Influence of physical activity and dietary restraint on resting energy expenditure in young nonobese females

1991 ◽  
Vol 69 (3) ◽  
pp. 320-326 ◽  
Author(s):  
Eric T. Poehlman ◽  
Helen F. Viers ◽  
Mark Detzer
Keyword(s):  
Thyroid Hormones ◽  
Metabolic Rate ◽  
Endurance Training ◽  
Energy Intake ◽  
Dietary Restraint ◽  
Resting Metabolic Rate ◽  
Fat Free Mass ◽  
Energy Requirements ◽  
Fasting Plasma ◽  
Resting Energy

An understanding of the physiological and behavioral determinants of resting energy requirements is important to nutritional considerations in females. We examined the influence of endurance training and self-reported dietary restraint on resting metabolic rate and fasting plasma hormones in 44 nonobese females characterized for body composition, maximal aerobic power [Formula: see text], and daily energy intake. To examine the association of metabolic rate and dietary restraint with hormonal status, fasting plasma levels of insulin, glucose, and thyroid hormones (total and free fractions of thyroxine and triiodothyronine) were determined. In univariate analysis, resting metabolic rate (kcal∙min−1) was positively related to [Formula: see text] (L∙min−1) (r = 0.54; p < 0.01). This relationship, however, was partially dependent on body size, since fat-free mass was also related to resting metabolic rate (r = 0.42; p < 0.01) and [Formula: see text] (L∙min−1) (r = 0.75; p < 0.01). After controlling for fat-free weight using partial correlation analysis, the relation between RMR and [Formula: see text] was weaker but still significant (partial r = 0.38; p < 0.05). On the other hand, high levels of dietary restraint were associated with higher levels of body fat (r = 0.31; p < 0.05) and a lower resting metabolic rate (r = −0.29; p = 0.07). These associations persisted after control for differences in fat-free mass. Total energy intake as well as total and free levels of triiodothyronine were not related to resting metabolic rate or level of dietary restraint. Our results suggest that the level of endurance training (i.e., [Formula: see text]) and dietary restraint, independent of differences in fat-free mass, contribute to individual variation in resting metabolic rate of nonobese females. These findings appear to be unrelated to fasting plasma concentrations of thyroid hormones. Whereas high levels of endurance training are associated with increased energy requirements at rest, higher levels of dietary restraint are associated with a lower resting metabolic rate and possibly a propensity to gain body fat.Key words: endurance training, dietary restraint, resting metabolic rate, females, energy intake.

Resting Metabolic Rate and Thermic Effect of a Meal in Low- and Adequate-Energy Intake Male Endurance Athletes

1993 ◽  
Vol 3 (2) ◽  
pp. 194-206 ◽  
Author(s):  
Janice Thompson ◽  
Melinda M. Manore ◽  
James S. Skinner
Keyword(s):  
Metabolic Rate ◽  
Energy Intake ◽  
Weight Maintenance ◽  
Resting Metabolic Rate ◽  
Activity Level ◽  
Low Energy ◽  
Fat Free Mass ◽  
Endurance Athletes ◽  
Male Athletes ◽  
Thermic Effect

The resting metabolic rate (RMR) and thermic effect of a meal (TEM) were determined in 13 low-energy intake (LOW) and 11 adequate-energy intake (ADQ) male endurance athletes. The LOW athletes reported eating 1,490 kcal·day-1less than the ADQ group, while the activity level of both groups was similar. Despite these differences, both groups had a similar fat-free mass (FFM) and had been weight stable for at least 2 years. The RMR was significantly lower (p<0.05) in the LOW group compared to the values of the ADQ group (1.19 vs. 1.29 kcal·FFM-1·hr-l, respectively); this difference represents a lower resting expenditure of 158 kcal·day-1. No differences were found in TEM between the two groups. These results suggest that a lower RMR is one mechanism that contributes to weight maintenance in a group of low- versus adequate-energy intake male athletes.

Decreased Resting Metabolic Rate in Ballet Dancers with Menstrual Irregularity

1999 ◽  
Vol 9 (3) ◽  
pp. 285-294 ◽  
Author(s):  
Kathryn H. Myburgh ◽  
Claire Berman ◽  
Illana Novick ◽  
Timothy D. Noakes ◽  
Estelle V. Lambert
Keyword(s):  
Thyroid Hormone ◽  
Metabolic Rate ◽  
Energy Intake ◽  
Resting Metabolic Rate ◽  
Eating Attitudes ◽  
Fat Free Mass ◽  
Menstrual Irregularity ◽  
Ballet Dancers ◽  
Dietary Record ◽  
Serum Thyroid Hormone

We studied 21 ballet dancers aged 19.4 ± 1.4 years, hypothesizing that undernu-trition was a major factor in menstrual irregularity in this population. Menstrual history was determined by questionnaire. Eight dancers had always been regular (R). Thirteen subjects had a history of menstrual irregularity (HI). Of these, 2 were currently regularly menstruating, 3 had short cycles, 6 were oligomenorrheic, and 2 were amenorrheic. Subjects completed a weighed dietary record and an Eating Attitudes Test (EAT). The following physiological parameters were measured: body composition by anthropometry, resting metabolic rate (RMR) by open-circuit indirect calorimetry, and serum thyroid hormone concentrations by radioimmunoassay. R subjects had significantly higher RMR than HI subjects. Also, HI subjects had lower RMR than predicted by fat-free mass, compared to the R subjects. Neitherreported energy intake nor serum thyroid hormone concentrations were different between R and HI subjects. EAT scores varied and were not different between groups. We concluded that in ballet dancers, low RMR is more strongly associated with menstrual irregularity than is currentreported energy intake or serum thyroid hormone concentrations.

scholarly journals The biology of appetite control: Do resting metabolic rate and fat-free mass drive energy intake?

2015 ◽  
Vol 152 ◽  
pp. 473-478 ◽  
Author(s):  
J.E. Blundell ◽  
G. Finlayson ◽  
C. Gibbons ◽  
P. Caudwell ◽  
M. Hopkins
Keyword(s):  
Metabolic Rate ◽  
Energy Intake ◽  
Resting Metabolic Rate ◽  
Fat Free Mass ◽  
Appetite Control

scholarly journals Current Predictive Resting Metabolic Rate Equations Are Not Sufficient to Determine Proper Resting Energy Expenditure in Olympic Young Adult National Team Athletes

2021 ◽  
Vol 12 ◽  
Author(s):  
Aydın Balci ◽  
Ebru Arslanoğlu Badem ◽  
Ayfer Ezgi Yılmaz ◽  
Aslı Devrim-Lanpir ◽  
Bihter Akınoğlu ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Metabolic Rate ◽  
Female Athletes ◽  
Resting Metabolic Rate ◽  
Resting Energy Expenditure ◽  
Mean Difference ◽  
Fat Free Mass ◽  
Male Athletes ◽  
National Team ◽  
Resting Energy

Predictive resting metabolic rate (RMR) equations are widely used to determine athletes’ resting energy expenditure (REE). However, it remains unclear whether these predictive RMR equations accurately predict REE in the athletic populations. The purpose of the study was to compare 12 prediction equations (Harris-Benedict, Mifflin, Schofield, Cunningham, Owen, Liu’s, De Lorenzo) with measured RMR in Turkish national team athletes and sedentary controls. A total of 97 participants, 49 athletes (24 females, 25 males), and 48 sedentary (28 females, 20 males), were recruited from Turkey National Olympic Teams at the Ministry of Youth and Sports. RMR was measured using a Fitmate GS (Cosmed, Italy). The results of each 12 prediction formulas were compared with the measured RMR using paired t-test. The Bland-Altman plot was performed to determine the mean bias and limits of agreement between measured and predicted RMRs. Stratification according to sex, the measured RMR was greater in athletes compared to controls. The closest equation to the RMR measured by Fitmate GS was the Harris-Benedict equation in male athletes (mean difference -8.9 (SD 257.5) kcal/day), and Liu’s equation [mean difference -16.7 (SD 195.0) kcal/day] in female athletes. However, the intra-class coefficient (ICC) results indicated that all equations, including Harris-Benedict for male athletes (ICC = 0.524) and Liu’s for female athletes (ICC = 0.575), had a moderate reliability compared to the measured RMR. In sedentary subjects, the closest equation to the measured RMR is the Nelson equation in males, with the lowest RMSE value of 118 kcal/day [mean difference: 10.1 (SD 117.2) kJ/day], whereas, in females, all equations differ significantly from the measured RMR. While Nelson (ICC = 0.790) had good and Owen (ICC = 0.722) and Mifflin (calculated using fat-free mass) (ICC = 0.700) had moderate reliability in males, all predictive equations showed poor reliability in females. The results indicate that the predictive RMR equations failed to accurately predict RMR levels in the participants. Therefore, it may not suitable to use them in determining total energy expenditure.

Four Weeks of Classical Altitude Training Increases Resting Metabolic Rate in Highly Trained Middle-Distance Runners

2017 ◽  
Vol 27 (1) ◽  
pp. 83-90 ◽  
Author(s):  
Amy L. Woods ◽  
Avish P. Sharma ◽  
Laura A. Garvican-Lewis ◽  
Philo U. Saunders ◽  
Anthony J. Rice ◽  
...  
Keyword(s):  
Metabolic Rate ◽  
Endurance Training ◽  
Energy Intake ◽  
Resting Metabolic Rate ◽  
P Value ◽  
Altitude Training ◽  
Moderate Altitude ◽  
Distance Runners ◽  
Training Adaptation ◽  
Altitude Exposure

High altitude exposure can increase resting metabolic rate (RMR) and induce weight loss in obese populations, but there is a lack of research regarding RMR in athletes at moderate elevations common to endurance training camps. The present study aimed to determine whether 4 weeks of classical altitude training affects RMR in middle-distance runners. Ten highly trained athletes were recruited for 4 weeks of endurance training undertaking identical programs at either 2200m in Flagstaff, Arizona (ALT, n = 5) or 600m in Canberra, Australia (CON, n = 5). RMR, anthropometry, energy intake, and hemoglobin mass (Hbmass) were assessed pre- and posttraining. Weekly run distance during the training block was: ALT 96.8 ± 18.3km; CON 103.1 ± 5.6km. A significant interaction for Time*Group was observed for absolute (kJ.day-1) (F-statistic, p-value: F(1,8)=13.890, p = .01) and relative RMR (F(1,8)=653.453, p = .003) POST-training. No significant changes in anthropometry were observed in either group. Energy intake was unchanged (mean ± SD of difference, ALT: 195 ± 3921kJ, p = .25; CON: 836 ± 7535kJ, p = .75). A significant main effect for time was demonstrated for total Hbmass (g) (F(1,8)=13.380, p = .01), but no significant interactions were observed for either variable [Total Hbmass (g): F(1,8)=1.706, p = .23; Relative Hbmass (g.kg-1): F(1,8)=0.609, p = .46]. These novel findings have important practical application to endurance athletes routinely training at moderate altitude, and those seeking to optimize energy management without compromising training adaptation. Altitude exposure may increase RMR and enhance training adaptation,. During training camps at moderate altitude, an increased energy intake is likely required to support an increased RMR and provide sufficient energy for training and performance.

Endurance training increases metabolic rate and norepinephrine appearance rate in older individuals

1991 ◽  
Vol 261 (2) ◽  
pp. E233-E239 ◽  
Author(s):  
E. T. Poehlman ◽  
E. Danforth
Keyword(s):  
Blood Pressure ◽  
Body Composition ◽  
Metabolic Rate ◽  
Endurance Training ◽  
Energy Intake ◽  
Resting Metabolic Rate ◽  
Older Individuals ◽  
Supine Blood Pressure ◽  
Appearance Rate ◽  
Before And After

We examined the effects of an 8-wk endurance training program (cycling exercise) on resting metabolic rate (RMR) and norepinephrine (NE) kinetics in 19 older persons (64 +/- 1.6 yr). Before and after training, RMR, NE kinetics, maximal O2 consumption (VO2max), body composition, supine blood pressure, estimated energy intake, and fasting levels of glucose, insulin, and thyroid hormones were measured. RMR increased 10% after training. Resting concentrations of NE increased 24% after training due to a 21% increase in NE appearance rate and no change in NE clearance. Training increased VO2max (14%; P less than 0.01) and energy intake (12%; P less than 0.01), whereas no change was noted in body composition. Supine blood pressure and plasma glucose were lower after training, whereas no change was noted in fasting levels of plasma insulin. The increase in RMR was associated with a higher rate of NE appearance (r = 0.57; P = 0.05) and with increase in energy intake (r = 0.56; P = 0.05). Together these two factors accounted for 49% (r2) of the variation of the change in RMR. Changes in blood pressure were not associated with changes in NE kinetics. We conclude that endurance training increases total energy expenditure in older individuals by the direct energy cost of physical activity and by elevating RMR. This increase is partially mediated by an increased NE appearance rate and increased food intake in healthy older individuals.

Energy Balance and Energy Availability During a Selection Course for Belgian Paratroopers

2021 ◽  
Author(s):  
Patrick Mullie ◽  
Pieter Maes ◽  
Laurens van Veelen ◽  
Damien Van Tiggelen ◽  
Peter Clarys
Keyword(s):  
Physical Activity ◽  
Energy Balance ◽  
Energy Expenditure ◽  
Metabolic Rate ◽  
Energy Intake ◽  
Rest Metabolic Rate ◽  
Negative Energy ◽  
Fat Free Mass ◽  
Negative Energy Balance ◽  
Energy Availability

ABSTRACT Introduction Adequate energy supply is a prerequisite for optimal performances and recovery. The aims of the present study were to estimate energy balance and energy availability during a selection course for Belgian paratroopers. Methods Energy expenditure by physical activity was measured with accelerometer (ActiGraph GT3X+, ActiGraph LLC, Pensacola, FL, USA) and rest metabolic rate in Cal.d−1 with Tinsley et al.’s equation based on fat-free mass = 25.9 × fat-free mass in kg + 284. Participants had only access to the French individual combat rations of 3,600 Cal.d−1, and body fat mass was measured with quadripolar impedance (Omron BF508, Omron, Osaka, Japan). Energy availability was calculated by the formula: ([energy intake in foods and beverages] − [energy expenditure physical activity])/kg FFM−1.d−1, with FFM = fat-free mass. Results Mean (SD) age of the 35 participants was 25.1 (4.18) years, and mean (SD) percentage fat mass was 12.0% (3.82). Mean (SD) total energy expenditure, i.e., the sum of rest metabolic rate, dietary-induced thermogenesis, and physical activity, was 5,262 Cal.d−1 (621.2), with percentile 25 at 4,791 Cal.d−1 and percentile 75 at 5,647 Cal.d−1, a difference of 856 Cal.d−1. Mean daily energy intake was 3,600 Cal.d−1, giving a negative energy balance of 1,662 (621.2) Cal.d−1. Mean energy availability was 9.3 Cal.kg FFM−1.d−1. Eleven of the 35 participants performed with a negative energy balance of 2,000 Cal.d−1, and only five participants out of 35 participants performed at a less than 1,000 Cal.d−1 negative energy balance level. Conclusions Energy intake is not optimal as indicated by the negative energy balance and the low energy availability, which means that the participants to this selection course had to perform in suboptimal conditions.

scholarly journals Adherence to a Low Carbohydrate Diet may Modify Resting Metabolic Rate among Overweight and Obese Women

2020 ◽  
Author(s):  
Seyedeh Forough Sajjadi ◽  
Atieh Mirzababaei ◽  
nasim Ghodoosi ◽  
Sara Pooyan ◽  
Hana Arghavani ◽  
...  
Keyword(s):  
Metabolic Rate ◽  
Resting Metabolic Rate ◽  
Fat Free Mass ◽  
Obese Women ◽  
Carbohydrate Diet ◽  
Cross Sectional ◽  
Low Carbohydrate Diet ◽  
Low Carbohydrate ◽  
Carb Diet ◽  
Adjusted Model

Abstract Objective Resting metabolic rate (RMR) accounts for most of the daily energy expenditure. The low-carb diet attenuates decreases in RMR. This study aims to investigate the relationship between a low-carb diet and resting metabolic rate status. Methods We enrolled 304 overweight and obese women in this cross-sectional study. BMI, fat mass, fat-free mass, visceral fat, insulin level were assessed. RMR was measured using indirect calorimetry. A low carbohydrate diet score was measured using a validated semi-quantitative food frequency questionnaire (FFQ). Results Our results showed no relationship between LCDS and DNR even after adjust for confounders (Inc. RMR: OR: 0.97; 95% CI: 0.92–1.01, P = 0.20; Dec. RMR: OR: 0.97; 95% CI: 0.94-1.00, P = 0.14). Some components of LCDS had significant differences with DNR, such as carbohydrate and Dec. RMR in adjusted model (OR: 1.62; 95% CI: 0.98–1.37, P = 0.08), MUFA and Dec. RMR in adjusted model (OR: 0.48; 95% CI: 0.21–1.10, P = 0.08) and refined grain and Inc. RMR in crude model (OR: 0.87; 95% CI: 0.77–0.99, P = 0.04). Conclusion Our study showed that there is no association between a low-carb diet and RMR status but carbohydrate, MUFA, and refined grain had a significant relationship.

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