in data treatment and analysis with the aim of discovering hidden knowledge and eliciting relationships and patterns within this data. Data mining techniques (also known as Knowledge Discovery in Databases) have been applied over a wide range of fields such as marketing, investment, fraud detection, manufacturing, telecommunications and health. In this study, well-known data mining techniques such as artificial neural networks (ANN), genetic programming (GP), forward selection linear regression (LR) and k-means clustering techniques, are proposed to the health and sports community in order to aid with resistance training prescription. Appropriate resistance training prescription is effective for developing fitness, health and for enhancing general quality of life. Resistance exercise intensity is commonly prescribed as a percent of the one repetition maximum. 1RM, dynamic muscular strength, one repetition maximum or one execution maximum, is operationally defined as the heaviest load that can be moved over a specific range of motion, one time and with correct performance. The safety of the 1RM assessment has been questioned as such an enormous effort may lead to muscular injury. Prediction equations could help to tackle the problem of predicting the 1RM from submaximal loads, in order to avoid or at least, reduce the associated risks. We built different models from data on 30 men who performed up to 5 sets to exhaustion at different percentages of the 1RM in the bench press action, until reaching their actual 1RM. Also, a comparison of different existing prediction equations is carried out. The LR model seems to outperform the ANN and GP models for the 1RM prediction in the range between 1 and 10 repetitions. At 75% of the 1RM some subjects (n = 5) could perform 13 repetitions with proper technique in the bench press action, whilst other subjects (n = 20) performed statistically significant (p < 0:05) more repetitions at 70% than at 75% of their actual 1RM in the bench press action. Rate of perceived exertion (RPE) seems not to be a good predictor for 1RM when all the sets are performed until exhaustion, as no significant differences (p < 0:05) were found in the RPE at 75%, 80% and 90% of the 1RM. Also, years of experience and weekly hours of strength training are better correlated to 1RM (p < 0:05) than body weight. O'Connor et al. 1RM prediction equation seems to arise from the data gathered and seems to be the most accurate 1RM prediction equation from those proposed in literature and used in this study. Epley's 1RM prediction equation is reproduced by means of data simulation from 1RM literature equations. Finally, future lines of research are proposed related to the problem of the 1RM prediction by means of genetic algorithms, neural networks and clustering techniques. RESUMEN En los últimos años ha habido un creciente interés en el tratamiento y análisis de datos con el propósito de descubrir relaciones, patrones y conocimiento oculto en los mismos. Las técnicas de data mining (también llamadas de \Descubrimiento de conocimiento en bases de datos\) se han aplicado consistentemente a lo gran de un gran espectro de áreas como el marketing, inversiones, detección de fraude, producción industrial, telecomunicaciones y salud. En este estudio, técnicas bien conocidas de data mining como las redes neuronales artificiales (ANN), programación genética (GP), regresión lineal con selección hacia adelante (LR) y la técnica de clustering k-means, se proponen a la comunidad del deporte y la salud con el objetivo de ayudar con la prescripción del entrenamiento de fuerza. Una apropiada prescripción de entrenamiento de fuerza es efectiva no solo para mejorar el estado de forma general, sino para mejorar la salud e incrementar la calidad de vida. La intensidad en un ejercicio de fuerza se prescribe generalmente como un porcentaje de la repetición máxima. 1RM, fuerza muscular dinámica, una repetición máxima o una ejecución máxima, se define operacionalmente como la carga máxima que puede ser movida en un rango de movimiento específico, una vez y con una técnica correcta. La seguridad de las pruebas de 1RM ha sido cuestionada debido a que el gran esfuerzo requerido para llevarlas a cabo puede derivar en serias lesiones musculares. Las ecuaciones predictivas pueden ayudar a atajar el problema de la predicción de la 1RM con cargas sub-máximas y son empleadas con el propósito de eliminar o al menos, reducir los riesgos asociados. En este estudio, se construyeron distintos modelos a partir de los datos recogidos de 30 hombres que realizaron hasta 5 series al fallo en el ejercicio press de banca a distintos porcentajes de la 1RM, hasta llegar a su 1RM real. También se muestra una comparación de algunas de las distintas ecuaciones de predicción propuestas con anterioridad. El modelo LR parece superar a los modelos ANN y GP para la predicción de la 1RM entre 1 y 10 repeticiones. Al 75% de la 1RM algunos sujetos (n = 5) pudieron realizar 13 repeticiones con una técnica apropiada en el ejercicio press de banca, mientras que otros (n = 20) realizaron significativamente (p < 0:05) más repeticiones al 70% que al 75% de su 1RM en el press de banca. El ínndice de esfuerzo percibido (RPE) parece no ser un buen predictor del 1RM cuando todas las series se realizan al fallo, puesto que no existen diferencias signifiativas (p < 0:05) en el RPE al 75%, 80% y el 90% de la 1RM. Además, los años de experiencia y las horas semanales dedicadas al entrenamiento de fuerza están más correlacionadas con la 1RM (p < 0:05) que el peso corporal. La ecuación de O'Connor et al. parece surgir de los datos recogidos y parece ser la ecuación de predicción de 1RM más precisa de aquellas propuestas en la literatura y empleadas en este estudio. La ecuación de predicción de la 1RM de Epley es reproducida mediante simulación de datos a partir de algunas ecuaciones de predicción de la 1RM propuestas con anterioridad. Finalmente, se proponen futuras líneas de investigación relacionadas con el problema de la predicción de la 1RM mediante algoritmos genéticos, redes neuronales y técnicas de clustering.

Body composition and its relationship to metabolic risk factors in Asian children

Obesity is a major public health problem in both developed and developing countries. The body mass index (BMI) is the most common index used to define obesity. The universal application of the same BMI classification across different ethnic groups is being challenged due to the inability of the index to differentiate fat mass (FM) and fat�]free mass (FFM) and the recognized ethnic differences in body composition. A better understanding of the body composition of Asian children from different backgrounds would help to better understand the obesity�]related health risks of people in this region. Moreover, the limitations of the BMI underscore the necessity to use where possible, more accurate measures of body fat assessment in research and clinical settings in addition to BMI, particularly in relation to the monitoring of prevention and treatment efforts. The aim of the first study was to determine the ethnic difference in the relationship between BMI and percent body fat (%BF) in pre�]pubertal Asian children from China, Lebanon, Malaysia, the Philippines, and Thailand. A total of 1039 children aged 8�]10 y were recruited using a non�]random purposive sampling approach aiming to encompass a wide BMI range from the five countries. Percent body fat (%BF) was determined using the deuterium dilution technique to quantify total body water (TBW) and subsequently derive proportions of FM and FFM. The study highlighted the sex and ethnic differences between BMI and %BF in Asian children from different countries. Girls had approximately 4.0% higher %BF compared with boys at a given BMI. Filipino boys tended to have a lower %BF than their Chinese, Lebanese, Malay and Thai counterparts at the same age and BMI level (corrected mean %BF was 25.7�}0.8%, 27.4�}0.4%, 27.1�}0.6%, 27.7�}0.5%, 28.1�}0.5% for Filipino, Chinese, Lebanese, Malay and Thai boys, respectively), although they differed significantly from Thai and Malay boys. Thai girls had approximately 2.0% higher %BF values than Chinese, Lebanese, Filipino and Malay counterparts (however no significant difference was seen among the four ethnic groups) at a given BMI (corrected mean %BF was 31.1�}0.5%, 28.6�}0.4%, 29.2�}0.6%, 29.5�}0.6%, 29.5�}0.5% for Thai, Chinese, Lebanese, Malay and Filipino girls, respectively). However, the ethnic difference in BMI�]%BF relationship varied by BMI. Compared with Caucasians, Asian children had a BMI 3�]6 units lower for a given %BF. More than one third of obese Asian children in the study were not identified using the WHO classification and more than half were not identified using the International Obesity Task Force (IOTF) classification. However, use of the Chinese classification increased the sensitivity by 19.7%, 18.1%, 2.3%, 2.3%, and 11.3% for Chinese, Lebanese, Malay, Filipino and Thai girls, respectively. A further aim of the first study was to determine the ethnic difference in body fat distribution in pre�]pubertal Asian children from China, Lebanon, Malaysia, and Thailand. The skin fold thicknesses, height, weight, waist circumference (WC) and total adiposity (as determined by deuterium dilution technique) of 922 children from the four countries was assessed. Chinese boys and girls had a similar trunk�]to�]extremity skin fold thickness ratio to Thai counterparts and both groups had higher ratios than the Malays and Lebanese at a given total FM. At a given BMI, both Chinese and Thai boys and girls had a higher WC than Malays and Lebanese (corrected mean WC was 68.1�}0.2 cm, 67.8�}0.3 cm, 65.8�}0.4 cm, 64.1�}0.3 cm for Chinese, Thai, Lebanese and Malay boys, respectively; 64.2�}0.2 cm, 65.0�}0.3 cm, 62.9�}0.4 cm, 60.6�}0.3 cm for Chinese, Thai, Lebanese and Malay girls, respectively). Chinese boys and girls had lower trunk fat adjusted subscapular/suprailiac skinfold ratio compared with Lebanese and Malay counterparts. The second study aimed to develop and cross�]validate bioelectrical impedance analysis (BIA) prediction equations of TBW and FFM for Asian pre�]pubertal children from China, Lebanon, Malaysia, the Philippines, and Thailand. Data on height, weight, age, gender, resistance and reactance measured by BIA were collected from 948 Asian children (492 boys and 456 girls) aged 8�]10 y from the five countries. The deuterium dilution technique was used as the criterion method for the estimation of TBW and FFM. The BIA equations were developed from the validation group (630 children randomly selected from the total sample) using stepwise multiple regression analysis and cross�]validated in a separate group (318 children) using the Bland�]Altman approach. Age, gender and ethnicity influenced the relationship between the resistance index (RI = height2/resistance), TBW and FFM. The BIA prediction equation for the estimation of TBW was: TBW (kg) = 0.231�~Height2 (cm)/resistance (ƒ¶) + 0.066�~Height (cm) + 0.188�~Weight (kg) + 0.128�~Age (yr) + 0.500�~Sex (male=1, female=0) . 0.316�~Ethnicity (Thai ethnicity=1, others=0) �] 4.574, and for the estimation of FFM: FFM (kg) = 0.299�~Height2 (cm)/resistance (ƒ¶) + 0.086�~Height (cm) + 0.245�~Weight (kg) + 0.260�~Age (yr) + 0.901�~Sex (male=1, female=0) �] 0.415�~Ethnicity (Thai ethnicity=1, others=0) �] 6.952. The R2 was 88.0% (root mean square error, RSME = 1.3 kg), 88.3% (RSME = 1.7 kg) for TBW and FFM equation, respectively. No significant difference between measured and predicted TBW and between measured and predicted FFM for the whole cross�]validation sample was found (bias = �]0.1�}1.4 kg, pure error = 1.4�}2.0 kg for TBW and bias = �]0.2�}1.9 kg, pure error = 1.8�}2.6 kg for FFM). However, the prediction equation for estimation of TBW/FFM tended to overestimate TBW/FFM at lower levels while underestimate at higher levels of TBW/FFM. Accuracy of the general equation for TBW and FFM compared favorably with both BMI�]specific and ethnic�]specific equations. There were significant differences between predicted TBW and FFM from external BIA equations derived from Caucasian populations and measured values in Asian children. There were three specific aims of the third study. The first was to explore the relationship between obesity and metabolic syndrome and abnormalities in Chinese children. A total of 608 boys and 800 girls aged 6�]12 y were recruited from four cities in China. Three definitions of pediatric metabolic syndrome and abnormalities were used, including the International Diabetes Federation (IDF) and National Cholesterol Education Program (NCEP) definition for adults modified by Cook et al. and de Ferranti et al. The prevalence of metabolic syndrome varied with different definitions, was highest using the de Ferranti definition (5.4%, 24.6% and 42.0%, respectively for normal�]weight, overweight and obese children), followed by the Cook definition (1.5%, 8.1%, and 25.1%, respectively), and the IDF definition (0.5%, 1.8% and 8.3%, respectively). Overweight and obese children had a higher risk of developing the metabolic syndrome compared to normal�]weight children (odds ratio varied with different definitions from 3.958 to 6.866 for overweight children, and 12.640�]26.007 for obese children). Overweight and obesity also increased the risk of developing metabolic abnormalities. Central obesity and high triglycerides (TG) were the most common while hyperglycemia was the least frequent in Chinese children regardless of different definitions. The second purpose was to determine the best obesity index for the prediction of cardiovascular (CV) risk factor clustering across a 2�]y follow�]up among BMI, %BF, WC and waist�]to�]height ratio (WHtR) in Chinese children. Height, weight, WC, %BF as determined by BIA, blood pressure, TG, high�]density lipoprotein cholesterol (HDL�]C), and fasting glucose were collected at baseline and 2 years later in 292 boys and 277 girls aged 8�]10 y. The results showed the percentage of children who remained overweight/obese defined on the basis of BMI, WC, WHtR and %BF was 89.7%, 93.5%, 84.5%, and 80.4%, respectively after 2 years. Obesity indices at baseline significantly correlated with TG, HDL�]C, and blood pressure at both baseline and 2 years later with a similar strength of correlations. BMI at baseline explained the greatest variance of later blood pressure. WC at baseline explained the greatest variance of later HDL�]C and glucose, while WHtR at baseline was the main predictor of later TG. Receiver�]operating characteristic (ROC) analysis explored the ability of the four indices to identify the later presence of CV risk. The overweight/obese children defined on the basis of BMI, WC, WHtR or %BF were more likely to develop CV risk 2 years later with relative risk (RR) scores of 3.670, 3.762, 2.767, and 2.804, respectively. The final purpose of the third study was to develop age�] and gender�]specific percentiles of WC and WHtR and cut�]off points of WC and WHtR for the prediction of CV risk in Chinese children. Smoothed percentile curves of WC and WHtR were produced in 2830 boys and 2699 girls aged 6�]12 y randomly selected from southern and northern China using the LMS method. The optimal age�] and gender�]specific thresholds of WC and WHtR for the prediction of cardiovascular risk factors clustering were derived in a sub�]sample (n=1845) by ROC analysis. Age�] and gender�]specific WC and WHtR percentiles were constructed. The WC thresholds were at the 90th and 84th percentiles for Chinese boys and girls, respectively, with sensitivity and specificity ranging from 67.2% to 83.3%. The WHtR thresholds were at the 91st and 94th percentiles for Chinese boys and girls, respectively, with sensitivity and specificity ranging from 78.6% to 88.9%. The cut�]offs of both WC and WHtR were age�] and gender�]dependent. In conclusion, the current thesis quantifies the ethnic differences in the BMI�]%BF relationship and body fat distribution between Asian children from different origins and confirms the necessity to consider ethnic differences in body composition when developing BMI and other obesity index criteria for obesity in Asian children. Moreover, ethnicity is also important in BIA prediction equations. In addition, WC and WHtR percentiles and thresholds for the prediction of CV risk in Chinese children differ from other populations. Although there was no advantage of WC or WHtR over BMI or %BF in the prediction of CV risk, obese children had a higher risk of developing the metabolic syndrome and abnormalities than normal�]weight children regardless of the obesity index used.

Validity of the computer science and applications (CSA) activity monitor in children

Purpose The purpose of this study was to evaluate the validity of the CSA activity monitor as a measure of children's physical activity using energy expenditure (EE) as a criterion measure. Methods Thirty subjects aged 10 to 14 performed three 5-min treadmill bouts at 3, 4, and 6 mph, respectively. While on the treadmill, subjects wore CSA (WAM 7164) activity monitors on the right and left hips. (V) over dot O-2 was monitored continuously by an automated system. EE was determined by multiplying the average (V) over dot O-2 by the caloric equivalent of the mean respiratory exchange ratio. Results Repeated measures ANOVA indicated that both CSA monitors were sensitive to changes in treadmill speed. Mean activity counts from each CSA unit were not significantly different and the intraclass reliability coefficient for the two CSA units across all speeds was 0.87. Activity counts from both CSA units were strongly correlated with EE (r = 0.86 and 0.87, P < 0.001). An EE prediction equation was developed from 20 randomly selected subjects and cross-validated on the remaining 10. The equation predicted mean EE within 0.01 kcal.min(-1). The correlation between actual and predicted values was 0.93 (P < 0.01) and the SEE was 0.93 kcal.min(-1). Conclusion These data indicate that the CSA monitor is a valid and reliable tool for quantifying treadmill walking and running in children.

Influence of posture and frequency modes in total body water estimation using bioelectrical impedance spectroscopy in boys and adult males

The aim of the study was to examine differences in total body water (TBW) measured using single-frequency (SF) and multi-frequency (MF) modes of bioelectrical impedance spectroscopy (BIS) in children and adults measured in different postures using the deuterium (2H) dilution technique as the reference. Twenty-three boys and 26 adult males underwent assessment of TBW using the dilution technique and BIS measured in supine and standing positions using two frequencies of the SF mode (50 kHz and 100 kHz) and the MF mode. While TBW estimated from the MF mode was comparable, extra-cellular fluid (ECF) and intra-cellular fluid (ICF) values differed significantly (p < 0.01) between the different postures in both groups. In addition, while estimated TBW in adult males using the MF mode was significantly (p < 0.01) greater than the result from the dilution technique, TBW estimated using the SF mode and prediction equation was significantly (p < 0.01) lower in boys. Measurement posture may not affect estimation of TBW in boys and adult males, however, body fluid shifts may still occur. In addition, technical factors, including selection of prediction equation, may be important when TBW is estimated from measured impedance.

Seismic hazard maps and spectrum for Patna considering region-specific seismotectonic parameters

The objective of this paper was to develop the seismic hazard maps of Patna district considering the region-specific maximum magnitude and ground motion prediction equation (GMPEs) by worst-case deterministic and classical probabilistic approaches. Patna, located near Himalayan active seismic region has been subjected to destructive earthquakes such as 1803 and 1934 Bihar-Nepal earthquakes. Based on the past seismicity and earthquake damage distribution, linear sources and seismic events have been considered at radius of about 500 km around Patna district center. Maximum magnitude (M (max)) has been estimated based on the conventional approaches such as maximum observed magnitude (M (max) (obs) ) and/or increment of 0.5, Kijko method and regional rupture characteristics. Maximum of these three is taken as maximum probable magnitude for each source. Twenty-seven ground motion prediction equations (GMPEs) are found applicable for Patna region. Of these, suitable region-specific GMPEs are selected by performing the `efficacy test,' which makes use of log-likelihood. Maximum magnitude and selected GMPEs are used to estimate PGA and spectral acceleration at 0.2 and 1 s and mapped for worst-case deterministic approach and 2 and 10 % period of exceedance in 50 years. Furthermore, seismic hazard results are used to develop the deaggregation plot to quantify the contribution of seismic sources in terms of magnitude and distance. In this study, normalized site-specific design spectrum has been developed by dividing the hazard map into four zones based on the peak ground acceleration values. This site-specific response spectrum has been compared with recent Sikkim 2011 earthquake and Indian seismic code IS1893.