Standard Error Of Estimate Research Articles

Quantitative estimation of muscle mass in older adults at risk of sarcopenia using ultrasound: a cross-sectional study.

Sarcopenia is an age-associated syndrome of decreased skeletal muscle function and loss of muscle mass. This cross-sectional study was designed to investigate whether ultrasound can be used to quantitatively estimate muscle mass in older adults as an efficient assistive method for the diagnosis of sarcopenia. A cohort of 103 older adults older aged over 60 years who were at risk of sarcopenia, including 57 males and 46 females, was recruited. The participants underwent ultrasound to measure the muscle thickness (MT) of 11 sites across the whole body. Bioelectrical impedance analysis (BIA) was then used to estimate the appendicular muscle mass, and the correlation between skeletal muscle mass index (SMI) and MT at different sites was studied. Finally, muscle mass estimation algorithms for older adults were developed using multiple linear regression. Male participants had a significantly higher SMI (7.03±0.73 vs. 5.84±0.72 kg/m2, P<0.001) and higher MT than female participants at all 11 sites (all P<0.05). The MT of Site 7 (rectus femoris and intermedius femoris) in males had the strongest correlation with SMI (R=0.719, P<0.001). In females, the MT at Site 3 (flexor pollicis longus, flexor digitorum superficialis, and brachioradialis) had the strongest correlation with SMI (R=0.733, P<0.001). The MT of Site 7 was selected for a one-site algorithm; the R2 and standard error of estimate (SEE) values were 0.701 and 0.519 kg/m2, respectively. The MT of Site 3, Site 7, Site 1 (biceps and brachialis), and Site 9 (tibialis anterior) were selected for a four-site algorithm; the R2 and SEE values were 0.819 and 0.404 kg/m2, respectively. MT measured using ultrasound is correlated with SMI at some sites, and the correlations differ between men and women. When sex and age were included in the algorithm, the MT at Site 1, Site 3, Site 7, and Site 9 were valuable for estimating SMI, with Site 7 being the best parameter among them. Ultrasound-derived algorithms can achieve a satisfying fitting effect and provide new solutions for muscle mass estimation in older adults.

Remote Sensing Image Analysis for Identification of Peat Thickness Using Spectral Transformation Approach: Case Study of Bengkalis Island, Riau, Indonesia

Peatland plays an important role in the global climate. Balancing economic, social and conservation needs on peatland utilization become an obligation in developing sustainable peatland regulation. To identify the appropriate land function in the peatland environment, the depth of peat is the main property to manage those balance needs. On the other hand, vast areas of peatland changing hinder rapid peat depth mapping method to have high accuracy. Multi-temporal remote sensing data were used to identify peatland-related land-use changes. The vegetation and wetness indices spectral transformations had been analyzed. The method used for the accuracy test in this study was correlation and regression analysis for modeling and the Standard Error of Estimate (SEE). The results of this study showed the vegetation indices and NDSI was not able to obtain peat thickness model due to the unstable vegetation and land cover changes. However, the NDWI was satisfied with the statistical assessment and was able to obtain the peat thickness with 41.96% accuracy. The determination of a sample design, the number and distribution of samples in preserved land covers, and missed variables and external factors in this study need to be considered in further research. The vegetation indices and wetness indices potentially can be the alternative variables to construct the peat depth map.

Relationship Between Organisational Factors and Adoption of New Technology in Kenya: A Case of CEMASTEA

This research evaluated the relationship between organisational factors and adoption of new technology in education support organisations in Kenya based on the case of Centre for Mathematics, Science and Technology Education in Africa (CEMASTEA). The research design adopted for this study was descriptive correlational design. The study population was 214 employees of CEMASTEA. A stratified sample of 64 participants was drawn from the different departments within the institution. A structured questionnaire was developed for data collection. Data was analysed using descriptive statistics and regression analysis in SPSS. An R-Square of 0.749 with the standard error of estimate being 0.235 was found, implying that for any variation in adoption of new technology by one-unit, organisational factors explained 74.9% of such variation. This was a demonstration that organisational factors were a salient determinant of technology adoption success. Government and all relevant authorities should also formulate and implement policies that will assist organisations adopt and adapt to new technologies in a favourable and user-friendly environment.

Assessment of Tree Vigour Models in University of Port Harcourt Park

Trees are the unsung heroes of the environment that requires care and maintenance so as to continue its provision of quantifiable benefits. Hence a better understanding of the physical aspect of tree mechanics would help to provide basis for the proper management of trees. The study assessed the vigour of selected trees in University of Port Harcourt Park. Data were collected from four selected tree species based on their abundance in the study area. Tree growth parameters were measured and tree vigour estimated using live crown ratio. Different regression functions were used to model tree vigour. The selected tree species used were Terminalia mantaly, Azadirachta indica, Mangifera indica and Terminalia cattapa with frequency of 214 (32.8%), 123 (18.89%), 82 (12.60%) and 79 (12.14%) relatively. Tree growth parameters such as height, Diameter at breast height, crown variable measured showed significant correlation with tree vigour (live crown ratio). The prediction models revealed plausible regression coefficient of determination (R2) value ranging from 0.247 - 0.953 and standard Error of Estimate (SEE) ranging from 0.0003 – 6.209. From the validation test, it was observed that all the models were good in prediction. However, the double log function was selected as the best model with R2 value of 0.953 and SEE value 0f 0.0003. Conclusively the trees within the University of Port Harcourt Park are stable hence are not prone to wind throw.

Aporte de agua del río “Tres ríos”, según el caudal recesivo, para los años hidrológicos 2008-2015, Cajamarca, Perú

The objective of the study was to quantify the contribution of water from the river "Tres Ríos" of the micro-basin of the same name. There was used two mathematical models: Maillet (1905) and Boussinesq (1904). The depletion curve was identified from the second break point, with hydrometric information since the period 2008 to 2015, it obtained in the Ronquillo diversion catchment to the same river. The simulated flows were contrasted with the observed flows of the “Tres Ríos” river, it used the following statistical indicators: Nash-Sutcliffe (0.71), Standard Error of Estimation (0.034), Modified Willmott Index (0.70) and a parametric statistical test (T Student). It was determined that the Maillet model (1904) it did not generate near depletion flows to the observed flows of the “Tres Ríos” river; however, the Boussinesq model (1904) generated underestimated depletion flows to the observed flows. With the Boussinesq model (1904), the average monthly depletion flows were generated and the contribution of water in the Ronquillo catchment of the micro-basin was estimated, it obtained an average monthly volume of 4.31 MMC.

Assessing complier average causal effects from longitudinal trials with multiple endpoints and treatment noncompliance: An application to a study of Arthritis Health Journal.

Treatment noncompliance often occurs in longitudinal randomized controlled trials (RCTs) on human subjects, and can greatly complicate treatment effect assessment. The complier average causal effect (CACE) informs the intervention efficacy for the subpopulation who would comply regardless of assigned treatment and has been considered as patient-oriented treatment effects of interest in the presence of noncompliance. Real-world RCTs evaluating multifaceted interventions often employ multiple study endpoints to measure treatment success. In such trials, limited sample sizes, low compliance rates, and small to moderate effect sizes on individual endpoints can significantly reduce the power to detect CACE when these correlated endpoints are analyzed separately. To overcome the challenge, we develop a multivariate longitudinal potential outcome model with stratification on latent compliance types to efficiently assess multivariate CACEs (MCACE) by combining information across multiple endpoints and visits. Evaluation using simulation data shows a significant increase in the estimation efficiency with the MCACE model, including up to 50% reduction in standard errors (SEs) of CACE estimates and 1-fold increase in the power to detect CACE. Finally, we apply the proposed MCACE model to an RCT on Arthritis Health Journal online tool. Results show that the MCACE analysis detects significant and beneficial intervention effects on two of the six endpoints while estimating CACEs for these endpoints separately fail to detect treatment effect on any endpoint.

Trophic State Assessment of a Freshwater Himalayan Lake Using Landsat 8 OLI Satellite Imagery: A Case Study of Wular Lake, Jammu and Kashmir (India)

AbstractA new version of Trophic State Index for freshwater Himalayan Lake (TSIFHL) has been derived from Landsat 8 OLI to determine the aquatic health of the lake ecosystem. TSIFHL is based on chlorophyll‐a concentration (CChl‐a) which has been retrieved from Landsat 8 OLI data and laboratory measurements using an empirical approach. Further, in situ measurements have also been taken with Secchi disk depth (ZSD) in a freshwater Himalayan Lake (FHL). The derived CChl‐a exhibited lower and upper limit of 25.81 and 207.96 μg/L, respectively. The modeled ZSD values ranged between 0.18 and 0.66 m with an average depth of 0.50 m. The best‐fitted regression model, developed for CChl‐a with R2 = 0.89, exhibited model error of 0.77 μg/L for the standard error of estimate (SEE). The mean absolute percentage error (MAPE), and Nash‐Sutcliffe coefficient (E) values were 5.83% and 0.98 μg/L, respectively. For the ZSD, the best‐fitted model showed errors of 0.11 μg/L (SEE), 13.93% (MAPE), and 0.77 μg/L (E) with R2 = 0.84. The proposed model is useful for bio‐optical studies of Himalayan Lake ecosystems and could provide potential support for inland water management. Long term strategies like multisector monitoring and control of nutrient load entering the lake can be developed to alleviate problems of eutrophication.

Abstract 015: Genome-wide Association Study Of Variant-by-thiazide Diuretic Interactions In The UK Biobank Identifies A Novel Locus For Thiazide-LDL-C Interaction

Introduction: Although thiazide diuretics are common therapies for the treatment of hypertension, evidence suggests that they are also associated with elevated blood lipid concentrations after their initiation. Pharmacogenomics studies could help identify potential biological pathways underlying this phenomenon, but few well-powered studies have been conducted. Methods: In participants of European ancestry from the UK Biobank, we conducted a genome-wide association study (GWAS) to examine whether common variants (minor allele frequency [MAF] &gt;1%) modified the effect of thiazide diuretic use on three lipid measures: low density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) concentrations (mmol/L). TG concentrations were natural log transformed, and LDL-C concentrations were adjusted for statin use. Given the conflicting evidence about the impact of loop diuretics on blood lipid concentrations, unexposed participants were defined as those taking neither thiazides nor loop diuretics. GWAS of UK10K plus 1000-Genomes Phase 3 imputed data were performed using SAIGE, adjusting for age, sex, fasting status, body mass index, study center, and the first 10 ancestral principal components. Results: Approximately 7.1% (n=27,971) of participants (n=395,058; mean age=57 years; 54% female [n=214,334]) used thiazides at study baseline. The thiazide-LDL-C GWAS (n=9,680,686 variants) identified one chromosome 19 locus harboring several genome-wide significant variants that modified the effect of thiazide diuretic use on LDL-C. The lead variant rs2199576 (MAF = 0.18) had an effect estimate (standard error) of 0.159 (0.013) among participants exposed to thiazides, and an effect estimate of 0.083 (0.003) among participants unexposed to any diuretic, yielding a significant variant-by-thiazide interaction effect estimate of 0.076 (0.013) ( p = 1.62 x 10 -9 ). These effect estimates are comparable to previously identified main effects of blood lipids loci. The rs2199576 variant resides near the PVRL2 locus, a cholesterol-responsive gene that is located within 100 kb of TOMM40 and the APO cluster, which have both been associated with blood lipid concentrations and Alzheimer’s disease. Conclusion: Our results may provide insights into pathways that influence the adverse effects of thiazide diuretic use and highlight the large sample sizes needed to detect modest pharmacogenetic effects.

Hydrogen sulfide emissions from a midwestern manure slurry storage basin.

Hydrogen sulfide (H2 S) emissions from midwestern U.S. dairy lagoons are episodic and seasonal. Emissions were determined using an inverse diffusion model in conjunction with measured upwind and downwind line-averaged H2 S concentrations and turbulence. Mean daily H2 S emissions from manure stored in earthen basins was 0.97μg m-2 s-1 (σ=1.35μg m-2 s-1 ). Mean live animal basis daily emission from the basins was 1.1g d-1 hd-1 (0.84g d-1 AU-1 ). Daily emission was modeled using the van't Hoff function with air temperature as a surrogate for slurry surface temperature and a linear function of friction velocity. The mean standard error of estimate of the model was 1.8μg m-2 s-1 (2.0g d-1 hd-1 , 1.6g d-1 AU-1 ) and accounted for 60% of emissions variability. H2 S emissions were enhanced for short periods during the year when the stored slurry was loaded onto trucks for removal. Emissions from the basins were not statistically different as barn manure handling changed from flushing to scraping. More measurements are needed to verify annual emissions estimates for these manure slurry storage basins and derive emission factors for these slurry storage systems.

Measuring body fat-How accurate is the extrapolation of predictive models in epidemiology?

Excess fat is a risk factor for many chronic diseases which can lead to premature mortality. Many studies have proposed predictive equations for body fat mass and body fat mass percentage based on anthropometric measures in relation to age and sex. However, the use of these predictive equations on other subject samples may not be relevant. Our objective is to assess whether the predictive equations proposed in the literature are generalizable to any population. We obtained fat mass and fat percentage on a reference population using Absorptiometry DXA. The predictive equations were applied to our population and the mean and individual differences between actual and estimated values were obtained. Predictive equations obtained from a reduced number of subjects have a very high Standard Error of Estimate (>3) and therefore their accuracy is not acceptable. Only the formulae established from a large number of individuals allow the estimation of values whose Standard Error of Estimate is less than 3. These equations, thanks to the large sample size, include a sufficiently large variability in anthropometric measurements covering the diversity of anthropometric values for the same fat value. However, predictive equations based on a large sample size, while exhibiting no current difference in variances, can show a shift in mean values. This mean-shift is the result of differences in DXA devices and needs to be corrected. It means that DXA values from a few individuals in the population under study must be obtained to calculate a corrective factor.

A Comparison of the Validity of Three Exercise Tests for Estimating Maximal Oxygen Uptake in Korean Adults Aged 19–64 Years

The purpose of this study was to compare the validity of three submaximal exercise tests (SMETs) and develop practical predictive models for the VO2max in Korean adults. A total of 541 (287 males and 254 females) adults participated in this study. Their ages ranged from 19 to 64. The VO2max was measured using the maximal-graded exercise treadmill test. The SMETs were performed by a treadmill test, the YMCA step test, and the PACER test. Regression analysis was conducted to compare the validity of the VO2max predictive equations using SMETs. The validity of the predictive models was evaluated using explanatory power, standard error of estimate (SEE), and Bland-Altman analysis. The explanatory power between the measured VO2max and the predicted VO2max was 58.0% (&lt;0.001), 59.2% (&lt;0.001), and 71.7% (&lt;0.001), respectively. The SEEs were 4.545, 4.478, and 3.732 (mL/kg/min). The models were significant predictors of VO2max and had acceptable validity in a large sample of Korean adults. Especially, among the predictive models, PACER had the highest acceptable effectiveness. Therefore, the equations developed in this study are recommended to better evaluate the cardiovascular endurance of Korean adults.

Multiple imputation for longitudinal data using Bayesian lasso imputation model.

Multiple imputation is a promising approach to handle missing data and is widely used in analysis of longitudinal clinical studies. A key consideration in the implementation of multiple imputation is to obtain accurate imputed values by specifying an imputation model that incorporates auxiliary variables potentially associated with missing variables. The use of informative auxiliary variables is known to be beneficial to make the missing at random assumption more plausible and help to reduce uncertainty of the imputations; however, it is not straightforward to pre-specify them in many cases. We propose a data-driven specification of the imputation model using Bayesian lasso in the context of longitudinal clinical study, and develop a built-in function of the Bayesian lasso imputation model which is performed within the framework of multiple imputation using chained equations. A simulation study suggested that the Bayesian lasso imputation model worked well in a variety of longitudinal study settings, providing unbiased treatment effect estimates with well-controlled type I error rates and coverage probabilities of the confidence interval; in contrast, ignorance of the informative auxiliary variables led to serious bias and inflation of type I error rate. Moreover, the Bayesian lasso imputation model offered higher statistical powers compared with conventional imputation methods. In our simulation study, the gains in statistical power were remarkable when the sample size was small relative to the number of auxiliary variables. An illustration through a real example also suggested that the Bayesian lasso imputation model could give smaller standard errors of the treatment effect estimate.

Pengaruh Kualitas Pelayanan terhadap Loyalitas Pelanggan Onelight Photography di Kabupaten Banyuwangi

Quality service can provide a sense of pleasure to customers that can have an impact on customer loyalty. This research was conducted to find out the effect of service quality on customer loyalty of Onelight Photography in Banyuwangi Regency. This research is quantitative research. Locciation research is determined by applying the purposive area method, namely in Onelight Photography, Jl. Ikan Mungsing no. 20 Banyuwangi. In determining the number of respondents using the Simple Random Sampling technique with the Slovin formula, there were 53 respondents. The techniques for collecting data used are kuesione,observation, and interviews. Datacollection uses editing, scoring, and tabulation. Inferential analysis uses data analysis that includes simple regression line analysis, regression variant analysis, F test, regression line effectiveness, and standard error of estimates. The acquisition of research showed that the service quality variable had a significant effect on customer loyalty of Onelight Photography in Banyuwangi Regency, namely Fhitung = 256,806 &gt; Ftabel = 4.03 with significance level F = 0.000 &lt;α = 0.05. For the variant value of the regression line which is 0.913 which belongs to a very strong category. The percentage of the percentage of service quality influence on Onelight Photography customer loyalty in Banyuwangi Regency amounted to 83.4%. While the rest is 16.6% dominated by other factors that are not discussed such as promotion, products and prices. This research can be concluded that there is a significant influence on the quality of service on customer loyalty of Onelight Photography in Banyuwangi Regency by 83.4%.

Pengaruh Kualitas Pelayanan terhadap Loyalitas Pelanggan Onelight Photography di Kabupaten Banyuwangi

Quality service can provide a sense of pleasure to customers that can have an impact on customer loyalty. This research was conducted to find out the effect of service quality on customer loyalty of Onelight Photography in Banyuwangi Regency. This research is quantitative research. Locciation research is determined by applying the purposive area method, namely in Onelight Photography, Jl. Ikan Mungsing no. 20 Banyuwangi. In determining the number of respondents using the Simple Random Sampling technique with the Slovin formula, there were 53 respondents. The techniques for collecting data used are kuesione,observation, and interviews. Datacollection uses editing, scoring, and tabulation. Inferential analysis uses data analysis that includes simple regression line analysis, regression variant analysis, F test, regression line effectiveness, and standard error of estimates. The acquisition of research showed that the service quality variable had a significant effect on customer loyalty of Onelight Photography in Banyuwangi Regency, namely Fhitung = 256,806 &gt; Ftabel = 4.03 with significance level F = 0.000 &lt;α = 0.05. For the variant value of the regression line which is 0.913 which belongs to a very strong category. The percentage of the percentage of service quality influence on Onelight Photography customer loyalty in Banyuwangi Regency amounted to 83.4%. While the rest is 16.6% dominated by other factors that are not discussed such as promotion, products and prices. This research can be concluded that there is a significant influence on the quality of service on customer loyalty of Onelight Photography in Banyuwangi Regency by 83.4%.

Development of an open-source measurement system to assess the areal bone mineral density of the proximal femur from clinical CT images.

For existing CT images acquired for various reasons other than osteoporosis, it might be beneficial to estimate areal BMD as assessed by dual-energy X-ray absorptiometry (DXA-based BMD) to ascertain the bone status based on DXA. In this study, we aimed to (1) develop an open-source reproducible measurement system to quantify DXA-based BMD from CT images and (2) validate its accuracy. This study analyzed 75 pairs of hip CT and DXA images of women that were acquired for the preoperative assessment of total hip arthroplasty. From the CT images, the femur and a calibration phantom were automatically segmented using pre-trained codes/models available at https://github.com/keisuke-uemura . The proximal femoral region was isolated by manually selected landmarks and was projected onto the coronal plane to measure the areal density (CT-aHU). The calibration phantom was employed to convert the CT-aHU into CT-aBMD. Each parameter was correlated with DXA-based BMD, and the residual errors of CT images to estimate the T-scores in DXA were calculated using the standard error of estimate (SEE). The correlation coefficients of DXA-based BMD with CT-aHU and CT-aBMD were 0.947 and 0.950, respectively (both p < 0.001). The SEE for quantifying the T-scores in DXA were 0.51 and 0.50 for CT-aHU and CT-aBMD, respectively. With the method developed herein, CT permits estimation of the DXA-based BMD of the proximal femur within the standard DXA total hip region of interest with an SEE of 0.5 in T-scores. The radiation dose for CT acquisition needs consideration; therefore, our data do not provide a rationale for performing CT for screening osteoporosis. However, on CT images already acquired for clinical indications other than osteoporosis, researchers may use this open-source system to investigate osteoporosis status through the estimated DXA-based BMD of the proximal femur.

Escitalopram administration, relearning, and neuroplastic effects: A diffusion tensor imaging study in healthy individuals

BackgroundNeuroplastic processes are influenced by serotonergic agents, which reportedly alter white matter microstructure in humans in conjunction with learning. The goal of this double-blind, placebo-controlled imaging study was to investigate the neuroplastic properties of escitalopram and cognitive training on white matter plasticity during (re)learning as a model for antidepressant treatment and environmental factors. MethodsSeventy-one healthy individuals (age=25.6 ± 5.0, 43 females) underwent three diffusion magnetic resonance imaging scans: at baseline, after 3 weeks of associative learning (emotional/non-emotional content), and after relearning shuffled associations for an additional 3 weeks. During the relearning phase, participants received a daily dose of 10 mg escitalopram or placebo orally. Fractional anisotropy (FA), and mean (MD), axial (AD), and radial diffusivity (RD) were calculated within the FMRIB software library and analyzed using tract-based spatial statistics. ResultsIn a three-way repeated-measures marginal model with sandwich estimator standard errors, we found no significant effects of escitalopram and content on AD, FA, MD, and RD during both learning and relearning periods (pFDR>0.05). When testing for escitalopram or content effects separately, we also demonstrated no significant findings (pFDR>0.05) for any of the diffusion tensor imaging metrics. LimitationsThe intensity of the study interventions might have been too brief to induce detectable white matter changes. DiscussionPrevious studies examining the effects of SSRIs on white matter tracts in humans have yielded inconclusive outcomes. Our results indicate that relearning under escitalopram does not affect the white matter microstructures in healthy individuals when administered for 3 weeks.

The Determination of Step Frequency in 3-min Incremental Step-in-Place Tests for Predicting Maximal Oxygen Uptake from Heart Rate Response in Taiwanese Adults.

The maximal oxygen uptake (VO2max) prediction models established by step tests are often used for evaluating cardiorespiratory fitness (CRF). However, it is unclear which type of stepping frequency sequence is more suitable for the public to assess the CRF. Therefore, the main purpose of this study was to test the effectiveness of two 3-min incremental step-in-place (3MISP) tests (i.e., 3MISP30s and 3MISP60s) with the same total number of steps but different step-frequency sequences in predicting VO2max. In this cross-sectional study, a total of 200 healthy adults in Taiwan completed 3MISP30s and 3MISP60s tests, as well as cardiopulmonary exercise testing. The 3MISP30s and 3MISP60s models were established through multiple stepwise regression analysis by gender, age, percent body fat, and 3MISP-heart rate. The statistical analysis included Pearson’s correlations, the standard errors of estimate, the predicted residual error sum of squares, and the Bland–Altman plot to compare the measured VO2max values and those estimated. The results of the study showed that the exercise intensity of the 3MISP30s test was higher than that of the 3MISP60s test (% heart rate reserve (HRR) during 3MISP30s vs. %HRR during 3MISP60s = 81.00% vs. 76.81%, p < 0.001). Both the 3MISP30s model and the 3MISP60s model explained 64.4% of VO2max, and the standard errors of the estimates were 4.2043 and 4.2090 mL·kg−1·min−1, respectively. The cross-validation results also indicated that the measured VO2max values and those predicted by the 3MISP30s and 3MISP60s models were highly correlated (3MISP30s model: r = 0.804, 3MISP60s model: r = 0.807, both p < 0.001). There was no significant difference between the measured VO2max values and those predicted by the 3MISP30s and 3MISP60s models in the testing group (p > 0.05). The results of the study showed that when the 3MISP60s test was used, the exercise intensity was significantly reduced, but the predictive effectiveness of VO2max did not change. We concluded that the 3MISP60s test was physiologically less stressful than the 3MISP30s test, and it could be a better choice for CRF evaluation.

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.

Estimation of stature from length of sternum in adult females of North East Madhya Pradesh - An autopsy based study

Determination of stature is an important step to complete the biological profile of an individual to establish identity particularly in partially skeletonized, dismembered, mutilated, and highly decomposed bodies. The present descriptive observational study was conducted on 50 female cadavers of age more than 25 years brought for medicolegal autopsy in this tertiary care center located in north east Madhya Pradesh. The aim of the present study was to find out a correlation between the lengths of different parts of fresh sternums and stature and derive a simple linear regression equation for stature estimation from the length of the sternum. Simple linear regression formulae were derived by correlating stature with the length of manubrium, length of mesosternum, and the combined length of manubrium and mesosternum. We observed a moderately positive correlation between stature and length of manubrium (R=0.167) and the combined length of manubrium and mesosternum (R=0.207). Amongst all parameters taken into consideration in the present study, stature can be most reliably predicted by the regression equation which is derived from the correlation between stature and the combined length of the manubrium and mesosternum [standard error of estimate (SEE) - 5.85]. Due to lesser values of correlation coefficient and higher SEE, lengths of different parts of the sternum are less reliable in predicting stature than lengths of long bones, hand length, and foot length. The findings of the present study will be useful for rapid estimation of stature in relatively fresh but mutilated bodies or badly decomposed bodies only when long bones are not available or are partially broken and fragmented.

Associations between estimated and measured carotid-femoral pulse wave velocity in older Black and White adults: the atherosclerosis risk in communities (ARIC) study.

Aortic stiffness offers important insight into vascular aging and cardiovascular disease (CVD) risk. The referent measure of aortic stiffness is carotid-femoral pulse wave velocity (cfPWV). cfPWV can be estimated (ePWV) from age and mean arterial pressure. Few studies have directly compared the association of ePWV to measured cfPWV, particularly in non-White adults. Moreover, whether ePWV and cfPWV correlate similarly with CVD risk remains unexplored. (1) To estimate the strength of the agreement between ePWV and cfPWV in both Black and White older adults; and (2) to compare the associations of ePWV and cfPWV with CVD risk factors and determine whether these associations were consistent across races. We evaluated 4478 [75.2 (SD 5.0) years] Black and White older adults in the Atherosclerosis Risk in Communities (ARIC) Study. cfPWV was measured using an automated pulse waveform analyzer. ePWV was derived from an equation based on age and mean arterial pressure. Association and agreement between the two measurements were determined using Pearson's correlation coefficient (r), standard error of estimate (SEE), and Bland-Altman analysis. Associations between traditional risk factors with ePWV and cfPWV were evaluated using linear mixed regression models. We observed weak correlations between ePWV and cfPWV within White adults (r = 0.36) and Black adults (r = 0.31). The mean bias for Bland-Altman analysis was low at -0.17 m/s (95%CI: -0.25 to -0.09). However, the inspection of the Bland-Altman plots indicated systematic bias (P < 0.001), which was consistent across race strata. The SEE, or typical absolute error, was 2.8 m/s suggesting high variability across measures. In models adjusted for sex, prevalent diabetes, the number of prevalent cardiovascular diseases, and medication count, both cfPWV and ePWV were positively associated with heart rate, triglycerides, and fasting glucose, and negatively associated with body mass index (BMI) and smoking status in White adults (P < 0.05). cfPWV and ePWV were not associated with heart rate, triglycerides, and fasting glucose in Black adults, while both measures were negatively associated with BMI in Black adults. Findings suggest a weak association between ePWV and cfPWV in older White and Black adults from ARIC. There were similar weak associations between CVD risk factors with ePWV and cfPWV in White adults with subtle differences in associations in Black adults.