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Some studies comparing persons with and without type 2 diabetes (T2DM) show no difference in resting energy expenditure (REE). However, the degree of glycemic control may be a crucial factor in determining energy requirements. Few studies have employed the doubly labeled water (DLW) method in persons with T2DM to objectively measure daily energy expenditure. To determine relationships between glycemia, body composition, and energy expenditure in adults with obesity and T2DM. We hypothesized that worse hyperglycemia, insulin resistance, and beta cell function would associate with higher resting and total energy expenditure (TEE). Two cohorts age 31–50 years were included: 78 with obesity and T2DM, 19 with normal weight and no chronic disease. Baseline data from clinical biomarkers, intravenous glucose tolerance tests, DXA and MRI for body composition, and dietary intakes were used in multivariable regression models to predict REE and TEE. Additionally, comparisons were made by categorizing participants as having controlled or uncontrolled glycemia based on glucose levels ≥175 mg/dL. REE was higher in participants with T2DM by 534.08 ± 74.35 kcal/d (p < 0.001). Higher fasting glucose and HbA1C levels associated with higher TEE. Abdominal SAT and VAT were also predictors in regression models accounting for 76 % of the variance in REE and 89 % of TEE. Participants with uncontrolled glycemia had 22 % higher adipose/lean ratio, two-fold higher VAT/SAT ratio, 21 % higher HOMA-IR score, and worse beta cell function (mean difference in HOMA2-%β of 74.09 ± 14.01, p < 0.001) than those with controlled glycemia. Both REE and TEE were significantly higher in uncontrolled glycemia, difference in REE of 154.17 ± 96.28 kcals/day (p = 0.04) and difference in TEE of 480.64 ± 215.45 kcals/day (p = 0.03). Poor beta cell function and uncontrolled glycemia associate with higher REE and TEE in persons with obesity and T2DM. This study is registered with clinicaltrials.gov identifier: NCT01239550. [ABSTRACT FROM AUTHOR]

To determine the thermic effect of food (TEF) in a Chinese mixed diet in young people. During the study, the participants were weighed and examined for body composition every morning. The total energy expenditure (TEE) of the participants was measured by the doubly labelled water method for 7 days, and during this period, basal energy expenditure was measured by indirect calorimetry and physical activity energy expenditure was measured by an accelerometer. The value obtained by subtracting basal energy expenditure and physical activity energy expenditure from TEE was used to calculate TEF. Twenty healthy young students (18–30 years; 10 male) participated in the study. The energy intake of the participants was not significantly different from the Chinese Dietary Reference Intake of energy (P > 0.05). The percentage of energy from protein, fat and carbohydrate were all in the normal range. The intakes of fruits, milk and dietary fiber of the participants were significantly lower than those in the Chinese Dietary Guidelines (P < 0.05). There was no significant difference in the body weight of the participants during the experiment (P > 0.05). When adjusted for body weight, there was no significant difference in either TEE or basal energy expenditure between the male and female participants (P > 0.05). In addition, there was no significant difference in physical activity energy expenditure and TEF between the male and female participants (P > 0.05). The percentage of TEF in TEE was 8.73%. The percentage of TEF in TEE in a Chinese mixed diet in young people was significantly lower than 10% (P < 0.001). A value of 10% is usually considered to be the TEF in mixed diets as a percentage of TEE. [ABSTRACT FROM AUTHOR]

Background and Purpose: Frailty is a common and important geriatric syndrome, distinct from any single chronic disease, and an independent predictor of mortality. It is characterized by age-associated decline in physiological reserve and function across multiple systems, culminating in a vicious cycle of altered energy expenditure. The total energy expenditure (TEE) of an individual includes the resting metabolic rate (RMR), the thermic effect of feeding, and the energy expenditure in physical activity (PAEE). The investigation of the energy expenditure of older adults who are frail is essential for better understanding the syndrome. Therefore, we compared the RMR, the PAEE, the physical activity level (PAL), and the TEE of older adults who were frail with those who were not frail. Methods: A cross-sectional study was conducted with 26 community-dwelling older adults (66-86 years of age). Older adults in the frail and nonfrail groups were matched for age and gender, and the matched pairs were randomly selected to continue the study. The RMR was measured by indirect calorimetry. The TEE was obtained by the multipoint, doubly labeled water method. After collecting a baseline urine sample, each participant received an oral dose of doubly labeled water composed of deuterium oxide and oxygen-18 (2H218O). Subsequently, urine samples were collected on the 1st, 2nd, 3rd, 7th, 12th, 13th, and 14th days after the baseline collection and analyzed by mass spectrometry. Results and Discussion: The older adults who were frail presented significantly lower PAEE (1453.7 [1561.9] vs 3336.1 [1829.3] kj/d, P <.01), PAL (1.4 [0.3] vs 1.9 [0.6], P =.04), and TEE (7919.0 [2151.9] vs 10442.4 [2148.0] kj/d, P <.01) than the older adults who were nonfrail. There was no difference in their RMRs (5673.3 [1569.2] vs 6062.0 [1891.7] kj/d, P =.57). Frailty has been associated with a smaller lean body mass and with a disease-related hypermetabolic state, which might explain the lack of difference in the RMR. The PAL of the older adults who were frail was below the recommended level for older adults and determined a lower PAEE and TEE when compared with older adults who were not frail. Conclusion: This study showed that low energy expenditure in physical activity is a main component of frailty. The PAL of the older adults who were frail was far below the recommended level for older adults. [ABSTRACT FROM AUTHOR]

Objectives: Usual physical activity (PA) is a complex exposure and typical instruments to measure aspects of PA are subject to measurement error, from systematic biases and biological variability. This error can lead to biased estimates of associations between PA and health outcomes. We developed a calibrated physical activity measure that adjusts for measurement error in both self-reported and accelerometry measures of PA in adults from the US Hispanic Community Health Study/Study of Latinos (HCHS/SOL), a community-based cohort study.Design: Total energy expenditure (TEE) from doubly labeled water and resting energy expenditure (REE) from indirect calorimetry were measured in 445 men and women aged 18-74years in 2010-2012, as part of the HCHS/SOL Study of Latinos: Nutrition & Physical Activity Assessment Study (SOLNAS). Measurements were repeated in a subset (N=98) 6months later.Method: Calibration equations for usual activity-related energy expenditure (AEE=0.90×TEE-REE) were developed by regressing this objective biomarker on self-reported PA and sedentary behavior, Actical accelerometer PA, and other subject characteristics.Results: Age, weight and height explained a significant amount of variation in AEE. Actical PA and wear-time were important predictors of AEE; whereas, self-reported PA was not independently associated with AEE. The final calibration equation explained fifty percent of variation in AEE.Conclusions: The developed calibration equations can be used to obtain error-corrected associations between PA and health outcomes in HCHS/SOL. Our study represents a unique opportunity to understand the measurement characteristics of PA instruments in an under-studied Hispanic/Latino cohort. [ABSTRACT FROM AUTHOR]

In this review, the energy metabolism and body composition in athletes are discussed on the basis of an energy balance study using a doubly labeled water method. In athletes, the energy expenditure induced by sports activities widely differs according to the type of sports event. In addition, the physical activity of athletes can reach more than twice the basal metabolic rate throughout the sports season. It may be also more than three times depending on the duration of the tournament and/or the training camp. Thus, the event type, and training time and intensity must be considered when estimating the energy requirement of athletes. [ABSTRACT FROM AUTHOR]

Objectives: This study explores the reliability and validity of the SenseWear Armband (SWA) and Actical (ACT) for free-living total energy expenditure, and energy expenditure during rest and light-to-moderate exercises (walking, ergocycling).Design: Participants wore the 2 devices during 7 days (free-living) and then participated to 3 days of testing in our laboratory.Methods: SWA and ACT estimates of total energy expenditure was compared to the doubly labeled water technique (7 days), and energy expenditure during rest (60min), treadmill (45min; intensities ∼22% to ∼41% VO2peak) and ergocycling (45min; ∼50% VO2peak) were compared to indirect calorimetry over the following 3 days. Paired T-tests and intra-class correlation coefficient (ICC) with 95% confidence interval (CI95) were computed.Results: Twenty adults were recruited (BMI 23.1±2.3kg/m(2)). Compared to doubly labelled water, SWA overestimated energy expenditure by 94kcal/d (±319; P=0.2) and ACT underestimated by -244kcal/d (±258; P=0.001). Energy expenditure during rest (SWA 210±116, ACT 124±133kcal/d; p<0.05) and treadmill (according on intensity: SWA 54±46 to 67±38, ACT 68±25 to 84±40kcal; p<0.05) were overestimated and underestimated during ergocycling (SWA -93±65, ACT -269±111kcal; p<0.05) compared to indirect calorimetry. High ICC were observed at rest (SWA 0.994 CI95 0.987-0.997; ACT 0.998 CI95 0.996-0.999) and during ergocycling (SWA 0.941 CI95 0.873-0.975; ACT 0.854 CI95 0.687-0.939).Conclusion: Acceptable estimation of total energy expenditure was observed with the SWA. Both devices were reliable but not accurate for energy expenditure's estimations during rest and for specific exercises. [ABSTRACT FROM AUTHOR]

Background: Physical activity (PA) during pregnancy is an effective and safe way to improve maternal health in uncomplicated pregnancies. However, compliance with PA recommendations remains low among pregnant women. Objective: The purpose of this study was to evaluate the effects of offering structured supervised exercise training (EXE) or motivational counseling on PA (MOT) during pregnancy on moderate-to-vigorous intensity physical activity (MVPA) level. Additionally, complementary measures of PA using the Pregnancy Physical Activity Questionnaire (PPAQ) and gold standard doubly labeled water (DLW) technique were investigated. The hypotheses were that both EXE and MOT would increase MVPA in pregnancy compared with standard care (CON) and that EXE would be more effective than MOT. In addition, the association between MVPA and the number of sessions attended was explored. Methods: A randomized controlled trial included 220 healthy, inactive pregnant women with a median gestational age of 12.9 (IQR 9.4-13.9) weeks. A total of 219 women were randomized to CON (45/219), EXE (87/219), or MOT (87/219). The primary outcome was MVPA (minutes per week) from randomization to the 29th gestational week obtained by a wrist-worn commercial activity tracker (Vivosport, Garmin International). PA was measured by the activity tracker throughout pregnancy, PPAQ, and DLW. The primary outcome analysis was performed as an analysis of covariance model adjusting for baseline PA. Results: The average MVPA (minutes per week) from randomization to the 29th gestational week was 33 (95% CI 18 to 47) in CON, 50 (95% CI 39 to 60) in EXE, and 40 (95% CI 30 to 51) in MOT. When adjusted for baseline MVPA, participants in EXE performed 20 (95% CI 4 to 36) minutes per week more MVPA than participants in CON (P =.02). MOT was not more effective than CON; EXE and MOT also did not differ. MVPA was positively associated with the number of exercise sessions attended in EXE from randomization to delivery (P =.04). Attendance was higher for online (due to COVID-19 restrictions) compared with physical exercise training (P =.03). Adverse events and serious adverse events did not differ between groups. Conclusions: Offering EXE was more effective than CON to increase MVPA among pregnant women, whereas offering MOT was not. MVPA in the intervention groups did not reach the recommended level in pregnancy. Changing the intervention to online due to COVID-19 restrictions did not affect MVPA level but increased exercise participation. Trial Registration: ClinicalTrials.gov NCT03679130; https://clinicaltrials.gov/ct2/show/NCT03679130 International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2020-043671 J Med Internet Res 2022;24(7):e37699 doi:10.2196/37699 [ABSTRACT FROM AUTHOR]

Summary: Background & aims: Little is known about the validity of repeated 24-h dietary recalls (24-HDR) as a measure of total energy intake (EI) in young children. This study aimed to evaluate the validity of proxy-reported EI by comparison with total energy expenditure (TEE) measured by the doubly labeled water (DLW) technique. Methods: The agreement between EI and TEE was investigated in 36 (47.2% boys) children aged 4–10 years from Belgium and Spain using subgroup analyses and Bland–Altman plots. Low-energy-reporters (LER), adequate-energy-reporters (AER) and high-energy-reporters (HER) were defined from the ratio of EI over TEE by application of age- and sex-specific cut-off values. Results: There was good agreement between means of EI (1500 kcal/day) and TEE (1523 kcal/day) at group level though in single children, i.e. at the individual level, large differences were observed. Almost perfect agreement between EI and TEE was observed in thin/normal weight children (EI: 1511 kcal/day; TEE: 1513 kcal/day). Even in overweight/obese children the mean difference between EI and TEE was only −86 kcal/day. Among the participants, 28 (78%) were classified as AER, five (14%) as HER and three (8%) as LER. Conclusion: Two proxy-reported 24-HDRs were found to be a valid instrument to assess EI on group level but not on the individual level. [Copyright &y& Elsevier]

Objective Doubly labeled water (DLW) method is the gold standard for measuring total energy expenditure (TEE). We used this method to measure TEE in Chinese young men. Methods Sixteen healthy young men age 23±1 years with body mass index 22.0±1.4 kg/m² were recruited. TEE was measured by the DLW method, and basal energy expenditure (BEE) was determined by indirect calorimetry. We also conducted 24-h activity, energy balance and factorial approach to estimate energy requirements of the subjects. Results TEE of subjects by DLW method was 9.45±0.57 MJ/day (2258±180 kcal/day). The 24-h activity was 10.80±0.33 MJ/day (2582±136 kcal/day). The energy requirement, derived from energy balance observations, was 9.93±1.32 MJ/day (2373±315 kcal/day). The BEE of 6.65±0.28 MJ/day (1589±67 kcal/day), calculated by the adjusted Schofield equation, was significantly higher (P<0.00l) than that measured by indirect calorimetry, 5.99±0.66 MJ/day (1433±158 kcal/day). The TEE derived from the factorial approach was 10.31±0.43 MJ/day (2463±104 kcal/day). Conclusion The TEE of Chinese young men measured by the DLW method was about 10% lower than the current recommended nutrient intake (RNI), suggesting that the RNI for Chinese men maybe overestimated. Further studies are warranted to determine the value of the estimated energy requirement. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: The purpose of this study was to compare established methods with newly-developed methods for estimating the total energy expenditure (TEE). Methods: The study subjects comprised 46 individuals, including 16 middle-aged men (mean age 51.4 years), 14 middle-aged women (mean age 49.9 years) and 16 young women (mean age 19.1 years). The TEE was estimated from 24-h heart rate (HR) data using newly-developed software (MoveSense HRAnalyzer 2011a, RC1, Suunto Oy, Vantaa, Finland), and was compared against the TEE determined using doubly labeled water (DLW). Agreement between the two methods was analyzed using Bland and Altman plots. Results: The HR method yielded similar TEE values as the DLW method at the group level, with an average of 8.6 kcal/day in the difference in the mean, but with large individual variations. Forty-four (96%) out of 46 subjects fell within ±2SD of the mean difference in TEE comparisons, and there was no tendency towards under- or over-estimation. Conclusion: Our results indicate that the current software using HR analysis for the estimation of daily TEE needs further development for use with free-living individuals. [Copyright &y& Elsevier]

Abstract: Objective: The present study aimed to evaluate the accuracy of the factorial method for estimating energy needs in individuals living in China. Methods: Sixteen healthy female adults aged 22.1±1.2 years with a body mass index (kg/m2) of 20.4±1.7 were selected as subjects. In free-living conditions, energy expenditure (EE) was determined by using the factorial method. At the same time, the doubly labeled water method (DLW) was also used to measure energy expenditure of the subjects and served as the criterion method. EE predicted by the factorial method (EEfactorial) was compared with the simultaneous measurement of EE by the validated DLW method (EEDLW). Results: There was excellent agreement between EEfactorial (7.46±0.59 MJ/d) and EEDLW (7.64 ± 0.49 MJ/d), with a difference of −2.6±4.9% (−0.18±0.36 MJ/d). No significant differences were found between the two methods. EEfactorial was highly correlated with EEDLW (r=0.795, P<0.001) and a good agreement for individuals was found by using the Bland and Altman test. Conclusion: The factorial method gives satisfactory estimates of EE for both groups and individuals living in China. [Copyright &y& Elsevier]

The article discusses a study to monitor food intake among elderly people. It states that one health risk factor among residents of assisted living communities is the inadequate monitoring of food. The study included 298 subjects between 70 to 79 years of age, and were asked to report on what they ate, how often they did so, how much energy they were able to expend, and more, over a 2 week period. A detailed discussion of the results, follows, but what reportedly stands out is the degree of under eating reported, with a suggestion of further studies.

Background: Self-monitoring using certain types of pedometers and accelerometers has been reported to be effective for promoting and maintaining physical activity (PA). However, the validity of estimating the level of PA or PA energy expenditure (PAEE) for general consumers using wearable devices has not been sufficiently established. Objective: We examined the validity of 12 wearable devices for determining PAEE during 1 standardized day in a metabolic chamber and 15 free-living days using the doubly labeled water (DLW) method. Methods: A total of 19 healthy adults aged 21 to 50 years (9 men and 10 women) participated in this study. They followed a standardized PA protocol in a metabolic chamber for an entire day while simultaneously wearing 12 wearable devices: 5 devices on the waist, 5 on the wrist, and 2 placed in the pocket. In addition, they spent their daily lives wearing 12 wearable devices under free-living conditions while being subjected to the DLW method for 15 days. The PAEE criterion was calculated by subtracting the basal metabolic rate measured by the metabolic chamber and 0.1×total energy expenditure (TEE) from TEE. The TEE was obtained by the metabolic chamber and DLW methods. The PAEE values of wearable devices were also extracted or calculated from each mobile phone app or website. The Dunnett test and Pearson and Spearman correlation coefficients were used to examine the variables estimated by wearable devices. Results: On the standardized day, the PAEE estimated using the metabolic chamber (PAEEcha) was 528.8±149.4 kcal/day. The PAEEs of all devices except the TANITA AM-160 (513.8±135.0 kcal/day; P >.05), SUZUKEN Lifecorder EX (519.3±89.3 kcal/day; P >.05), and Panasonic Actimarker (545.9±141.7 kcal/day; P >.05) were significantly different from the PAEEcha. None of the devices was correlated with PAEEcha according to both Pearson (r=−.13 to.37) and Spearman (ρ=−.25 to.46) correlation tests. During the 15 free-living days, the PAEE estimated by DLW (PAEEdlw) was 728.0±162.7 kcal/day. PAEE values of all devices except the Omron Active style Pro (716.2±159.0 kcal/day; P >.05) and Omron CaloriScan (707.5±172.7 kcal/day; P >.05) were significantly underestimated. Only 2 devices, the Omron Active style Pro (r=.46; P =.045) and Panasonic Actimarker (r=.48; P =.04), had significant positive correlations with PAEEdlw according to Pearson tests. In addition, 3 devices, the TANITA AM-160 (ρ=.50; P =.03), Omron CaloriScan (ρ=.48; P =.04), and Omron Active style Pro (ρ=.48; P =.04), could be ranked in PAEEdlw. Conclusions: Most wearable devices do not provide comparable PAEE estimates when using gold standard methods during 1 standardized day or 15 free-living days. Continuous development and evaluations of these wearable devices are needed for better estimations of PAEE. [ABSTRACT FROM AUTHOR]

Background: Meal-Q and its shorter version, MiniMeal-Q, are 2 new Web-based food frequency questionnaires. Their meal-based and interactive format was designed to promote ease of use and to minimize answering time, desirable improvements in large epidemiological studies. Objective: We evaluated the validity of energy and macronutrient intake assessed with Meal-Q and MiniMeal-Q as well as the reproducibility of Meal-Q. Methods: Healthy volunteers aged 20-63 years recruited from Stockholm County filled out the 174-item Meal-Q. The questionnaire was compared to 7-day weighed food records (WFR; n=163), for energy and macronutrient intake, and to doubly labeled water (DLW; n=39), for total energy expenditure. In addition, the 126-item MiniMeal-Q was evaluated in a simulated validation using truncated Meal-Q data. We also assessed the answering time and ease of use of both questionnaires. Results: Bland-Altman plots showed a varying bias within the intake range for all validity comparisons. Cross-classification of quartiles placed 70%-86% in the same/adjacent quartile with WFR and 77% with DLW. Deattenuated and energy-adjusted Pearson correlation coefficients with the WFR ranged from r=0.33-0.74 for macronutrients and was r=0.18 for energy. Correlations with DLW were r=0.42 for Meal-Q and r=0.38 for MiniMeal-Q. Intraclass correlations for Meal-Q ranged from r=0.57-0.90. Median answering time was 17 minutes for Meal-Q and 7 minutes for MiniMeal-Q, and participants rated both questionnaires as easy to use. Conclusions: Meal-Q and MiniMeal-Q are easy to use and have short answering times. The ranking agreement is good for most of the nutrients for both questionnaires and Meal-Q shows fair reproducibility. (J Med Internet Res 2013;15(6):e109) [ABSTRACT FROM AUTHOR]

Background: Online or Web-based measurement systems have been proposed as convenient methods for collecting physical activity data. We developed two Web-based physical activity systems--the 24-hour Physical Activity Record Web (24hPAR WEB) and 7 days Recall Web (7daysRecall WEB). Objective: To examine the validity of two Web-based physical activity measurement systems using the doubly labeled water (DLW) method. Methods: We assessed the validity of the 24hPAR WEB and 7daysRecall WEB in 20 individuals, aged 25 to 61 years. The order of email distribution and subsequent completion of the two Web-based measurements systems was randomized. Each measurement tool was used for a week. The participants' activity energy expenditure (AEE) and total energy expenditure (TEE) were assessed over each week using the DLW method and compared with the respective energy expenditures estimated using the Web-based systems. Results: The mean AEE was 3.90 (SD 1.43) MJ estimated using the 24hPAR WEB and 3.67 (SD 1.48) MJ measured by the DLW method. The Pearson correlation for AEE between the two methods was r = .679 (P < .001). The Bland-Altman 95% limits of agreement ranged from -2.10 to 2.57 MJ between the two methods. The Pearson correlation for TEE between the two methods was r = .874 (P < .001). The mean AEE was 4.29 (SD 1.94) MJ using the 7daysRecall WEB and 3.80 (SD 1.36) MJ by the DLW method. The Pearson correlation for AEE between the two methods was r = .144 (P = .54). The Bland-Altman 95% limits of agreement ranged from -3.83 to 4.81 MJ between the two methods. The Pearson correlation for TEE between the two methods was r = .590 (P = .006). The average input times using terminal devices were 8 minutes and 10 seconds for the 24hPAR WEB and 6 minutes and 38 seconds for the 7daysRecall WEB. Conclusions: Both Web-based systems were found to be effective methods for collecting physical activity data and are appropriate for use in epidemiological studies. Because the measurement accuracy of the 24hPAR WEB was moderate to high, it could be suitable for evaluating the effect of interventions on individuals as well as for examining physical activity behavior. [ABSTRACT FROM AUTHOR]