An objective measure of energy intake using the principle of energy balance.

Estimating human energy intake using mathematical models , ,

Prolonged low energy availability, which is the underpinning aetiology of the Relative Energy Deficiency in Sport and the Female and Male Athlete Triad frameworks, can have unfavourable impacts on both health and performance in athletes. Energy availability is calculated as energy intake minus exercise energy expenditure, expressed relative to fat free mass. The current measurement of energy intake is recognized as a major limitation for assessing energy availability due to its reliance on self-report methods, in addition to its short-term nature. This article introduces the application of the energy balance method for the measurement of energy intake, within the context of energy availability. The energy balance method requires quantification of the change in body energy stores over time, with concurrent measurement of total energy expenditure. This provides an objective calculation of energy intake, which can then be used for the assessment of energy availability. This approach, the Energy Availability - Energy Balance (EAEB) method, increases the reliance on objective measurements, provides an indication of energy availability status over longer periods and removes athlete burden to self-report energy intake. Implementation of the EAEB method could be used to objectively identify and detect low energy availability, with implications for the diagnosis and management of Relative Energy Deficiency in Sport and the Female and Male Athlete Triad.

Inability to match energy intake with energy expenditure at sustained near-maximal rates of energy expenditure in older men during a 14-d cycling expedition

Short sleep duration has been recognized as a risk factor for obesity. Whether extending sleep duration may mitigate this risk remains unknown. To determine the effects of a sleep extension intervention on objectively assessed energy intake, energy expenditure, and body weight in real-life settings among adults with overweight who habitually curtailed their sleep duration. This single-center, randomized clinical trial was conducted from November 1, 2014, to October 30, 2020. Participants were adults aged 21 to 40 years with a body mass index (calculated as weight in kilograms divided by height in meters squared) between 25.0 and 29.9 and had habitual sleep duration of less than 6.5 hours per night. Data were analyzed according to the intention-to-treat principle. After a 2-week habitual sleep period at baseline, participants were randomized to either an individualized sleep hygiene counseling session that was intended to extend their bedtime to 8.5 hours (sleep extension group) or to continue their habitual sleep (control group). All participants were instructed to continue daily routine activities at home without any prescribed diet or physical activity. The primary outcome was change in energy intake from baseline, which was objectively assessed as the sum of total energy expenditure and change in body energy stores. Total energy expenditure was measured by the doubly labeled water method. Change in body energy stores was computed using regression of daily home weights and body composition changes from dual-energy x-ray absorptiometry. Sleep duration was monitored by actigraphy. Changes from baseline were compared between the 2 groups using intention-to-treat analysis. Data from 80 randomized participants (mean [SD] age, 29.8 [5.1] years; 41 men [51.3%]) were analyzed. Sleep duration was increased by approximately 1.2 hours per night (95% CI, 1.0 to 1.4 hours; P < .001) in the sleep extension group vs the control group. The sleep extension group had a significant decrease in energy intake compared with the control group (-270 kcal/d; 95% CI, -393 to -147 kcal/d; P < .001). The change in sleep duration was inversely correlated with the change in energy intake (r = -0.41; 95% CI, -0.59 to -0.20; P < .001). No significant treatment effect in total energy expenditure was found, resulting in weight reduction in the sleep extension group vs the control group. This trial found that sleep extension reduced energy intake and resulted in a negative energy balance in real-life settings among adults with overweight who habitually curtailed their sleep duration. Improving and maintaining healthy sleep duration over longer periods could be part of obesity prevention and weight loss programs. ClinicalTrials.gov Identifier: NCT02253368.

Estimation of energy requirements in persons with severe central nervous system impairment

Validation of an inexpensive and accurate mathematical method to measure long-term changes in free-living energy intake

BackgroundSelf-report methods are widely used to assess energy intake but are prone to measurement errors. We aimed to identify under-reported, over-reported, and plausible self-reported energy intake by dietary recalls (rEI) using a standard method (Method 1) that calculates the rEI ratio against measured energy expenditure (mEE) by doubly-labeled water (DLW), and compare it to a novel method (Method 2), which calculates the rEI ratio against measured energy intake (mEI) by the principle of energy balance (EB = mEE + changes in energy stores).MethodsThe rEI:mEE and rEI:mEI ratios were assessed for each subject. Group cut-offs were calculated for both methods, using the coefficient of variations of rEI, mEE, and mEI. Entries within ± 1SD of the cutoffs were categorized as plausible, < 1SD as under-reported, and > 1SD as over-reported. Kappa statistics was calculated to assess the agreement between both methods. Percentage bias (bβ) was estimated by linear regression. Remaining bias (dβ) was calculated after applying each method cutoffs.ResultsThe percentage of under-reporting was 50% using both methods. Using Method 1, 40.3% of recalls were categorized as plausible, and 10.2% as over-reported. With Method 2, 26.3% and 23.7% recalls were plausible and over-reported, respectively. There was a significant positive relationship between mEI with weight (ß = 21.7, p < 0.01) and BMI (ß = 48.8, p = 0.04), but not between rEI with weight (ß = 13.1, p = 0.06) and BMI (ß = 41.8, p = 0.11). The rEI relationships were significant when only plausible entries were included using Method 1 (weight: ß = 17.4, p < 0.01, remaining bias = 49.5%; BMI: ß = 44.6, p = 0.01, remaining bias = 60.2%) and Method 2 (weight: ß = 19.5, p < 0.01, remaining bias = 24.9%; BMI: ß = 44.8, p = 0.03, remaining bias = 56.9%).ConclusionsThe choice of method significantly impacts plausible and over-reported classification, with the novel method identifying more over-reported entries. While rEI showed no relationships with anthropometric measurements, applying both methods reduced bias. The novel method showed greater bias reduction, suggesting that it may have superior performance when identifying plausible rEI.Clinical trials registrationNCT04465721.Graphical

A computational model to determine energy intake during weight loss

The energy expenditures (EE) of 23 adult male Marines were measured during a strenuous 11-day cold-weather field exercise at 2,200- to 2,550-m elevation by both doubly labeled water (2H2 18O, DLW) and intake balance methods. The DLW EE calculations were corrected for changes in baseline isotopic abundances in a control group that did not receive 2H2 18O. Intake balance EE was estimated from the change in body energy stores and food intake. Body energy-store changes were calculated from anthropometric [-1,574 +/- 144 (SE) kcal/day] and isotope dilution (-1,872 +/- 293 kcal/day) measurements made before and after the field exercise. The subjects kept daily logbook records of ration consumption (3,132 +/- 165 kcal/day). Mean DLW EE (4,919 +/- 190 kcal/day) did not differ significantly from intake balance EE estimated from food intake and either anthropometric (4,705 +/- 181 kcal/day) or isotope dilution (5,004 +/- 240 kcal/day) estimates of the change in body energy stores. The DLW method can be used with at least the same degree of confidence as the intake balance method to measure the EE of active free-living humans.

Total energy expenditure of healthy Swedish women during pregnancy and lactation

This study aimed to compare the similarities and differences among Food Frequency Questionnaire(FFQ), 24-hour dietary recall method, and weighing method in assessing the intakes of foods, energy, and nutrients in pregnant women, and to evaluate the validity of the FFQ. From June to September in 2017, a total of 82 pregnant women were recruited across the entire Wuqiang County in Hebei Province. The subjects were surveyed using the FFQ for the most recent week, 24-hour dietary recall for 3 consecutive days, and in-home weighing for 3 consecutive days. The differences in the average daily intakes of food, energy, and nutrients obtained by the FFQ and the other two method were calculated and compared. The correlation and consistency between the FFQ and the other two method were evaluated through the Spearman correlation coefficient and the Bland-Altman plot, respectively. There was no statistically significant difference between the FFQ and 24-hour dietary recall method in assessing the average daily intakes of soybeans and their products, fruits, dairy products and nuts. The intakes of fruits and dairy products evaluated by the two method were all positively correlated(r=0.34-0.56, P&lt;0.05). There was no statistically significant difference between the FFQ and the weighing method in assessing the average daily intakes of cereals, soybeans and their products, vegetables, fruits, aquatic products, eggs, dairy products and nuts. Among them, the intakes obtained by the two method for cereals, vegetables and nuts were all positively correlated(r=0.53-0.58, P&lt;0.05). There was no statistically significant difference between the FFQ and 24-hour dietary recall method in evaluating average daily intakes of energy, protein, carbohydrates, the ratio of carbohydrates to energy supply, dietary fiber, total vitamin A, thiamine, riboflavin, vitamin C, calcium, phosphorus, potassium, magnesium, iron and zinc. Among them, intakes of energy, protein, fat and carbohydrates were all positively correlated(r=0.41-0.58, P&lt;0.05). There was no statistically significant difference between FFQ and weighing method in evaluating the average daily intakes of energy, fat, the ratio of fat to energy supply, carbohydrates, the ratio of carbohydrates to energy supply, total vitamin A, thiamine, riboflavin, vitamin C, calcium, potassium, sodium and magnesium. Among them, the intakes of energy, protein and carbohydrates obtained by the two method were all positively correlated(r=0.49-0.52, P&lt;0.05). Bland-Altman analysis showed that for most subjects, the intakes of energy, protein, fat and carbohydrates were within the 95% consistency limits between FFQ and 24-hour recall method(91.1%-94.9%)or weighing method(92.9%-100%). FFQ can relatively accurately evaluate the intakes of food and nutrients among pregnant women.

The effects of regular physical activity on energy intake in obese adolescents are unknown. The objective is to determine how physical activity interventions affect energy and macronutrient intake in overweight/obese youth. Databases were searched from December 2014 to December 2015 for studies that measured energy and/or macronutrient consumption in response to physical activity intervention in overweight/obese youth. The review comprises primary source articles published in English in peer-reviewed journals. Articles that presented data on energy and/or macronutrient intake before and after a physical activity intervention (without dietary restriction) in overweight or obese children and teenagers (up to 18 years old) were included. Of the initial 307 references found, nine were included. The nine included studies analysed the effect of 15 different physical activity interventions. Nine showed a decrease and six unchanged energy intakes. The effect size for total energy intake ranged from -2.108 to -0.207 (n = 14). Results of the meta-analysis revealed a mean effect of physical intervention to reduce intake of -1.003 (95% confidence interval = -1.261 to -0.745, p < 0.001). Results for heterogeneity among these studies were I2 = 67.421; Q = 39.903; df = 13, p < 0.001. The mean energy intake reduction was -323 ± 286 kcal. Macronutrient intake was assessed in 11 interventions. Protein intake was found decreased in five (reduction of -26.8 ± 19.2 g), seven reported fat decrease (reduction of -26.4 ± 17.8 g) and five a decrease in CHO (reduction of -72.5 ± 22.8 g). The meta-analysis revealed significant decreases of each macronutrient (p < 0.001). Structured physical activity interventions favour decreased daily energy intake in obese adolescents.

Because of the improved survival rate, both short term and long term adverse effects of breast cancer treatment have become increasingly important. Body weight and body composition before, during, and after chemotherapy may influence side effects during treatment and survival. The aims of this thesis were to assess among stage I-IIIB breast cancer patients: 1) the association between pre-treatment body composition and dose-limiting toxicities during chemotherapy, 2) potential changes in body weight and body composition during and after chemotherapy compared to changes in age-matched women without cancer in the same time period, and 3) dietary intake during chemotherapy compared to age-matched women without cancer in the same time period. Chapter 2 describes the association between pre-treatment body composition and dose-limiting toxicities during chemotherapy. Data from 172 breast cancer patients who participated in the COBRA-study were analysed. Body composition was measured using a total body Dual Energy X-ray Absorption (DEXA) scan. Information regarding dose-limiting toxicities was abstracted from medical records. A higher BMI (kg/m2) and a higher fat mass (kg and percentage) were associated with an increased risk of dose-limiting toxicity, while lean body mass (kg) was not associated with risk of toxicities. Chapter 3 presents the findings of a meta-analysis on changes in body weight during chemotherapy in breast cancer patients. The meta-analysis showed an overall gain in body weight of 2.7 kg (95% CI: 2.0-3.3) during chemotherapy, with a high degree of heterogeneity (I2= 94.2%). Weight gain in breast cancer patients was more pronounced in papers published before 2000 and studies including cyclophosphamide, methotrexate and 5-fluorouracil as chemotherapy regime. Chapter 4 describes changes in body weight and body composition during and after chemotherapy. Data from 145 patients and 121 women of an age-matched comparison group, participating in the COBRA-study were analysed. Body composition was measured using DEXA-scan at three time points during the study period. For the patient group, these tie points were: before start of chemotherapy, shortly after chemotherapy, and 6 months after chemotherapy. For the comparison group these measurements were conducted over a similar time frame: baseline, 6 months after baseline, and 12 months after baseline. In addition, we identified determinants of changes in body weight and body composition. Shortly after chemotherapy, patients had a significantly higher body weight, BMI, and lean body mass than women in the comparison group, while fat mass was similar. Six months after chemotherapy no differences in body weight or body composition were observed between the patient and comparison group. A younger age, better appetite during chemotherapy, and an ER-receptor negative tumour were associated with greater changes in body weight over time. A younger age and better appetite during chemotherapy were associated with greater changes in fat mass over time, while the only determinant associated with greater changes in lean body mass over time was a better appetite during chemotherapy. Chapter 5 describes the dietary intake and food groups before and during chemotherapy of breast cancer patients compared with women without cancer. In addition we assessed the association between symptoms and energy intake. Data from 117 breast cancer patients and 88 women without breast cancer who participated in the COBRA-study were used. Habitual dietary intake before chemotherapy was assessed using a food frequency questionnaire. Two 24-hr dietary recalls were used to assess actual dietary intake during chemotherapy for patients and within 6 months for the comparison group. Shortly after the 24-hr dietary recall, participants filled out questionnaires about symptoms. Before chemotherapy, dietary intake was similar for both groups. During chemotherapy, breast cancer patients reported significantly lower total energy, total fat, total protein, and alcohol intake than women without cancer, which could be explained by a lower intake of specific food groups. Overall results from this thesis suggest that pre-treatment fat mass is associated with dose-limiting toxicities during chemotherapy. Weight gain during chemotherapy appeared to be more modest than we expected based on literature and changes in body composition during chemotherapy consist mainly of an increase in lean body mass, which is only temporary and returned to baseline within 6 months after chemotherapy. A higher appetite during chemotherapy was associated with changes in body weight and body composition. A younger age at diagnosis was associated with greater changes in body weight and fat mass, but not with changes in lean body mass. In addition, an ER-receptor negative tumour was associated with greater changes in body weight, but not with changes in fat mass or lean body mass. During chemotherapy women with breast cancer have a lower intake of energy, fat, protein and alcohol compared to age-matched women without cancer, which was expressed in a lower intake of specific food groups. The results of this thesis do not suggest that dietary intake is associated with weight gain during chemotherapy.

Energy expenditure measurements in relation to energy requirements

To compare, in mice, the accuracy of estimates of energy expenditure (EE) using an energy balance technique (TEEbal: food energy intake and body composition change) vs indirect calorimetry (TEEIC). In 32 male C57BL/6J mice, EE was estimated using an energy balance (caloric intake minus change in body energy stores) method over a 37-day period. EE was also measured in the same animals by indirect calorimetry. These measures were compared. The two methods were highly correlated (r(2)=0.87: TEEbal=1.07*TEEIC-0.22, P<0.0001). By Bland-Altman analysis, TEEbal estimates were slightly higher (4.6±1.5%; P<0.05) than TEEIC estimates (Bias=0.55 kcal per 24 h). TEEbal can be performed in 'home cages' and provides an accurate integrated long-term measurement of EE while minimizing potentially confounding stress that may accompany the use of indirect calorimetry systems. The technique can also be used to assess long-term energy intake.