Rate Prediction Equations Research Articles

Evaluation of astaxanthin stability under varying temperatures and ultraviolet irradiation durations based on Raman spectroscopy

As a potent naturally carotenoid, Astaxanthin (AST) is commonly used as a natural coloring agent and antioxidant in food products, and it's stability is of great interest. The stability of AST solution stored in glass bottle under different temperatures and ultraviolet (UV) irradiation durations was analyzed in situ using confocal Raman spectroscopy, and the acceptable depth of focus was optimized. Raman spectra of AST geometrical isomers were determined by density functional theory (DFT) simulation, and characteristic peaks were selected for studying AST degradation and isomerization. Raman spectra and peak-fitting spectra based on gaussian multi-peak fitting analysis combined with Pearson's correlation analysis were conducted to study the effect of temperatures and UV irradiation on AST degradation and isomerization. The peak intensity ratio of I1518/I880 had been selected as the optimal Raman spectral variable for AST degradation based on Pearson's correlation analysis. Finally, degradation kinetic curves and degradation rate prediction equation were established. The results indicated that the isomerization of 9,13-di-cis isomer occurred at a UV irradiation of 288 h. Moreover, high temperatures above 60 °C and prolonged UV exposure exceeds 48 h can cause significant degradation of AST, with a degradation rate above 20 %. This study provided an in-situ, nondestructive potential method for the calculation of AST degradation under different temperatures and UV irradiation durations, which contribute guiding insights into the development and utilization of AST in food industry.

Maximum heart rate prediction equations based on field-tests with adequate clinical applicability for aerobic exercise prescription to individuals after stroke

Objectives 1) to develop two maximum heart rate (HRmax) prediction equations for individuals after stroke; 2) to investigate its validity. Methods The HRmax was obtained by a Cardiopulmonary Exercise Test (CPET). Participants also completed the Six-minute Walking Test (6MWT) and the Incremental Shuttle Walking Test (ISWT). Results 60 individuals (54(12) years; 64(69) months after stroke) were included. Twenty individuals (58(10) years; 67(61) months after stroke) were included in the cross-validation group. For the first model, the following equation was generated (equation-1): HRmax= 87.655 + 0.726 (HRpeak in the 6MWT) − 0.386 (age in years), (R2=0.53; Standard Error of the Estimate (SEE)= 15.35; p < 0.0001). For the second model, the following equation was generated (equation-2): HRmax= 96.523 + 0.681 (HRpeak in the ISWT) − 0.039 (walking distance in meters in the ISWT) − 0.400 (age in years), (R2=0.53; SEE = 15.51; p < 0.0001). Significant and high magnitude agreement was found between the HRmax obtained by the CPET and the predicted HRmax by equation-1 (ICC= 0.85; 95% CI= 0.63 − 0.94) and equation-2 (ICC= 0.72; 95% CI= 0.29 − 0.89). Conclusions Two HRmax prediction equations have been developed and showed adequate validity. Professionals will have the option of choosing one of the two equations to use.

Resting energy expenditure during spinal cord injury rehabilitation and utility of fat-free mass-based energy prediction equations: a pilot study

Study designLongitudinal observational study. Measurements were undertaken between weeks 4–6 post-spinal cord injury (SCI), repeated at week 8 and every 4 weeks thereafter until week 20 or rehabilitation discharge, whichever occurred first.ObjectivesObserve variation in measured resting energy expenditure (REE) and body composition in males undergoing SCI rehabilitation, compare REE with SCI-specific prediction equations incorporating fat-free mass (FFM), and explore the prevalence of clinical factors that may influence individual REE.SettingSpinal Injuries Unit, Brisbane, Queensland, Australia.MethodsIndirect calorimetry was used to measure REE and bioimpedance spectroscopy to assess body composition. Four SCI-specific FFM-based REE and basal metabolic rate (BMR) prediction equations were compared to measured REE. A clinically significant change in REE was defined as +/− 10% difference from the week 4–6 measurement. Clinical factors that may affect REE variations were collected including infection, pressure injuries, autonomic dysreflexia, spasticity, and medications.ResultsFifteen people participated (mean age 35 ± 13 years, 67% paraplegic). There was no statistically significant change in mean REE, weight, or body composition, and the Chun and Nightingale BMR prediction equations performed best (rc > 0.8 at all time points). One-third of participants had >10% change in REE on 11 occasions, with clinical factors not consistently associated with the observed changes.ConclusionDuring SCI rehabilitation, mean REE, weight, and body composition remain unchanged, and FFM-based BMR prediction equations may be an acceptable alternative to indirect calorimetry. Future research designs should avoid single indirect calorimetry measures as snapshot data may not represent typical REE in this population.

Whole body sweat rate prediction: indoor treadmill and cycle ergometer exercise.

This article describes the development and validation of accurate whole body sweat rate prediction equations for individuals performing indoor cycle ergometer and treadmill exercise, where power output can be measured or derived from simple inputs. For cycle ergometry, 112 trials (67 participants) were used for model development and another 56 trials (42 participants) for model validation. For treadmill exercise, 171 trials (67 participants) were used for model development and another 95 trials (63 participants) for model validation. Trials were conducted over a range of dry-bulb temperature (20°C to 40°C), relative humidity (14% to 60%), and exercise intensity (∼40% to 85% of peak aerobic power) conditions, which were matched between model development and model validation. Whole body sweat rates were measured, and proprietary prediction models were developed (accounting for all relevant biophysical factors) and then validated. For model validation, mean absolute error for predicted sweating rate was 0.01 and 0.02 L·h-1 for cycle and treadmill trials, respectively. The 95% confidence intervals were modest for cycle ergometer (+0.25 and -0.22 L·h-1) and treadmill exercise (+0.33 and -0.29 L·h-1). The accounted for variance between predicted and measured values was 92% and 78% for cycle and treadmill exercise, respectively. Bland-Altman analysis indicated that zero and one predicted value exceeded the a priori acceptable level of agreement (equivalent to ±2% of total body mass in 3 h) for cycle and treadmill exercise, respectively. There were fewer trials with female subjects, but their values did not differ from those expected for males. This is the foremost study to develop and validate whole body sweat rate prediction equations for indoor treadmill and cycle ergometer exercise of moderate to high intensity. These prediction equations are publicly available for use (https://sweatratecalculator.com).NEW & NOTEWORTHY This study presents the development of new proprietary whole body sweat rate prediction models for people exercising indoors on a cycle ergometer or treadmill using simple input parameters and delivered through a publicly available online calculator: https://sweatratecalculator.com. In an independent validation group, the predictive models for both indoor cycling and treadmill exercise were accurate across moderate to high exercise intensities in temperate to hot conditions. These equations will enable individualized hydration management during physical training and exercise physiology experiments.

Whole body sweat rate prediction: outdoor running and cycling exercise.

Our aim was to develop and validate separate whole body sweat rate prediction equations for moderate to high-intensity outdoor cycling and running, using simple measured or estimated activity and environmental inputs. Across two collection sites in Australia, 182 outdoor running trials and 158 outdoor cycling trials were completed at a wet-bulb globe temperature ranging from ∼15°C to ∼29°C, with ∼60-min whole body sweat rates measured in each trial. Data were randomly separated into model development (running: 120; cycling: 100 trials) and validation groups (running: 62; cycling: 58 trials), enabling proprietary prediction models to be developed and then validated. Running and cycling models were also developed and tested when locally measured environmental conditions were substituted with participants' subjective ratings for black globe temperature, wind speed, and humidity. The mean absolute error for predicted sweating rate was 0.03 and 0.02 L·h-1 for running and cycling models, respectively. The 95% confidence intervals for running (+0.44 and -0.38 L·h-1) and cycling (+0.45 and -0.42 L·h-1) were within acceptable limits for an equivalent change in total body mass over 3 h of ±2%. The individual variance in observed sweating described by the predictive models was 77% and 60% for running and cycling, respectively. Substituting measured environmental variables with subjective assessments of climatic characteristics reduced the variation in observed sweating described by the running model by up to ∼25%, but only by ∼2% for the cycling model. These prediction models are publicly accessible (https://sweatratecalculator.com) and can guide individualized hydration management in advance of outdoor running and cycling.NEW & NOTEWORTHY We report the development and validation of new proprietary whole body sweat rate prediction models for outdoor running and outdoor cycling using simple activity and environmental inputs. Separate sweat rate models were also developed and tested for situations where all four environmental parameters are not available, and some must be subsequently estimated by the user via a simple rating scale. All models are freely accessible through an online calculator: https://sweatratecalculator.com. These models, via the online calculator, will enable individualized hydration management for training or recreational cycling or running in an outdoor environment.

Completely Plant-Based Diets That Meet Energy Requirements for Resistance Training Can Supply Enough Protein and Leucine to Maximize Hypertrophy and Strength in Male Bodybuilders: A Modeling Study.

Despite increasing awareness of plant-based diets for health and athletic performance, athletes are cautioned that careful dietary monitoring is necessary. Whether commonly consumed plant-based diets are nutritionally adequate for maximal muscular hypertrophy remains unknown. This modeling study assessed the nutrient composition of completely plant-based diets scaled to the caloric demands of maximal muscle mass and strength development in adult male bodybuilders. To model calorie requirements, anthropometric data from bodybuilders were input into the Tinsley resting metabolic rate prediction equation, and an appropriate physical activity factor and calorie surplus were applied. Dietary data from a large cohort following completely plant-based diets were then scaled to meet these needs. Modeled intakes for nutrients of interest were calculated as 1.8 g/kg/day of protein and 2.75 g/meal of leucine, which surpass mean requirements for maximal increases in muscle mass and strength and muscle protein synthesis, respectively. Daily levels for all micronutrients, except vitamin D, also exceeded requirements. Saturated fat levels were aligned with dietary guidelines, although sodium levels exceeded recommended limits. Consumption of larger portions of commonplace plant-based diets, scaled to meet the energy demands of maximal accrual of muscle mass and strength, satisfied protein and leucine requirements without the need for additional planning.

The Development of a Resting Metabolic Rate Prediction Equation for Professional Male Rugby Union Players.

Determining resting metabolic rate (RMR) is an important aspect when calculating energy requirements for professional rugby union players. Prediction equations are often used for convenience to estimate RMR. However, the accuracy of current prediction equations for professional rugby union players remains unclear. The aims of this study were to examine the RMR of professional male rugby union players compared to nine commonly used prediction equations and develop and validate RMR prediction equations specific to professional male rugby union players. One hundred and eight players (body mass (BM) = 102.9 ± 13.3 kg; fat-free mass (FFM) = 84.8 ± 10.2 kg) undertook Dual-energy X-ray Absorptiometry scans to assess body composition and indirect calorimetry to determine RMR. Mean RMR values of 2585 ± 176 kcal∙day-1 were observed among the group with forwards (2706 ± 94 kcal·day-1), demonstrating significantly (p < 0.01; d = 1.93) higher RMR compared to backs (2465 ± 156 kcal·day-1), which appeared to be due to their higher BM and FFM measures. Compared to the measured RMR for the group, seven of the nine commonly used prediction equations significantly (p < 0.05) under-estimated RMR (-104-346 kcal·day-1), and one equation significantly (p < 0.01) over-estimated RMR (192 kcal·day-1). This led to the development of a new prediction equation using stepwise linear regression, which determined that the strongest predictor of RMR for this group was FFM alone (R2 = 0.70; SEE = 96.65), followed by BM alone (R2 = 0.65; SEE = 104.97). Measuring RMR within a group of professional male rugby union players is important, as current prediction equations may under- or over-estimate RMR. If direct measures of RMR cannot be obtained, we propose the newly developed prediction equations be used to estimate RMR within professional male rugby union players. Otherwise, developing team- and/or group-specific prediction equations is encouraged.

Meta-analysis of age-based maximum heart rate prediction equations: validating existing models across diverse populations

Meta-analysis of age-based maximum heart rate prediction equations: validating existing models across diverse populations

Accuracy of Resting Metabolic Rate Prediction Equations in Sport Climbers.

Resting metabolic rate (RMR) represents the energy required to maintain vital body functions. In dietary practice, RMR is determined by predictive equations on the basis of using body weight or fat-free mass. Our study aimed to assess whether predictive equations used to estimate RMR are reliable tools for estimating the energy requirements of sport climbers. The study included 114 sport climbers whose RMR was measured with a Fitmate WM. Anthropometric measurements were performed with X-CONTACT 356. The resting metabolic rate was measured by indirect calorimetry and was compared with the RMR estimated by 14 predictive equations on the basis of using body weight/fat-free mass. All equations underestimated RMR in male and female climbers, except for De Lorenzo's equation in the group of women. The De Lorenzo equation demonstrated the highest correlation with RMR in both groups. The results of the Bland-Altman tests revealed an increasing measurement error with increasing metabolism for most of the predictive equations in male and female climbers. All equations had low measurement reliability according to the intraclass correlation coefficient. Compared with the indirect calorimetry measurement results, none of the studied predictive equations demonstrated high reliability. There is a need to develop a highly reliable predictive equation to estimate RMR in sport climbers.

Using Raw Accelerometer Data to Predict High-Impact Mechanical Loading.

The purpose of this study was to develop peak ground reaction force (pGRF) and peak loading rate (pLR) prediction equations for high-impact activities in adult subjects with a broad range of body masses, from normal weight to severe obesity. A total of 78 participants (27 males; 82.4 ± 20.6 kg) completed a series of trials involving jumps of different types and heights on force plates while wearing accelerometers at the ankle, lower back, and hip. Regression equations were developed to predict pGRF and pLR from accelerometry data. Leave-one-out cross-validation was used to calculate prediction accuracy and Bland-Altman plots. Body mass was a predictor in all models, along with peak acceleration in the pGRF models and peak acceleration rate in the pLR models. The equations to predict pGRF had a coefficient of determination (R2) of at least 0.83, and a mean absolute percentage error (MAPE) below 14.5%, while the R2 for the pLR prediction equations was at least 0.87 and the highest MAPE was 24.7%. Jumping pGRF can be accurately predicted through accelerometry data, enabling the continuous assessment of mechanical loading in clinical settings. The pLR prediction equations yielded a lower accuracy when compared to the pGRF equations.

Cross-Validation of a New General Population Resting Metabolic Rate Prediction Equation Based on Body Composition

Current prediction equations for resting metabolic rate (RMR) were validated in a relatively small sample with high-individual variance. This study determined the accuracy of five common RMR equations and proposed a novel prediction equation, including body composition. A total of 3001 participants (41 ± 13 years; BMI 28.5 ± 5.5 kg/m2; 48% males) from nutrition clinics in Israel were measured by indirect calorimetry to assess RMR. Dual-energy X-ray absorptiometry were used to evaluate fat mass (FM) and free-fat mass (FFM). Accuracy and mean bias were compared between the measured RMR and the prediction equations. A random training set (75%, n = 2251) and a validation set (25%, n = 750) were used to develop a new prediction model. All the prediction equations underestimated RMR. The Cunningham equation obtained the largest mean deviation [-16.6%; 95% level of agreement (LOA) 1.9, -35.1], followed by the Owen (-15.4%; 95% LOA 4.2, -22.6), Mifflin-St. Jeor (-12.6; 95% LOA 5.8, -26.5), Harris-Benedict (-8.2; 95% LOA 11.1, -27.7), and the WHO/FAO/UAU (-2.1; 95% LOA 22.3, -26.5) equations. Our new proposed model includes sex, age, FM, and FFM and successfully predicted 73.5% of the explained variation, with a bias of 0.7% (95% LOA -18.6, 19.7). This study demonstrates a large discrepancy between the common prediction equations and measured RMR and suggests a new accurate equation that includes both FM and FFM.

The predictive equations of resting metabolic rate for Tibetan adolescents aged 13-18 in Tibet, China.

The resting metabolic rate (RMR) predictive equations suitable for Tibetan adolescents in Tibet, China, were developed to provide a reference for their reasonable energy intake. We measured RMR by indirect calorimetry and body composition by bioelectrical impedance analysis in 325 Tibetan adolescents aged 13-18 years in Tibet, China. Stepwise regression analysis was used to develop the predictive equations. Pearson correlation analysis, paired sample t test, bias rate, concordance correlation coefficient (CCC) and Bland-Altman were used to verify the validity of the predictive equations. The R2 of Model 8 (0.642) was larger than Model 2 (boys, 0.642; girls, 0.533) and Model 7 (0.540), and Model 10 (0.534) was larger than Model 4 (boys, 0.531; girls, 0.443) and Model 9 (0.477).Compared with the existing predictive equations, the correlation (0.68-0.84) between the predicted values and the measured values, CCC (0.74-0.81) and consistency (Similar proportions within the upper and lower limits but lower differences) were higher and the bias rate (-1.0%to -2.5%) and root mean square error (207.4-263.7kcal/day) were lower in this study. By comprehensive comparison, Model 8 and Model 10 were more valid. The existing predictive equations cannot accurately predict the RMR of Tibetan adolescents in Tibet, China. In this study, the age segmentation predictive equations with age, sex, and fat free mass (FFM) as independent variables were more valid. The predictive equations were as follows: RMR (kcal/day)=50.1 × FFM (kg)-202.8 × Sex (F: 0; M: 1)-72.1 × Age + 930.3, 13-15 years; RMR (kcal/day)=58.4 × FFM (kg)-441.1 × Sex (F: 0; M: 1)-702.2, 16-18 years.

The accuracy of ten common resting metabolic rate prediction equations in men and women collegiate athletes

ABSTRACT Predictive resting metabolic rate (RMR) equations are widely used to determine total daily energy expenditure (TDEE). However, it remains unclear whether these predictive RMR equations accurately predict TDEE in the athletic populations. The purpose of this study was to examine the accuracy of 10 commonly used RMR prediction equations (Cunningham, De Lorenzo, Freire, Harris-Benedict, Mifflin St. Jeor, Nelson, Owen, Tinsley, Watson, Schofield) in collegiate men and women athletes. One-hundred eighty-seven National Collegiate Athletic Association Division III men (n = 97) and women (n = 90) athletes were recruited to participate in one day of metabolic testing. RMR was measured using indirect calorimetry and body composition was analyzed using air displacement plethysmography. A repeated measures ANOVA with Bonferroni post hoc analyses was selected to determine mean differences between measured and predicted RMR. Linear regression analysis was used to assess the accuracy of each RMR prediction method (p<0.05). All prediction equations significantly underestimated RMR (p<0.001), although there was no difference between the De Lorenzo and Watson equations and measured RMR (p = 1.00) for women, only. In men, the Tinsley and Freire equations were the most agreeable formulas with the lowest root-mean-square prediction error value of 404 and 412 kcals, respectively. In women, the De Lorenzo and Watson equations were the most agreeable equations with the lowest root-mean-squared error value of 171 and 211 kcals, respectively. The results demonstrate that such RMR equations may underestimate actual energy requirements of athletes and thus, practitioners should interpret such values with caution. Highlights All prediction equations significantly underestimated RMR in men athletes. All prediction equations, except for the De Lorenzo and Watson equations, significantly underestimated RMR in women athletes. Although a significant underestimation of RMR in men athletes, the Freire and Tinsley equations were the most agreeable prediction equations. In women athletes, the De Lorenzo and Watson equations were the most agreeable prediction equations.

Metabolic equivalent of task and the accuracy of resting metabolic rate prediction equations in inactive, healthy postmenopausal women with overweight and obesity

The aim of this study is to compare the traditionally accepted oxygen consumption value (3.5 mL.kg −1 .min −1 ) for 1 metabolic equivalent of task with measured resting oxygen consumption in postmenopausal women with overweight and obesity. The second purpose is to determine the accuracy of selected resting metabolic rate prediction equations. This study includes the baseline resting metabolic rate data collected for a more comprehensive exercise intervention study in postmenopausal women with overweight and obesity. Twenty-eight postmenopausal women with overweight and obesity (age: 55.5 ± 4.0 years, body mass index: 33.0 ± 4.2 kg.m −2 ) participated in this study. Resting metabolic rate was measured by indirect calorimetry and predicted by selected six equations. One-Sample t-test, Paired-Sample t-test, Intraclass Correlation Coefficient, Information Based Measure of Disagreement and Bland-Altman plots were used for data analysis. Resting oxygen consumption (1.96 mL.kg −1 .min −1 ) was 44% lower than the traditionally accepted oxygen consumption value for 1 metabolic equivalent of task ( P < 0.05). Measured resting metabolic rate was 1113 ± 255 kcal.day −1 . All prediction equations, Mifflin Weight (1373 ± 106 kcal.day −1 ), Mifflin FatFreeMass (1281 ± 83 kcal.day −1 ), Harris-Benedict (1481 ± 95 kcal.day −1 ), World Health Organization (1540 ± 90 kcal.day −1 ), Bernstein (1225 ± 73 kcal.day −1 ) and Owen FatFreeMass (1202 ± 83 kcal.day −1 ) significantly overestimated the resting metabolic rate ( P < 0.05). Resting oxygen consumption in postmenopausal women with overweight and obesity is lower than the traditionally accepted value of 1 metabolic equivalent of task. Therefore, the use of standard metabolic equivalent of task may overestimate the resting metabolic rate and lead to misclassification of physical activity intensities in this population. The accuracy of the prediction equations is very low, and they overestimate resting metabolic rate in postmenopausal women with overweight and obesity, which may interfere with the weight management programmes. Whenever possible indirect calorimetry should be used to determine resting metabolic rate in this population. Le but de cette étude est de comparer la consommation d’oxygène au repos mesurée chez les femmes ménopausées en surpoids et obèses avec la valeur de consommation d’oxygène conventionnellement acceptée (3,5 mL.kg −1 min −1 ) pour 1 équivalent métabolique. Le deuxième objectif de cette étude est de déterminer l’exactitude des certaines formules d’estimation du taux métabolique de base. Cette étude comprend les données de base sur le taux métabolique au repos collectées pour l’intervention d’exercice plus complète dans une étude chez les femmes post-ménopausées en surpoids et obèses. Vingt-huit femmes post-ménopausées en surpoids et obèses (âge : 55,5 ± 4,0 ans, indice de masse corporelle : 33,0 ± 4,2 kg.m −2 ) ont participé à cette étude. Le taux métabolique de base a été mesuré par calorimétrie indirecte et a été calculé par six équations sélectionnées. Le test t à un échantillon, le test t à échantillon apparié, coefficient de corrélation intraclasse, mesure du désaccord basée sur l’information et les graphiques de Bland-Altman ont été utilisés pour l’analyse des données. La valeur de la consommation d’oxygène au repos (1,96 ml.kg −1 .min −1 ) est 44 % inférieure à la valeur de consommation d’oxygène conventionnellement acceptée pour 1 équivalent métabolique ( p < 0,05). Le taux métabolique de base mesuré par calorimétrie indirecte a été déterminé à 1113 ± 255 kcal.jour −1 . Les taux métaboliques de base calculés par toutes les formules de prédiction, Mifflin Weight (1373 ± 106 kcal.day −1 ), Mifflin FatFreeMass (1281 ± 83 kcal.day −1 ), Harris-Benedict (1481 ± 95 kcal.day −1 ), Organisation Mondiale de la Santé (1540 ± 90 kcal.day −1 ), Bernstein (1225 ± 73 kcal.day −1 ) et Owen FatFreeMass (1202 ± 83 kcal.day −1 ) sont visiblement plus élevés que le taux métabolique de base mesuré dans cette étude ( p < 0,05). Chez les femmes post-ménopausées en surpoids et obèses, la consommation d’oxygène au repos est inférieure à l’équivalent métabolique conventionnellement accepté de 1. En conséquence, par l’utilisation d’équivalents métaboliques standards on peut surestimer le taux métabolique de base et conduire à une classification erronée de l’intensité de l’activité physique dans cette population. La précision des formules d’estimation est très faible et le taux métabolique de base surestimés, qui peut interagir avec les programmes de gestion du poids chez les femmes post-ménopausées en surpoids et obèses. Dans cette population, la calorimétrie indirecte devrait être utilisée pour déterminer le taux métabolique de base dans la mesure du possible.

Analysis of graphite nozzle throat erosion in liquid rockets using ethanol/LOX

Interstellar Technologies, Inc. has been developing a series of liquid rockets using ethanol/LOX called MOMO. These rockets use graphite nozzle throat inserts for which nozzle erosion, referring to the increase in diameter of the nozzle throat due to the chemical reactions between the combustion gas and the graphite, has become a problem. This study aims to develop a predictive equation for the nozzle erosion rate of liquid rockets using ethanol/LOX. The authors analyzed data from seven long-duration combustion experiments and calculated the nozzle erosion rate. Using the heat transfer coefficient of the nozzle throat, the heat transfer equation at the nozzle throat was solved and the nozzle wall temperature was also estimated. As a result, it was found that the nozzle erosion phenomenon of liquid rockets using ethanol/LOX has a similar trend to that of nozzle erosion in hybrid rockets using HDPE/LOX. A good correlation was obtained by using the nozzle erosion rate prediction equation of Kamps et al., which has chamber pressure, equivalence ratio and nozzle wall temperature as parameters.

Experimental study on the thermal comfort and physiological responses of the elderly in unstable environments

In this study, the investigators used comparative experiments in order to study the age differences and the effects of temperature changes on physiological responses. Firstly, This paper analyzed the thermal sensation differences and the trend of changes in the design working conditions in different age groups. The researchers found that the thermal sensation of elderly people was lower than that of young people in the hot environment and higher than that of young people in the cold environment. On this basis, the heart rate changes of the elderly in the dynamic environment and the differences with the young people were studied. The results showed that the heart rate of the elderly and the young people increased with the increase of temperature in the temperature rise condition, but there was no significant difference between the two groups. In the temperature drop condition, the heart rate decreased with the decrease of temperature. Finally, an equation was developed to quantitatively predict heart rate parameters. The equation predicts human heart rate by ambient temperature, and the results show that the least square error of the prediction equation for elderly people is 46, while the average difference between the actual and predicted values is 1.2. Therefore, it is recommended to use the proposed heart rate prediction equation when predicting the healthiness of heart rate in elderly people.

Improving the Minimum Design Velocity of Sewage Pipes

The minimum design velocity for sewage pipes is set based on the planned quantity; however, deriving a reasonable design with only a single standard is limited. Thus, it is necessary to review whether consistent standards, such as the current minimum flow velocity, can efficiently transport the particles flowing in pipes. Improving the design that can determine the minimum design velocity rate and slope according to the concentration, specific gravity, particle size, and characteristics of the pipe material of the sediment flowing into the sewer system is necessary. In this study, the minimum design velocity for each particle size was examined based on the results of the simulation of fluid-solid multiphase flow required for the review process of hydraulic properties inside the pipe. The minimum design velocity standards for domestic and international sewage systems were investigated and compared, and a minimum design flow rate prediction equation was proposed considering the sedimentation. The equation includes the relationship between the particle size and pipe diameter and can be easily estimated.

Basal metabolic rate for high-performance female karate athletes.

Introduction: karate is a millennial martial art, currently inserted in the context of Olympic Combat Sports. However, important scientific gaps still persist in monitoring high-performance athletes, including the basal metabolism measurement of female karate athletes. Aim: to contribute to understanding the applicability of equations for predicting basal metabolic rate in this population. Methods: this is a cross-sectional study with a retro-analytical component, in which data were obtained from the medical records of seven athletes participating in the project "Karate São Paulo Olímpico" (São Paulo Olympic Karate) (KSPO) during their nutrition counseling, including body composition and indirect calorimetry testing, with the aim of comparing these data to basal metabolic rate prediction equations. Results: only one out of the five evaluated equations did not have a significant statistical difference relative to the value obtained by open-circuit indirect calorimetry. Conclusion: in case basal metabolism cannot be measured through standard methodology (calorimetry), Cunningham's prediction equation (1980) would be appropriate to obtain total energy expenditure for high-performance female karate athletes.

Accuracy of resting metabolic rate prediction equations among healthy adults in Trinidad and Tobago.

Over 50% of adults in Latin America and the Caribbean have a body mass index (BMI) ≥ 25 suggesting excess energy intakes relative to energy expenditure. Accurate estimation of resting metabolic rate (RMR), the largest component of total energy requirements, is crucial to strategies aimed at reducing the prevalence and incidence of overweight and obesity. We evaluated the accuracies of established and locally developed RMR prediction equations (RMRP) among adults. Four hundred adult volunteers ages 20 to 65 years had RMR measured (RMRM) with a MedGem® indirect calorimeter according to recommended procedures. RMRP were compared to RMRM with values ± 10% of RMRM deemed accurate. Anthropometry was measured using standard procedure. Linear regression with bootstrap analyses was used to develop local RMRP equations based on anthropometric and demographic variables. The University of the West Indies Ethics Committee approved the study. Males had higher mean absolute RMR (p < 0.001) but similar mean age-adjusted measured RMR per kg of body (20.9 vs. 21.5 kcals/day; p = 0.1) to females. The top performing established anthropometry-based RMRP among participants by sex, physical activity (PA) level and BMI status subgroups were Mifflin-St Jeor, Owen, Korth, Harris-Benedict, and Livingston, while Johnstone, Cunningham, Müller (body composition (BC)), Katch and McArdle, Mifflin-St Jeor (BC) were the most accurate BC-based RMRP. Locally developed RMRP had accuracies comparable to their top-ranked established RMRP counterparts. Accuracies of established RMRP depended on habitual PA level, BMI status, BC and sex. Furthermore, locally developed RMRP provide useful alternatives to established RMRP.

Accuracy of Equations Used to Predict Resting Metabolic Rate (RMR) Requirements Among Healthy and Acutely Ill Females in Trinidad and Tobago; Aged 18 to 65 Years

Abstract Objectives To determine the level of accuracy of the recommended resting metabolic rate (RMR) prediction equations among healthy and acutely ill females: aged 18 to 65 years, in Trinidad and Tobago. Methods Following informed consent and enrollment, sixty female (acutely ill 30; healthy 30) volunteers had anthropometry and RMR (MedGem® indirect calorimeter, Micro life, USA) measured using recommended procedures. RMR from prediction equations were compared to RMR measured by indirect calorimetry with values between ±10% of measured RMR being considered accurate. The university's ethics committee approved the study. Results The top-two ranked recommended RMR prediction equations for acutely ill females, in decreasing order of accuracy were Owen (46.7%) and Bernstein (40%). Among the healthy females, the top-two ranked recommended RMR prediction equations with a similar level of accuracy (46.7%) were Livingston and Kohlstadt, and DeLorenzo. The population-derived RMR prediction equations had 56.7% and 70% accuracies among the acutely ill and healthy females respectively. These were significantly higher than the top-ranked recommended prediction equations for both groups. Notably, limiting the risk of malnutrition by at least 5%: through diet quality by way of accurate energy predictions could improve health-related quality of life. Increasing the predictability of energy needs within any population can also ensure accuracy of RMR per weight in kilogram (kcal/kg) needed daily and energy balance. On average, this female population utilized 18.4 kcal of energy per kilogram body weight (kcal/kg). Conclusions All the recommended RMR prediction equations resulted in biases &amp;gt; 50%, thus our population-derived RMR equations can be used as a superior alternative among participants to determining the energy-needs of acutely ill individuals as well as healthy females. Substituting the commonly used prediction equations with population-specific equations can increase the level of accuracy by at least 10%, thus limiting the risk of malnutrition by at least 5% and improving health-related quality of life. Funding Sources NIL.