Active Women before/after an Intervention Designed to Restore Menstrual Function: Resting Metabolic Rate and Comparison of Four Methods to Quantify Energy Expenditure and Energy Availability

2014 ◽  
Vol 24 (1) ◽  
pp. 37-46 ◽  
Author(s):  
Charlotte P. Guebels ◽  
Lynn C. Kam ◽  
Gianni F. Maddalozzo ◽  
Melinda M. Manore
Keyword(s):  
Energy Expenditure ◽  
Metabolic Rate ◽  
Resting Metabolic Rate ◽  
Future Research ◽  
Energy Availability ◽  
Menstrual Dysfunction ◽  
Exercise Energy Expenditure ◽  
Gynecological Age ◽  
Active Controls ◽  
Low Energy Availability

It is hypothesized that exercise-related menstrual dysfunction (ExMD) results from low energy availability (EA), defined as energy intake (EI)—exercise energy expenditure (EEE). When EI is too low, resting metabolic rate (RMR) may be reduced to conserve energy.Purpose:To measure changes in RMR and EA, using four methods to quantify EEE, before/after a 6-month diet intervention aimed at restoring menses in women with ExMD; eumenorrheic (Eumen) active controls (n = 9) were also measured.Methods:Active women with ExMD (n = 8) consumed +360 kcal/d (supplement) for 6 months; RMR was measured 2 times at 0 months/6 months. EI and total energy expenditure (TEE) were estimated using 7-day diet/activity records, with EA assessed using four methods to quantify EEE.Results:At baseline, groups did not differ for age, gynecological age, body weight, lean/fat mass, VO2max, EI and EA, but mean TEE was higher in ExMD (58.3 ± 4.4kcal/kgFFM/d; Eumen = 50.6 ± 2.4; p < .001) and energy balance (EB) more negative (–10.3 ± 6.9 kcal/kgFFM/d; Eumen=-3.0 ± 9.7; p = .049). RMR was higher in ExMD (31.3 ± 1.8 kcal/kgFFM/d) vs. Eumen (29.1 ± 1.9; p < .02). The intervention increased weight (1.6 ± 2.0kg; p = .029), but there were no significant changes in EA (0-month range = 28.2–36.7 kcal/kgFFM/d; 6-month range = 30.0–45.4; p > .05), EB (6 months = –0.7 ± 15.1 kcal/kgFFM/d) or RMR (0 months = 1515 ± 142; 6 months = 1522 ± 134 kcal/d). Assessment of EA varied dramatically (~30%) by method used.Conclusions:For the ExMD group, EI and weight increased with +360 kcal/d for 6 months, but there were no significant changes in EB, EA or RMR. No threshold EA value was associated with ExMD. Future research should include TEE, EB and clearly quantifying EEE (e.g.,>4 MET) if EA is measured.

scholarly journals Accuracy of resting metabolic rate prediction equations in female rugby players

2020 ◽  
Vol 79 (OCE2) ◽  
Author(s):  
Jack O'Neill ◽  
Ciara Walsh ◽  
Senan McNulty ◽  
Martha Corish ◽  
Hannah Gantly ◽  
...  
Keyword(s):  
Metabolic Rate ◽  
Muscle Damage ◽  
Resting Metabolic Rate ◽  
Low Energy ◽  
Strenuous Exercise ◽  
Energy Availability ◽  
Prediction Equations ◽  
Individual Level ◽  
Rugby Players ◽  
Low Energy Availability

AbstractThis study aimed to investigate (1) the accuracy of resting metabolic rate (RMR) prediction equations in female rugby players on a group and individual level; and (2) whether individual differences in the accuracy of prediction equations is associated with muscle damage or energy availability.RMR was assessed in 14 female provincial and club rugby players (Age: 20–34 years, FFM: 47–63 kg, FM: 15–37%) training a minimum of twice per week. Participants attended the laboratory following an overnight fast and having avoided strenuous exercise for 24 hours. RMR was measured over 30 minutes by indirect calorimetry, and taken as the 10 minutes with the lowest variation. Body composition was assessed by air displacement plethysmography, muscle damage indicated by creatine kinase (CK) and risk of low energy availability assessed by the Low Energy Availability in Females Questionnaire. Accuracy of RMR prediction equations relevant to the general population and athletes were assessed including the Harris Benedict (1919), Cunningham (1980) and Ten Haaf FFM (2014) based equations.Measured RMR was 1748 ± 146 kcal/day (range: 1474–2010 kcal/day). Predicted RMR determined by the Harris-Benedict equation (1601 ± 120 kcal/day) was significantly lower than measured RMR (p < 0.001), whereas predicted RMR using the Cunningham (1753 ± 146 kcal/day, p = 0.89) and the Ten Haaf (1781 ± 115 kcal/day, p = 0.33) equations did not differ from measured RMR. On an individual level, 50% (n = 7), 86% (n = 12) and 79% (n = 11) of participants fell within 10% of the measured RMR value when RMR was predicted by Harris-Benedict, Cunningham and Ten Haaf equations respectively. CK values were 182 ± 155U/L (range: 25–490U/L). When correlations of the whole group were studied, the difference between predicted and measured RMR was not associated with CK (r = 0.13). However, in the two individuals who fell outside the 10% range of that predicted by the Cunningham equation, one above and one below, CK values were 428U/L and 166U/L respectively. Muscle damage (as indicated by a high CK value) could therefore be one potential explanation for the higher measured RMR in the individual who was above the Cunningham predicted value.In this cohort of female rugby players, the Cunningham equation showed the best accuracy on a group and individual level, suggesting this may be the most suitable prediction equation for this population. Further studies with larger sample sizes and investigating underlying reasons for why RMR measured values may differ from predicted values are needed.

scholarly journals Prevalence of Low Energy Availability in Competitively Trained Male Endurance Athletes

Medicina ◽  
2019 ◽  
Vol 55 (10) ◽  
pp. 665 ◽  
Author(s):  
Lane ◽  
Hackney ◽  
Smith-Ryan ◽  
Kucera ◽  
Registar-Mihalik ◽  
...  
Keyword(s):  
Resting Metabolic Rate ◽  
Relative Energy ◽  
Low Energy ◽  
Endurance Athletes ◽  
Risk Level ◽  
Future Research ◽  
Energy Availability ◽  
Energy Deficiency ◽  
Cut Points ◽  
Low Energy Availability

Background and Objectives: Relative energy deficiency in sport (RED-S) has been introduced as a broad-spectrum syndrome leading to possible dysfunction in numerous physiological systems, driven primarily by low energy availability (EA). Research in females has identified specific EA cut-points indicative of risk level for developing physiological and performance disturbances. Cut-points in males have yet to be evaluated. This study examined the prevalence of low EA in competitive (non-elite), recreationally trained (CRT) male endurance athletes. Materials and Methods: Subjects were 108 CRT (38.6 ± 13.8 y; 12.2 ± 5.4 h/wk training) male endurance athletes (runners, cyclists, triathletes) who completed a descriptive survey online via Qualtrics® and returned 3 day diet and exercise training records. EA was calculated from returned surveys and training records. Resting metabolic rate (RMR) and lean body mass (LBM) were estimated from self-reported survey data. Prevalence of risk group was categorized based on the female cut-points: at risk (AR) ≤30 kcal/kg LBM, moderate risk (MR) = 30–45 kcal/kg LBM, or no risk (NR) ≥45 kcal/kg LBM. Results: In this sample, 47.2% (n = 51) were classified as AR, 33.3% (n = 36) as MR, and 19.4% (n = 21) as NR for low EA. Cyclists had lower EA (26.9 ± 17.4 kcal/kg LBM, n = 45) than runners (34.6 ± 13.3 kcal/kg LBM, n = 55, p = 0.016) and all other sport categories (39.5 ± 19.1 kcal/kg LBM, n = 8, p = 0.037). Conclusions: The findings indicate this sample had a high prevalence of risk for low EA, at 47.2%. Only 19.4% of participants were at no risk, meaning ~80% of participants were at some degree of risk of experiencing low EA. Cyclists were at greater risk in this cohort of low EA, although why this occurred was unclear and is in need of further investigation. Future research should address whether the current female cut-points for low EA are appropriate for use in male populations.

scholarly journals Male Flat Jockeys Do Not Display Deteriorations in Bone Density or Resting Metabolic Rate in Accordance With Race Riding Experience: Implications for RED-S

2018 ◽  
Vol 28 (4) ◽  
pp. 434-439 ◽  
Author(s):  
George Wilson ◽  
Dan Martin ◽  
James P. Morton ◽  
Graeme L. Close
Keyword(s):  
Lumbar Spine ◽  
Metabolic Rate ◽  
Bone Health ◽  
Resting Metabolic Rate ◽  
Low Energy ◽  
Fat Free Mass ◽  
Published Data ◽  
Energy Availability ◽  
Mineral Density ◽  
Low Energy Availability

Despite consistent reports of poor bone health in male jockeys, it is not yet known if this is a consequence of low energy availability or lack of an osteogenic stimulus. Given the rationale that low energy availability is a contributing factor in low bone health, we tested the hypothesis that both hip and lumbar bone mineral density (BMD) should progressively worsen in accordance with the years of riding. In a cross-sectional design, male apprentice (n = 17) and senior (n = 14) jockeys (matched for body mass and fat-free mass) were assessed for hip and lumbar spine BMD, as well as both measured and predicted resting metabolic rate (RMR). Despite differences (p < .05) in years of race riding (3.4 ± 2 vs. 16.3 ± 6.8), no differences were apparent (p > .05) in hip (−0.9 ± 1.1 vs. −0.8 ± 0.7) and lumbar Z-scores (−1.3 ± 1.4 vs. −1.5 ± 1) or measured RMR (1,459 ± 160 vs. 1,500 ± 165 kcal/day) between apprentices and senior jockeys, respectively. Additionally, years of race riding did not demonstrate any significant correlations (p > .05) with either hip or lumbar spine BMD. Measured RMR was also not different (p > .05) from predicted RMR in either apprentice (1,520 ± 44 kcal/day) or senior jockeys (1,505 ± 70 kcal/day). When considered with previously published data examining underreporting of energy intake and direct assessments of energy expenditure, we suggest that low BMD in jockeys is not due to low energy availability per se but rather the lack of an osteogenic stimulus associated with riding.

Determination of total energy expenditure, resting metabolic rate and physical activity in lean and overweight people

1997 ◽  
Vol 36 (4) ◽  
pp. 310-312 ◽  
Author(s):  
F. Thielecke ◽  
J. Möseneder ◽  
A. Kroke ◽  
K. Klipstein-Grobusch ◽  
H. Boeing ◽  
...  
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Metabolic Rate ◽  
Total Energy ◽  
Resting Metabolic Rate ◽  
Total Energy Expenditure ◽  
Overweight People

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.

Energy Expenditure of Level Overground Walking in Young Adults: Comparison With Prediction Equations

2021 ◽  
pp. 1-8
Author(s):  
Jingjing Xue ◽  
Shuo Li ◽  
Rou Wen ◽  
Ping Hong
Keyword(s):  
Young Adults ◽  
Energy Expenditure ◽  
Metabolic Rate ◽  
Energy Cost ◽  
Sports Medicine ◽  
Resting Metabolic Rate ◽  
Prediction Equations ◽  
Overground Walking ◽  
All Speeds ◽  
Walking Energy Cost

Background: The purpose of this study was to investigate the accuracy of the published prediction equations for determining level overground walking energy cost in young adults. Methods: In total, 148 healthy young adults volunteered to participate in this study. Resting metabolic rate and energy expenditure variables at speeds of 4, 5, and 6 km/h were measured by indirect calorimetry, walking energy expenditure was estimated by 3 published equations. Results: The gross and net metabolic rate per mile of level overground walking increased with increased speed (all P < .01). Females were less economical than males. The present findings revealed that the American College of Sports Medicine and Pandolf et al equations significantly underestimated the energy cost of overground walking at all speeds (all P < .01) in young adults. The percentage mean bias for American College of Sports Medicine, Pandolf et al, and Weyand et al was 12.4%, 16.8%, 1.4% (4 km/h); 21.6%, 15.8%, 7.1% (5 km/h); and 27.6%, 12%, 6.6% (6 km/h). Bland–Altman plots and prediction error analysis showed that the Weyand et al was the most accurate in 3 existing equations. Conclusions: The Weyand et al equation appears to be the most suitable for the prediction of overground walking energy expenditure in young adults.

scholarly journals Dietary Intake and Energy Expenditure in Breast Cancer Survivors: A Review

Nutrients ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 3394
Author(s):  
Sarah A. Purcell ◽  
Ryan J. Marker ◽  
Marc-Andre Cornier ◽  
Edward L. Melanson
Keyword(s):  
Breast Cancer ◽  
Physical Activity ◽  
Energy Balance ◽  
Energy Expenditure ◽  
Dietary Intake ◽  
Metabolic Rate ◽  
Cancer Survivors ◽  
Breast Cancer Survivors ◽  
Resting Metabolic Rate ◽  
Fat Free Mass

Many breast cancer survivors (BCS) gain fat mass and lose fat-free mass during treatment (chemotherapy, radiation, surgery) and estrogen suppression therapy, which increases the risk of developing comorbidities. Whether these body composition alterations are a result of changes in dietary intake, energy expenditure, or both is unclear. Thus, we reviewed studies that have measured components of energy balance in BCS who have completed treatment. Longitudinal studies suggest that BCS reduce self-reported energy intake and increase fruit and vegetable consumption. Although some evidence suggests that resting metabolic rate is higher in BCS than in age-matched controls, no study has measured total daily energy expenditure (TDEE) in this population. Whether physical activity levels are altered in BCS is unclear, but evidence suggests that light-intensity physical activity is lower in BCS compared to age-matched controls. We also discuss the mechanisms through which estrogen suppression may impact energy balance and develop a theoretical framework of dietary intake and TDEE interactions in BCS. Preclinical and human experimental studies indicate that estrogen suppression likely elicits increased energy intake and decreased TDEE, although this has not been systematically investigated in BCS specifically. Estrogen suppression may modulate energy balance via alterations in appetite, fat-free mass, resting metabolic rate, and physical activity. There are several potential areas for future mechanistic energetic research in BCS (e.g., characterizing predictors of intervention response, appetite, dynamic changes in energy balance, and differences in cancer sub-types) that would ultimately support the development of more targeted and personalized behavioral interventions.

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