Abstract 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.
BACKGROUND: Associations between body composition and the energy expended on basal metabolism and activity are complex and age dependent. OBJECTIVE: The objective was to examine associations between ...body composition and daily (DEE), basal (BEE), and activity energy expenditure (AEE) throughout the adult life span. DESIGN: A cross-sectional study was conducted in 529 adults aged 18-96 y. DEE was measured by using doubly labeled water, BEE by using respirometry, and body composition by isotope dilution. AEE was calculated as DEE - BEE, and physical activity level (PAL) was calculated as DEE/BEE. RESULTS: Up to age 52 y, fat-free mass (FFM) and fat mass (FM) were positively associated with age in men, but no significant effect was observed in women. No effects of age on DEE and AEE were observed. The average DEE in men (14.1 MJ/d) was 27% greater than that in women (10.7 MJ/d). PAL averaged 1.84 in men and 1.75 in women. Above and including the age of 52 y, FFM, FM, DEE, BEE, and AEE were all negatively associated with greater age. The effect of age on AEE was greater than on BEE; consequently, PAL by the age of 95 y was only 1.36. PAL and AEE were both unrelated to FFM (both age adjusted). CONCLUSIONS: PAL and AEE were not associated with age in subjects aged <52 y. AEE, BEE, and PAL were all negatively associated with age in subjects aged ≥52 y. An absence of a relation between age-adjusted PAL and FFM suggested that greater physical activity was not associated with higher FFM in the elderly.
ABSTRACT
Background
Fasting during the month of Ramadan entails abstinence from eating and drinking between dawn and sunset and a major shift in meal times and patterns with associated changes in ...several hormones and circadian rhythms; whether there are accompanying changes in energy metabolism is unclear.
Objective
We have investigated the impact of Ramadan fasting on resting metabolic rate (RMR), activity, and total energy expenditure (TEE).
Design
Healthy nonobese volunteers (n = 29; 16 women) fasting during Ramadan were recruited. RMR was measured with the use of indirect calorimetry. In subgroups of participants, activity (n = 11; 5 women) and TEE (n = 10; 5 women) in free-living conditions were measured with the use of accelerometers and the doubly labeled water technique, respectively. Body composition was measured with the use of bioelectrical impedance. Measurements were repeated after a wash-out period of between 1 and 2 mo after Ramadan. Nonparametric tests were used for comparative statistics.
Results
Ramadan fasting did not result in any change in RMR (mean ± SD: 1365.7 ± 230.2 compared with 1362.9 ± 273.6 kcal/d for Ramadan and post-Ramadan respectively, P = 0.713, n = 29). However, controlling for the effects of age, sex, and body weight, RMR was higher in the first week of Ramadan than in subsequent weeks. During Ramadan, the total number of steps walked were significantly lower (n = 11, P = 0.001), while overall sleeping time was reduced and different sleeping patterns were seen. TEE did not differ significantly between Ramadan and post-Ramadan (mean ± SD: 2224.1 ± 433.7 compared with 2121.0 ± 718.5 kcal/d for Ramadan and post-Ramadan, P = 0.7695, n = 10).
Conclusions
Ramadan fasting is associated with reduced activity and sleeping time, but no significant change in RMR or TEE. Reported weight changes with Ramadan in other studies are more likely to be due to differences in food intake. This trial is registered at clinicaltrials.gov as NCT02696421.
We report a web-based tool for analysis of experiments using indirect calorimetry to measure physiological energy balance. CalR simplifies the process to import raw data files, generate plots, and ...determine the most appropriate statistical tests for interpretation. Analysis using the generalized linear model (which includes ANOVA and ANCOVA) allows for flexibility in interpreting diverse experimental designs, including those of obesity and thermogenesis. Users also may produce standardized output files for an experiment that can be shared and subsequently re-evaluated using CalR. This framework will provide the transparency necessary to enhance consistency, rigor, and reproducibility. The CalR analysis software will greatly increase the speed and efficiency with which metabolic experiments can be organized, analyzed per accepted norms, and reproduced and will likely become a standard tool for the field. CalR is accessible at https://CalRapp.org/.
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•CalR is a free web tool for analysis of experiments using indirect calorimetry•Imports data, generates plots, and determines the best-fit statistical model•Outputs a standardized CalR file that can be shared, deposited, and re-read by CalR•Increases speed, transparency, and reproducibility of energy balance experiments
Indirect calorimetry is a powerful tool for studying energy balance. These experiments produce rich datasets but are difficult to analyze properly and lack transparency. Mina and colleagues created a rigorous tool, CalR, that takes raw data from indirect calorimeters, allows comprehensive data exploration and reproducible workflows, and provides standardized analyses.
Although resting metabolic rate (RMR) is crucial for understanding athletes’ energy requirements, limited information is available on the RMR of Paralympic athletes.
The aim of this study was to ...determine RMR and its predictors in a diverse cohort of Paralympic athletes and evaluate the agreement between measured and predicted RMR from both newly developed and pre-existing equations.
This cross-sectional study, conducted between September 2020 and September 2022 in the Netherlands and Norway, assessed RMR in Paralympic athletes by means of ventilated hood indirect calorimetry and body composition by means of dual-energy x-ray absorptiometry.
Sixty-seven Paralympic athletes (male: n = 37; female: n = 30) competing in various sports, with a spinal cord disorder (n = 22), neurologic condition (n = 8), limb deficiency (n = 18), visual or hearing impairment (n = 7), or other disability (n = 12) participated.
RMR, fat-free mass (FFM), body mass, and triiodothyronine (T3) concentrations were assessed.
Multiple regression analyses were conducted with height, FFM, body mass, sex, T3 concentration, and disabilities as potential predictors of RMR. Differences between measured and predicted RMRs were analyzed for individual accuracy, root mean square error, and intraclass correlation.
Mean ± SD RMR was 1386 ± 258 kcal/d for females and 1686 ± 302 kcal/d for males. Regression analysis identified FFM, T3 concentrations, and the presence of a spinal cord disorder, as the main predictors of RMR (adjusted R2 = 0.71; F = 50.3; P < .001). The novel prediction equations based on these data, as well as pre-existing equations of Chun and colleagues and Nightingale and Gorgey performed well on accuracy (>60% of participants within 10% of measured RMR), had good reliability (intraclass correlation >0.78), and low root mean square error (≤141 kcal).
FFM, total T3 concentrations, and presence of spinal cord disorder are the main predictors of RMR in Paralympic athletes. Both the current study’s prediction equations and those from Chun and colleagues and Nightingale and Gorgey align well with measured RMR, offering accurate prediction equations for the RMR of Paralympic athletes.
Metabolic adaptation to weight changes relates to body weight control, obesity and malnutrition. Adaptive thermogenesis (AT) refers to changes in resting and non-resting energy expenditure (REE and ...nREE) which are independent from changes in fat-free mass (FFM) and FFM composition. AT differs in response to changes in energy balance. With negative energy balance, AT is directed towards energy sparing. It relates to a reset of biological defence of body weight and mainly refers to REE. After weight loss, AT of nREE adds to weight maintenance. During overfeeding, energy dissipation is explained by AT of the nREE component only. As to body weight regulation during weight loss, AT relates to two different
set points
with a
settling
between them. During early weight loss, the first
set
is related to depleted glycogen stores associated with the fall in insulin secretion where AT adds to meet brain’s energy needs. During maintenance of reduced weight, the second
set
is related to low leptin levels keeping energy expenditure low to prevent triglyceride stores getting too low which is a risk for some basic biological functions (e.g., reproduction). Innovative topics of AT in humans are on its definition and assessment, its dynamics related to weight loss and its constitutional and neuro-endocrine determinants.
Historically, obese individuals were believed to have lower energy expenditure (EE) rates than nonobese individuals (normal and overweight), which, in the long term, would contribute to a positive ...energy balance and subsequent weight gain. The aim of this review was to critically appraise studies that compared measures of EE and its components, resting EE (REE), activity EE (AEE), and diet-induced thermogenesis (DIT), in obese and nonobese adults to elucidate whether obesity is associated with altered EE. Contrary to popular belief, research has shown that obese individuals have higher absolute REE and total EE. When body composition (namely the metabolically active component, fat-free mass) is taken into account, these differences between obese and nonobese individuals disappear, suggesting that EE in obese individuals is not altered. However, an important question is whether AEE is lower in obese individuals because of a decrease in overall physical activity or because of less energy expended while performing physical activity. AEE and DIT could be reduced in obese individuals, mostly because of unhealthy behavior (low physical activity, higher intake of fat). However, the current evidence does not support the hypothesis that obesity is sustained by lower daily EE or REE. Future studies, comparing EE between obese and nonobese and assessing potential physiologic abnormalities in obese individuals, should be able to better answer the question of whether these individuals have altered energy metabolism.
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