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.

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 Energy intake, physical activity and body weight: a simulation model

1995 ◽  
Vol 73 (3) ◽  
pp. 337-347 ◽  
Author(s):  
Klaas R. Westerterp ◽  
Jeroen H. H. L. M. Donkers ◽  
Elisabeth W. H. M. Fredrix ◽  
Piet oekhoudt
Keyword(s):  
Physical Activity ◽  
Body Composition ◽  
Body Weight ◽  
Energy Balance ◽  
Energy Expenditure ◽  
Dietary Intake ◽  
Energy Intake ◽  
Relative Size ◽  
The Body ◽  
Fat Free Mass

In adults, body mass (BM) and its components fat-free mass (FFM) and fat mass (FM) are normally regulated at a constant level. Changes in FM and FFM are dependent on energy intake (EI) and energy expenditure (EE). The body defends itself against an imbalance between EI and EE by adjusting, within limits, the one to the other. When, at a given EI or EE, energy balance cannot be reached, FM and FFM will change, eventually resulting in an energy balance at a new value. A model is described which simulates changes in FM and FFM using EI and physical activity (PA) as input variables. EI can be set at a chosen value or calculated from dietary intake with a database on the net energy of foods. PA can be set at a chosen multiple of basal metabolic rate (BMR) or calculated from the activity budget with a database on the energy cost of activities in multiples of BMR. BMR is calculated from FFM and FM and, if necessary, FFM is calculated from BM, height, sex and age, using empirical equations. The model uses existing knowledge on the adaptation of energy expenditure (EE) to an imbalance between EI and EE, and to resulting changes in FM and FFM. Mobilization and storage of energy as FM and FFM are functions of the relative size of the deficit (EI/EE) and of the body composition. The model was validated with three recent studies measuring EE at a fixed EI during an interval with energy restriction, overfeeding and exercise training respectively. Discrepancies between observed and simulated changes in energy stores were within the measurement precision of EI, EE and body composition. Thus the consequences of a change in dietary intake or a change in physical activity on body weight and body composition can be simulated.

scholarly journals Metabolic Adaptations to Weight Loss: Relative Changes in Resting Metabolic Rate and Energy Expenditure for Physical Activity and Association With Weight Loss Maintenance

2021 ◽  
Vol 5 (Supplement_2) ◽  
pp. 526-526
Author(s):  
Rachel Silver ◽  
Sai Das ◽  
Michael Lowe ◽  
Susan Roberts
Keyword(s):  
Physical Activity ◽  
Weight Loss ◽  
Linear Regression ◽  
Energy Expenditure ◽  
Metabolic Rate ◽  
Fat Mass ◽  
Weight Regain ◽  
Resting Metabolic Rate ◽  
Fat Free Mass ◽  
The Mean

Abstract Objectives There is persistent controversy over the extent to which different components of energy expenditure disproportionately decrease after weight loss and contribute to weight regain through decreased energy requirements. We conducted a secondary analysis of the CALERIE I study to test the hypothesis that decreased resting metabolic rate (RMR) and energy expenditure for physical activity (EEPA) after a 6-month calorie restriction intervention would predict weight regain at 12 months, with a greater decrease in RMR than EEPA. Methods Participants (n = 46) received all food and energy-containing beverages for 6 months. Outcome measures included total energy expenditure by doubly labeled water, RMR by indirect calorimetry, and body composition by BOD POD. Predictions for RMR and EEPA were derived from baseline linear regression models including age, sex, fat mass, and fat free mass. Baseline regression coefficients were used to calculate the predicted RMR and EEPA at 6 months. Residuals were calculated as the difference between measured and predicted values and were adjusted for body weight. The presence of metabolic adaptation was evaluated by a paired t-test comparing measured and predicted RMR at 6 months. Differences between 6-month RMR and EEPA residuals were evaluated by the same method. Linear regression was used to assess the association between 6-month residuals and weight loss maintenance (% weight change, 6 to 12 months). Results Mean weight loss was 6.9% at 6 months with 2.1% regain from 6 to 12 months. No adaptation in RMR was observed at 6 months (mean residual: 19 kcal; 95% confidence interval: −9, 48; P = 0.18). However, significant adaptation was observed in EEPA (mean residual: −199 kcal; −126, −272; P < 0.0001). In addition, the mean 6-month RMR residual was significantly greater than the mean 6-month EEPA residual (218 kcal; 133, 304; P < 0.0001). There was no significant association between 6-month RMR or EEPA residuals and weight regain at 12 months (P = 0.56, 0.34). Conclusions There was no measurable decrease in RMR with weight loss after adjusting for changes in fat free mass and fat mass, but there was a decrease in EEPA. Changes in RMR and EEPA with weight loss over 6 months did not predict weight regain at 12 months. Funding Sources Jean Mayer USDA Human Nutrition Research Center on Aging Doctoral Scholarship; USDA agreement #8050–51000-105–01S

scholarly journals A Personalized, Dynamic Physical Activity Intervention Is Feasible and Improves Energetic Capacity, Energy Expenditure, and Quality of Life in Breast Cancer Survivors

2021 ◽  
Vol 11 ◽  
Author(s):  
Tarah J. Ballinger ◽  
Sandra K. Althouse ◽  
Timothy P. Olsen ◽  
Kathy D. Miller ◽  
Jeffrey S. Sledge
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Physical Activity ◽  
Energy Expenditure ◽  
Cancer Survivors ◽  
Breast Cancer Survivors ◽  
Physical Activity Intervention ◽  
Movement Data ◽  
Baseline Activity

PurposeDespite survival and quality of life benefits associated with physical activity, many breast cancer survivors remain inactive. Effective, sustainable interventions must account for individual differences in capability, motivation, and environment. Here, we evaluate the feasibility, mechanics, and efficacy of delivering an individualized, dynamic intervention to increase energetic capacity and energy expenditure.MethodsStage 0–III breast cancer patients who had completed primary treatment were enrolled. Prior to the intervention, detailed movement data was collected with a wearable GPS and accelerometer for 3 weeks to establish baseline activity. Movement data was collected continuously throughout the 12-week intervention, during which patients received electronically delivered, tailored, dynamic activity “prescriptions”, adjusted based on demonstrated individual capability, daily movement in their environment, and progress.ResultsOf 66 enrolled, 57 participants began and completed the intervention. The intervention resulted in significant improvements in average steps (+558 steps/day, p = 0.01), energetic capacity measured by power generation on a stationary bicycle (1.76 to 1.99 W/kg lean mass, p < 0.01), and quality of life (FACT-B TOI, 72.8 to 74.8, p = 0.02). The greatest improvement in functional energetic capacity was seen in the lowest performing tertile at baseline (0.76 to 1.12 W/kg, p < 0.01).DiscussionWearable technology delivery of personalized activity prescriptions based on individual capability and movement behaviors demonstrates feasibility and early effectiveness. The high variability seen in baseline activity and function, as well as in response to the intervention, supports the need for future work in precision approaches to physical activity (NCT03158519).

scholarly journals Feasibility study of “BENECA mHealth”: a mobile system to improve quality of life via energy balance in breast cancer survivors (Preprint)

2019 ◽  
Author(s):  
Mario Lozano-Lozano ◽  
Irene Cantarero-Villanueva ◽  
Lydia Martin-Martin ◽  
Noelia Galiano-Castillo ◽  
Maria-José Sanchez ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Physical Activity ◽  
Energy Balance ◽  
Cancer Survivors ◽  
User Satisfaction ◽  
Breast Cancer Survivors ◽  
Rating Scale ◽  
Mobile App

BACKGROUND Energy balance is defined as the difference between energy expenditure and energy intake. The current state of knowledge supports the need to better integrate mechanistic approaches through effective studies of energy balance in cancer population, due to it is observed a significant lack of adherence to healthy lifestyle recommendations. In an attempt to stimulate changes in breast cancer survivors (BCS) lifestyles based on energy balance, our group developed BENECA mHealth application, which has been previously validated as a reliable energy balance monitoring system. OBJECTIVE Based on our previous results, the goal of this study was to investigate the feasibility of BENECA mHealth in an ecological clinical setting with breast cancer survivors, studying (1) its feasibility; and (2) pretest-posttest differences with regard to BCS’ lifestyles, quality of life (QoL), and physical activity (PA) motivation. METHODS Eighty BCS were enrolled in this prospective test-retest quasi-experimental study diagnosed with stage I to IIIA and with a body mass index over 25 kg/m2. Patients had to use BENECA mHealth for 8 weeks and were assessed at baseline and post-intervention period. Feasibility main outcomes included percentage of adoption, usage and attrition, user app-quality perception measured with the Mobile App Rating Scale (MARS), satisfaction with Net Promoter Score (NPS), and barriers and facilitators of its use. Clinical main outcomes included quality of life measure with EORT QLQ-C30, PA assess with accelerometry, PA motivation measure with the self-efficacy scale for physical activity (EAF), and body composition with a Dual-energy X-ray absorptiometry. Statistical (paired-sample t-tests was used) and Kaplan-Meier survival curve were analyzed. RESULTS BENECA was considered feasible by the BCS, in terms of use (76.3%; 58/76 BCS) adoption (69%; 80/116), and satisfaction (positive NPS). App quality score did not make it one of the best rated apps (3.71 ± 0.47 points out of 5). BENECA mHealth seems to improve the QoL of BCSs (global health mean difference (MD) 12.83, 95% CI 8.95–16.71, p<.001), as well as EAF score (global MD 36.99, 95% CI 25.52 – 48.46, p<.001), daily moderate-to-vigorous PA (MD 7.38, 95% CI 14.37–0.39, p=.039) and reduce body weight (MD -1.42, 95% CI -1.97 – -0.87, p<.001). CONCLUSIONS BENECA mHealth can be considered feasible in a real clinical context for being able to promote behavioral changes in the lifestyles of BCSs, but it needs to be optimized to improve user satisfaction with use and functionality. This study highlights the importance of the use of mobile applications based on energy balance and how the QoL of BCSs can be improved via monitoring.

Physical activity, strength and weight management in breast cancer survivors

2013 ◽  
Author(s):  
Shannon L. Mihalko ◽  
Samantha E. Yocke ◽  
Greg Russell ◽  
Marissa Howard-McNatt ◽  
Edward A. Levine
Keyword(s):  
Breast Cancer ◽  
Physical Activity ◽  
Cancer Survivors ◽  
Weight Management ◽  
Breast Cancer Survivors
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