Poor performance of predictive equations to estimate resting energy expenditure in patients with Crohn's disease.

Studies exploring the accuracy of equations calculating Resting Energy Expenditure (REE) in patients with Crohn's disease are lacking. The aim of this study was to investigate the accuracy of REE predictive equations against indirect calorimetry in Crohn's disease patients. REE was measured using indirect calorimetry (mREE) after an overnight fasting. Fourteen predictive equations, with and without body composition analysis parameters, were compared with mREE using different body weight approaches. Body composition analysis was performed using dual X-ray absorptiometry. 186 Crohn's disease outpatients (102 males) with mean age 41.3±14.1 years and 37.6% with active disease were evaluated. Mean mREE in the total sample was 1734±443 kcal/day. All equations under-predicted REE and showed moderate correlations with mREE (Pearson's r or Spearman's rho 0.600-0.680 for current weight, all p-values<0.001). Accuracy was low for all equations at the individual level (28-42% and 25-40% for current and adjusted body weight, respectively, 19-33% for equations including body composition parameters). At the group level, accuracy showed wide limits of agreement and proportional biases. Accuracy remained low when sample was studied according to disease activity, sex, body mass index and medication use. All predictive equations underestimated REE and showed low accuracy. Indirect calorimetry remains the best method for estimating REE of patients with Crohn's disease.

Development of new predictive equations for basal metabolic rate in Iranian healthy adults: negligible effect of sex.

Some studies have reported inaccuracy of predicting basal metabolic rate (BMR) by using common equations for Asian people. Thus, this study was undertaken to develop new predictive equations for the Iranian community and also to compare their accuracy with the commonly used formulas. Anthropometric measures and thyroid function were evaluated for 267 healthy subjects (18-60 y). Indirect calorimetry (InCal) was performed only for those participants with normal thyroid function tests (n = 252). Comparison of predicted RMR (both kcal/d and kcal.kg.wt-1.d-1) using current predictive formulas and measured RMR revealed that Harris-Benedict and FAO/WHO/UNU significantly over-estimated and Mifflin-St. Jeor significantly under-estimated RMR as compared to InCal measurements. In stepwise regression analysis for developing new equations, the highest r2 (=0.89) was from a model comprising sex, height and weight. However, further analyses revealed that unlike the subjects under 30 y, the association between age and the measured RMR in subjects 30 y and plus was negative (r = -0.241, p = 0.001). As a result, two separate equations were developed for these two age groups. Over 80 percent of variations were covered by the new equations. In conclusion, there were statistical significant under- and over-estimation of RMR using common predictive equations in our subjects. Using the new equations, the accuracy of the calculated RMR increased remarkably.

Generalized bioelectric impedance-based equations underestimate body fluids in athletes.

The current study aimed: (i) to external validate total body water (TBW) and extracellular water (ECW) derived from athlete and non-athlete predictive equations using radioisotope dilution techniques as a reference criterion in male and female athletes; (ii) in a larger sample, to determine the agreement between specific and generalized equations when estimating body fluids in male and female athletes practicing different sports. A total of 1371 athletes (men: n = 921, age 23.9 ± 1.4 y; women: n = 450, age 27.3 ± 6.8 y) participated in this study. All athletes underwent bioelectrical impedance analyses, while TBW and ECW were assessed with dilution techniques in a subgroup of 185 participants (men: n = 132, age 21.7 ± 5.1 y; women: n = 53, age 20.3 ± 4.5 y). Two specific and eight generalized predictive equations were tested. Compared to the criterion methods, no mean bias was observed using the athlete-specific equations for TBW and ECW (-0.32 to 0.05, p > 0.05) and the coefficient of determination ranged from R2  = 0.83 to 0.94. The majority of the generalized predictive equations underestimated TBW and ECW (p < 0.05); R2 ranged from 0.66 to 0.89. In the larger sample, all the generalized equations showed lower TBW and ECW values (ranging from -6.58 to -0.19, p < 0.05) than specific predictive equations; except for TBW in female power/velocity (one equation) athletes and team sport (two equations). The use of generalized BIA-based equations leads to an underestimation of TBW, and ECW compared to athlete-specific predictive equations. Additionally, the larger sample indicates that generalized equations overall provided lower TBW and ECW compared to the athlete-specific equations.

Reference values for distal motor conduction of the tibial nerve: Effects of demographic and anthropometric measures.

INTRODUCTION: In this study we collected reference values for the across-tarsal-tunnel conduction of the motor tibial nerve (mTN). METHODS: The mTN compound muscle action potentials (CMAPs) from the abductor hallucis muscle were obtained by stimulating below/above the malleolus and the popliteal fossa. The effect of weight, height, body mass index (BMI), foot and leg length, sex, and age were evaluated using univariate and multivariate correlation analyses, and predictive equations for each mTN conduction parameter were developed. RESULTS: On the basis of data from 185 subjects, there were differences between women and men in all anthropometric parameters and for some nerve conduction values. Through multivariate analysis, age, but not sex, was found to have a significant impact. Height affected both distal and proximal conduction velocity. BMI affected CMAP amplitude. DISCUSSION: mTN conduction is influenced by various demographic and anthropometric factors. For all intrinsic factors, height demonstrated the greatest effect on mTN conduction across the tarsal tunnel.

Agreement between the total energy expenditure calculated with accelerometry data and the BMR yielded by predictive equations v. the total energy expenditure obtained with doubly labelled water in low-income women with excess weight.

Low-income women are the group with the highest levels of obesity worldwide. In low-income settings, the use of predictive equations, which yield a measure of the individuals' BMR, is a feasible approach to estimate the individuals' total energy expenditure (TEE), using the factorial method (calculated-TEE = BMR × physical activity level), an important step of the obesity nutritional care. The present study aimed to identify the predictive equation that, in conjunction with metabolic equivalents of tasks (MET) data from accelerometers, yields the calculated-TEE with better agreement compared with the TEE measured by doubly labelled water (TEE-DLW). Forty-five women aged 19-45 years, with excess weight and mothers of undernourished children, were included. They received DLW to determine TEE (14 d); at the same time, they used triaxial accelerometers (7 d) to estimate their MET. The Bland-Altman method, paired-sample t tests, concordance correlation coefficient and root-mean-square error were used to assess the agreement. Maximum allowed differences were defined as 24 %, based on the within-variance coefficient of the energy intake of the sample. Eleven equations were studied. The calculated-TEE obtained by five equations showed non-significant bias: Dietary Reference Intake (Institute of Medicine (2005) Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids), FAO/WHO/UNU ((2001) Food and Nutrition Technical Report Series), Harris & Benedict ((1919) Proc Natl Acad Sci USA4, 370-373), Henry & Rees ((1991) Eur J Clin Nutr45, 177-185) and Schofield ((1985) Hum Nutr Clin Nutr39, 5-41). The mean percentage differences were -1·5, -0·8, 2·2, -2·2 and 2·0 %, respectively. Considering all parameters, FAO/WHO/UNU ((2001) Food and Nutrition Technical Report Series) equation performed slightly better than the others; nevertheless, no equation in conjunction with the estimated-MET showed a calculated-TEE with its CI for the Bland-Altman limits of agreement inside the pre-defined acceptable range.

Low validity of predictive equations for calculating resting energy expenditure in overweight and obese women with polycystic ovary syndrome.

BACKGROUND: Predictive equations are the main clinical tools for determining resting energy expenditure (REE). However, their adequate use in overweight and obese individuals is unclear. Thus, we investigated the best predictive equations for estimating REE in overweight and obese women with polycystic ovary syndrome (PCOS). METHODS: Eleven analyses were performed with prediction equations (pREE) based on anthropometric parameters in 30 overweight or obese women with PCOS without other chronic diseases. The measured REE (mREE) was calculated by indirect calorimetry. The validity of the equations was investigated by comparison, accuracy and agreement tests between pREE and mREE at both the individual and group level. RESULTS: Four analyses were similar to those of mREE, and smallest mean differences were observed for the World Health Organization/Food and Agriculture Organization of the United Nations/United Nations University (WHO/FAO/UNU) considering weight (W) [0.07 (1.13) MJ (16 [270] kcal)]. Individual accuracy was greater than 50% for Harris and Benedict, Müller and Lazzer equations. The percentage of REE underestimation ranged between 16.7% and 73.3%, whereas higher rates of overestimation were observed in the De Luis (66.7%) and Ireton-Jones (43.3%) equations. Mean bias at the group level was lowest in the WHO/FAO/UNU W and WHO/FAO/UNU considering weight and height (WH), Müller and Lazzer equations (-2.8 to 0.5). The WHO/FAO/UNU W and WHO/FAO/UNU WH formulas were optimal in individual agreement (33.3%). CONCLUSIONS: FAO/WHO/UNU W equations may estimate the REE in overweight and obese women with PCOS. However, the low individual accuracy and agreement in relation to mREE suggest caution regarding when to use the formula to perform an individual nutritional plan.

Estimation of Resting Energy Expenditure: Validation of Previous and New Predictive Equations in Obese Children and Adolescents.

OBJECTIVE: Accurate estimation of resting energy expenditure (REE) in childrenand adolescents is important to establish estimated energy requirements. The aim of the present study was to measure REE in obese children and adolescents by indirect calorimetry method, compare these values with REE values estimated by equations, and develop the most appropriate equation for this group. METHODS: One hundred and three obese children and adolescents (57 males, 46 females) between 7 and 17 years (10.6 ± 2.19 years) were recruited for the study. REE measurements of subjects were made with indirect calorimetry (COSMED, FitMatePro, Rome, Italy) and body compositions were analyzed. RESULTS: In females, the percentage of accurate prediction varied from 32.6 (World Health Organization [WHO]) to 43.5 (Molnar and Lazzer). The bias for equations was -0.2% (Kim), 3.7% (Molnar), and 22.6% (Derumeaux-Burel). Kim's (266 kcal/d), Schmelzle's (267 kcal/d), and Henry's equations (268 kcal/d) had the lowest root mean square error (RMSE; respectively 266, 267, 268 kcal/d). The equation that has the highest RMSE values among female subjects was the Derumeaux-Burel equation (394 kcal/d). In males, when the Institute of Medicine (IOM) had the lowest accurate prediction value (12.3%), the highest values were found using Schmelzle's (42.1%), Henry's (43.9%), and Müller's equations (fat-free mass, FFM; 45.6%). When Kim and Müller had the smallest bias (-0.6%, 9.9%), Schmelzle's equation had the smallest RMSE (331 kcal/d). The new specific equation based on FFM was generated as follows: REE = 451.722 + (23.202 * FFM). According to Bland-Altman plots, it has been found out that the new equations are distributed randomly in both males and females. CONCLUSION: Previously developed predictive equations mostly provided unaccurate and biased estimates of REE. However, the new predictive equations allow clinicians to estimate REE in an obese children and adolescents with sufficient and acceptable accuracy.

Evaluation of a non-invasive multisensor accelerometer for calculating energy expenditure in ventilated intensive care patients compared to indirect calorimetry and predictive equations.

Continuous measurement of resting energy expenditure (REE) in critically ill patients remains challenging but is required to prevent malnutrition. SenseWear Pro 3 Armband (SWA) is a research grade accelerometer for assessment of REE with the advantage of easy handling. In a prospective study we compared SWA with indirect calorimetry (IC) and predictive equations in critically ill, ventilated patients. REE was measured by SWA, IC and calculated by predictive formulas. Potential confounding factors that influence REE were also recorded. Results of SenseWear Armband and indirect calorimetry were compared using the Bland-Altman method. 34 ICU patients were investigated. SWA underestimated resting energy expenditure compared to IC with a mean bias of ΔREE = -253.6 ± 333.2 kcal, equivalent to -11.7 % (p = 0.025). This underestimation was seen in both, medical (-14.9 %) and surgical (-12.9 %) patients and the bias was greater in patients with fever (-19.0 %), tachycardia (-18.7 %) or tachypnea (-26.2 %). Differences were also noted when SWA was compared to predictive formulas. At present, SWA cannot be regarded as an alternative to indirect calorimetry. Individual measurements are often inaccurate and should be used with caution until improved algorithms, based on the results of this study, have been implemented.

Resting energy expenditure in obese women: comparison between measured and estimated values.

Assessing energy requirements is a fundamental activity in clinical dietetic practice. The aim of this study was to investigate which resting energy expenditure (REE) predictive equations are the best alternatives to indirect calorimetry before and after an interdisciplinary therapy in Brazilian obese women. In all, twelve equations based on weight, height, sex, age, fat-free mass and fat mass were tested. REE was measured by indirect calorimetry. The interdisciplinary therapy consisted of nutritional, physical exercise, psychological and physiotherapy support during the course of 1 year. The average differences between measured and predicted REE, as well as the accuracy at the ±10 % level, were evaluated. Statistical analysis included paired t tests, intraclass correlation coefficients and Bland-Altman plots. Validation was based on forty obese women (BMI 30-39·9 kg/m2). Our major findings demonstrated a wide variation in the accuracy of REE predictive equations before and after weight loss in non-morbid, obese women. The equations reported by Harris-Benedict and FAO/WHO/United Nations University (UNU) were the only ones that did not show significant differences compared with indirect calorimetry and presented a bias <5 %. The Harris-Benedict equation provided 40 and 47·5 % accurate predictions before and after therapy, respectively. The FAO equation provided 35 and 47·5 % accurate predictions. However, the Bland-Altman analysis did not show good agreement between these equations and indirect calorimetry. Therefore, the Harris-Benedict and FAO/WHO/UNU equations should be used with caution for obese women. The need to critically re-assess REE data and generate regional and more homogeneous REE databases for the target population is reinforced.

Multiple Off-Ice Performance Variables Predict On-Ice Skating Performance in Male and Female Division III Ice Hockey Players.

The purpose of this study was to determine if off-ice performance variables could predict on-ice skating performance in Division III collegiate hockey players. Both men (n = 15) and women (n = 11) hockey players (age = 20.5 ± 1.4 years) participated in the study. The skating tests were agility cornering S-turn, 6.10 m acceleration, 44.80 m speed, modified repeat skate, and 15.20 m full speed. Off-ice variables assessed were years of playing experience, height, weight and percent body fat and off-ice performance variables included vertical jump (VJ), 40-yd dash (36.58m), 1-RM squat, pro-agility, Wingate peak power and peak power percentage drop (% drop), and 1.5 mile (2.4km) run. Results indicated that 40-yd dash (36.58m), VJ, 1.5 mile (2.4km) run, and % drop were significant predictors of skating performance for repeat skate (slowest, fastest, and average time) and 44.80 m speed time, respectively. Four predictive equations were derived from multiple regression analyses: 1) slowest repeat skate time = 2.362 + (1.68 x 40-yd dash time) + (0.005 x 1.5 mile run), 2) fastest repeat skate time = 9.762 - (0.089 x VJ) - (0.998 x 40-yd dash time), 3) average repeat skate time = 7.770 + (1.041 x 40-yd dash time) - (0.63 x VJ) + (0.003 x 1.5 mile time), and 4) 47.85 m speed test = 7.707 - (0.050 x VJ) - (0.01 x % drop). It was concluded that selected off-ice tests could be used to predict on-ice performance regarding speed and recovery ability in Division III male and female hockey players. Key pointsThe 40-yd dash (36.58m) and vertical jump tests are significant predictors of on-ice skating performance specific to speed.In addition to 40-yd dash and vertical jump, the 1.5 mile (2.4km) run for time and percent power drop from the Wingate anaerobic power test were also significant predictors of skating performance that incorporates the aspect of recovery from skating activity.Due to the specificity of selected off-ice variables as predictors of on-ice performance, coaches can elect to assess player performance off-ice and focus on other uses of valuable ice time for their individual teams.

Appendicular skeletal muscle in hospitalised hip-fracture patients: development and cross-validation of anthropometric prediction equations against dual-energy X-ray absorptiometry.

BACKGROUND: accurate and practical assessment methods for assessing appendicular skeletal muscle (ASM) is of clinical importance for the diagnosis of geriatric syndromes associated with skeletal muscle wasting. OBJECTIVES: the purpose of this study was to develop and cross-validate novel anthropometric prediction equations for the estimate of ASM in older adults post-surgical fixation for hip fracture, using dual-energy X-ray absorptiometry (DEXA) as the criterion measure. SUBJECTS: community-dwelling older adults (aged ≥65 years) recently hospitalised for hip fracture. SETTING: participants were recruited from hospital in the acute phase of recovery. DESIGN: validation measurement study. MEASUREMENTS: a total of 79 hip fracture patients were involved in the development of the regression models (MD group). A further 64 hip fracture patients also recruited in the early phase of recovery were used in the cross-validation of the regression models (CV group). Multiple linear regression analyses were undertaken in the MD group to identify the best performing prediction models. The linear coefficient of determination (R(2)) in addition to the standard error of the estimate (SEE) were calculated to determine the best performing model. Agreement between estimated ASM and ASMDEXA in the CV group was assessed using paired t-tests with the 95% limits of agreement (LOA) assessed using Bland-Altman analyses. RESULTS: the mean age of all the participants was 82.1 ± 7.3 years. The best two prediction models are presented as follows: ASMPRED-EQUATION_1: 22.28 - (0.069 * age) + (0.407 * weight) - (0.807 * BMI) - (0.222 * MAC) (adjusted R(2): 0.76; SEE: 1.80 kg); ASMPRED-EQUATION_2: 16.77 - (0.036 * age) + (0.385 * weight) - (0.873 * BMI) (adjusted R(2): 0.73; SEE: 1.90 kg). The mean bias from the CV group between ASMDEXA and the predictive equations is as follows: ASMDEXA - ASMPRED-EQUATION_1: 0.29 ± 2.6 kg (LOA: -4.80, 5.40 kg); ASMDEXA - ASMPRED-EQUATION_2: 0.13 ± 2.5 kg (LOA: -4.77, 5.0 kg). No significant difference was observed between measured ASMDEXA and estimated ASM (ASMDEXA: 16.4 ± 3.9 kg; ASMPRED-EQUATION_1: 16.7 ± 3.2 kg (P = 0.379); ASMPRED-EQUATION_2: 16.6 ± 3.2 kg (P = 0.670)). CONCLUSIONS: we have developed and cross-validated novel anthropometric prediction equations against DEXA for the estimate of ASM designed for application in older orthopaedic patients. Our equation may be of use as an alternative to DEXA in the diagnosis of skeletal muscle wasting syndromes. Further validation studies are required to determine the clinical utility of our equation across other settings, including hip fracture patients admitted from residential care, and also with a longer-term follow-up.

Adequacy of basal metabolic rate prediction equations in individuals with severe obesity: A systematic review and meta-analysis.

The determination of energy requirements in clinical practice is based on basal metabolic rate (BMR), frequently predicted by equations that may not be suitable for individuals with severe obesity. This systematic review and meta-analysis examined the accuracy and precision of BMR prediction equations in adults with severe obesity. Four databases were searched in March 2021 and updated in May 2023. Eligible studies compared BMR prediction equations with BMR measured by indirect calorimetry. Forty studies (age: 28-55 years, BMI: 40.0-62.4 kg/m2) were included, most of them with a high risk of bias. Studies reporting bias (difference between estimated and measured BMR) were included in the meta-analysis (n = 20). Six equations were meta-analyzed: Harris & Benedict (1919); WHO (weight) (1985); Owen (1986); Mifflin (1990); Bernstein (1983); and Cunningham (1980). The most accurate and precise equations in the overall analysis were WHO (-12.44 kcal/d; 95%CI: -81.4; 56.5 kcal/d) and Harris & Benedict (-18.9 kcal/d; 95%CI -73.2; 35.2 kcal/d). All the other equations tended to underestimate BMR. Harris & Benedict and WHO were the equations with higher accuracy and precision in predicting BMR in individuals with severe obesity. Additional analyses suggested that equations may perform differently according to obesity BMI ranges, which warrants further investigation.

Measured and predicted resting metabolic rate in patients with inflammatory bowel disease.

OBJECTIVE: The present study aimed to compare measured and estimated resting metabolic rate (RMR) predicted by selected equations in patients with nonactive inflammatory bowel disease (IBD) on an outpatient university clinic regimen. RESEARCH METHODS & PROCEDURES: Seventy-two adult (≥20 years) IBD patients (45 with Crohn's disease-CD) had RMR measured (mRMR) by indirect calorimetry and also estimated by predictive equations (Cunningham, Henry, Anjos et al., and Marra et al.). Body composition was assessed by DXA. Absolute Bias (estimated - mRMR) and % Bias (Bias/mRMR) were calculated. Agreement was assessed as the limit of agreement (LoA) in the Bland & Altman approach. RESULTS: There was no difference in age, body composition and mRMR between individuals with CD (5414.2 ± 1023.7 kJ/day) and ulcerative colitis (5443.9 ± 1008.9 kJ/day). Among the equations, only the Anjos et al.'s population-specific equation (-52.1 [642.0] kJ/day, P = 0.493; LoA: -1311; 1206 kJ/d) accurately estimated RMR. The equations of Marra et al. produced the highest % Bias (24.1 ± 14.8%). The Bland & Altman plots showed that the range of the LoA was relatively similar for all equations. In the simple regression analysis, the model with FFM resulted in a higher coefficient of determination (R2 = 0.51 for DC 0.74 for UC) compared to the model that included BM (R2 = 0.35 for DC and 0.65 for UC). CONCLUSIONS: Among the equations analyzed, only Anjos et al.'s accurately estimated RMR in outpatients with nonactive IBD. However, caution is advised when applying it at the individual level, due to the wide observed LoA.

Validity of predictive equations for total energy expenditure against doubly labeled water.

Variations in physical activity energy expenditure can make accurate prediction of total energy expenditure (TEE) challenging. The purpose of the present study was to determine the accuracy of available equations to predict TEE in individuals varying in physical activity (PA) levels. TEE was measured by DLW in 56 adults varying in PA levels which were monitored by accelerometry. Ten different models were used to predict TEE and their accuracy and precision were evaluated, considering the effect of sex and PA. The models generally underestimated the TEE in this population. An equation published by Plucker was the most accurate in predicting the TEE in our entire sample. The Pontzer and Vinken models were the most accurate for those with lower PA levels. Despite the levels of accuracy of some equations, there were sizable errors (low precision) at an individual level. Future studies are needed to develop and validate these equations.

Evaluation of Effective Energy Values of Six Protein Ingredients Fed to Beagles and Predictive Energy Equations for Protein Feedstuff.

This study evaluated the nutrition composition, the nutrient digestibility, and the energy value of six protein ingredients used in pet food by the difference method in six beagles within a 7 × 6 incomplete Latin square design. The results showed that the apparent total tract digestibility of gross energy (GE) and organic matter (OM) in beagles fed the fish meal (FM) and corn gluten meal (CGM) diets was higher than for those fed the meat and bone meal (MBM), soybean meal (SBM), mealworm meal (MM), and yeast extract (YE) diets (p < 0.05). The digestible energy (DE), metabolizable energy (ME), and net energy (NE) of the MM diet were greater than the other diets, and MBM was the lowest (p < 0.05). The ME of protein ingredients was positively correlated with organic matter and negatively correlated with the ash content. The NE of protein ingredients was positively correlated with the crude protein content and negatively correlated with the ash content. The study resulted in predictive energy equations for protein ingredients that were more accurate than the NRC's predictive equation of ME when the ash content of the ingredient was more than 30% DM. In conclusion, the nutrient digestibility and energy value of corn gluten meal were similar to those of fish meal and those of soybean meal were similar to yeast extract. All predictive energy equations for six protein feedstuffs had slight differences with measured energy values.

Evaluating predictive equations for energy requirements throughout breast cancer trajectory: A comparative study.

BACKGROUND & AIMS: Accurately estimating resting energy requirements is crucial for optimizing energy intake, particularly in the context of patients with varying energy needs, such as individuals with cancer. We sought to evaluate the agreement between resting energy expenditure (REE) predicted by 40 equations and that measured by reference methods in women undergoing active breast cancer treatment stage (I-IV) and post-completion (i.e., survivors). METHODS: Data from 4 studies were combined. REE values estimated from 40 predictive equations identified by a systematic search were compared with REE assessed by indirect calorimetry (IC) using a metabolic cart (MC-REE N = 46) or a whole-room indirect calorimeter (WRIC-REE N = 44). Agreement between methods was evaluated using Bland-Altman and Lin's concordance coefficient correlation (Lin's CCC). RESULTS: Ninety participants (24 % survivors, 61.1% had early-stage breast cancer I or II, mean age: 56.8 ± 11 years; body mass index: 28.7 ± 6.4 kg/m2) were included in this analysis. Mean MC-REE and WRIC-REE values were 1389 ± 199 kcal/day and 1506 ± 247 kcal/day, respectively. Limits of agreement were wide for all equations compared to both MC and WRIC (∼300 kcal for both methods), including the most commonly used ones, such as Harris-Benedict and Mifflin ST. Jeor equations; none had a bias within ±10% of measured REE, and all had low agreement per Lin's CCC analysis (<0.90). The Korth equation exhibited the best performance against WRIC and the Lvingston-Kohlstadt equation against MC. Similar patterns of bias were observed between survivors and patients and between patients with stages I-III versus IV cancer. CONCLUSION: Most equations failed to accurately predict REE at the group level, and none were effective at the individual level. This inaccuracy has significant implications for women with or surviving breast cancer, who may experience weight gain, maintenance, or loss due to inaccurate energy needs estimations. Therefore, our research underscores the need for further efforts to improve REE estimation.

Using static postures to estimate spinal loading during dynamic lifts with participant-specific thoracolumbar musculoskeletal models.

Static biomechanical simulations are sometimes used to estimate in vivo kinetic demands because they can be solved efficiently, but this ignores any potential inertial effects. To date, comparisons between static and dynamic analyses of spinal demands have been limited to lumbar joint differences in young males performing sagittal lifts. Here we compare static and dynamic vertebral compressive and shear force estimates during axial, lateral, and sagittal lifting tasks across all thoracic and lumbar vertebrae in older men and women. Participant-specific thoracolumbar full-body musculoskeletal models estimated vertebral forces from recorded kinematics both with and without consideration of dynamic effects, at an identified frame of peak vertebral loading. Static analyses under-predicted dynamic compressive and resultant shear forces, by an average of about 16% for all three lifts across the thoracic and lumbar spine but were highly correlated with dynamic forces (average r2 > .95). The study outcomes have the potential to enable standard clinical and occupational estimates using static analyses.

Adequacy of the "pocket formulas" for measuring total energy expenditure in healthy women compared with the new DRI equation: A doubly labeled water study.

OBJECTIVES: "Pocket formulas" are practical alternatives for calculating an individual's total energy expenditure (TEE). Typically, more sophisticated predictive equations are used, such as the new equations proposed in the Dietary Reference Intakes (DRI). Nevertheless, these new equations necessitate estimating physical activity levels (PALs). The aim of this study was to compare the use of pocket formulas (kcal/kg of body weight) with the new predictive equations for energy expenditure proposed by the DRI (2023) in healthy women and with the doubly labeled water (DLW) method to predict TEE. METHODS: The TEEs of healthy adult women were measured by DLW and calculated using the pocket formulas (× 20, × 25, × 30, and × 35 kcal/kg of body weight) and the new DRI equation. PALs by triaxial accelerometers were also collected. RESULTS: The study included 55 women. For the entire sample, the × 30 pocket formula had the lowest bias (-6%; limits of agreement [LOAs]: -39.8; 27.5; root mean square error: 373.4) and the highest precision (42%). The pocket formulas showed reasonable agreement in the different body mass index categories compared with the results found by the 2023 DRI proposal. For individuals with normal weight, the agreement was × 35 kcal/kg: bias (%) = -4.8; LoA = -41.5; 31.8, with overweight, it was × 30 kcal/kg: bias (%) = -2.2; LoA = -25.1; 20.6, and with obesity, it was × 30 kcal/kg: bias (%) = 4.2; LoA = -21.1; 29.4. CONCLUSION: Pocket formulas provide a reasonable agreement with TEE in healthy, sedentary, or low-active adult women, which may be a more simplistic strategy when there is no PAL data for calculating the DRI equations.

Estimates of Resting Energy Expenditure and Total Energy Expenditure Using Predictive Equations for Individuals After Bariatric Surgery: a Systematic Review with Meta-analysis.

PURPOSE: Patients after metabolic bariatric surgery (MBS) require attention to maintain energy balance and avoid weight regain. Predictive equations for resting energy expenditure (REE) and total energy expenditure (TEE) are needed since gold standard methods like calorimetry and doubly labeled water are rarely available in routine clinical practice. This study aimed to determine which predictive equation for REE and TEE has the lowest bias in subjects after MBS. METHODS: MEDLINE, Embase, Web of Science, and CENTRAL searches were performed. Meta-analyses were performed with the data calculated by the predictive equations and measured by the gold standard methods for those equations that had at least two studies with these data. The DerSimonian and Laird random-effects model and the I2 statistic were used to quantify heterogeneity in the quantitative analyses. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal checklist. RESULTS: Seven studies were included. The present study found that the Mifflin St. Jeor (1990) equation had the lowest bias (mean difference = - 39.71 kcal [95%CI = - 128.97; 49.55]) for calculating REE in post-BS individuals. The Harris-Benedict (1919) equation also yielded satisfactory results (mean difference = - 54.60 kcal [95%CI = - 87.92; - 21.28]). CONCLUSION: The predictive equation of Mifflin St. Jeor (1990) was the one that showed the lowest bias for calculating the REE of patients following MBS.

Methods over Materials: The Need for Sport-Specific Equations to Accurately Predict Fat Mass Using Bioimpedance Analysis or Anthropometry.

Bioelectrical impedance analysis (BIA) and anthropometry are considered alternatives to well-established reference techniques for assessing body composition. In team sports, the percentage of fat mass (FM%) is one of the most informative parameters, and a wide range of predictive equations allow for its estimation through both BIA and anthropometry. Although it is not clear which of these two techniques is more accurate for estimating FM%, the choice of the predictive equation could be a determining factor. The present study aimed to examine the validity of BIA and anthropometry in estimating FM% with different predictive equations, using dual X-ray absorptiometry (DXA) as a reference, in a group of futsal players. A total of 67 high-level male futsal players (age 23.7 ± 5.4 years) underwent BIA, anthropometric measurements, and DXA scanning. Four generalized, four athletic, and two sport-specific predictive equations were used for estimating FM% from raw bioelectric and anthropometric parameters. DXA-derived FM% was used as a reference. BIA-based generalized equations overestimated FM% (ranging from 1.13 to 2.69%, p < 0.05), whereas anthropometry-based generalized equations underestimated FM% in the futsal players (ranging from −1.72 to −2.04%, p < 0.05). Compared to DXA, no mean bias (p > 0.05) was observed using the athletic and sport-specific equations. Sport-specific equations allowed for more accurate and precise FM% estimations than did athletic predictive equations, with no trend (ranging from r = −0.217 to 0.235, p > 0.05). Regardless of the instrument, the choice of the equation determines the validity in FM% prediction. In conclusion, BIA and anthropometry can be used interchangeably, allowing for valid FM% estimations, provided that athletic and sport-specific equations are applied.