Dataset Information

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

ABSTRACT: Accurate measurement of free-living energy intake (EI) over long periods is imperative for understanding obesity and its treatment. Unfortunately, traditional methods rely on self-report and are notoriously inaccurate. Although EI can be indirectly estimated by the intake-balance method, this technique is prohibitively labor-intensive and expensive, requiring repeated measures of energy expenditure via doubly labeled water (DLW) along with multiple dual-energy X-ray absorptiometry (DXA) scans to measure changes in body energy stores.Our objective was to validate a mathematical method to measure long-term changes in free-living energy intake.We measured body weight and EI changes (?EI) over 4 time intervals by using the intake-balance method in 140 individuals who underwent 2 y of caloric restriction as part of the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy study. We compared the ?EI values calculated by using DLW/DXA with those obtained by using a mathematical model of human metabolism whose only inputs were the initial demographic information and repeated body weight data.The mean ?EI values calculated by the model were within 40 kcal/d of the DLW/DXA method throughout the 2-y study. For individual subjects, the overall root mean square deviation between the model and DLW/DXA method was 215 kcal/d, and most of the model-calculated ?EI values were within 132 kcal/d of the DLW/DXA method.Accurate and inexpensive estimates of ?EI that are comparable to the DLW/DXA method can be obtained by using a mathematical model and repeated body weight measurements.

SUBMITTER: Sanghvi A

PROVIDER: S-EPMC4515869 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Sanghvi Arjun A Redman Leanne M LM Martin Corby K CK Ravussin Eric E Hall Kevin D KD

The American journal of clinical nutrition 20150603 2

<h4>Background</h4>Accurate measurement of free-living energy intake (EI) over long periods is imperative for understanding obesity and its treatment. Unfortunately, traditional methods rely on self-report and are notoriously inaccurate. Although EI can be indirectly estimated by the intake-balance method, this technique is prohibitively labor-intensive and expensive, requiring repeated measures of energy expenditure via doubly labeled water (DLW) along with multiple dual-energy X-ray absorptiom ...[more]

PMID: 26040640

Similar Datasets

Project description:BackgroundTo improve weight management in pregnant women, there is a need to deliver specific, data-based recommendations on energy intake.ObjectiveThis cross-sectional study evaluated the accuracy of an electronic reporting method to measure daily energy intake in pregnant women compared with total daily energy expenditure (TDEE).MethodsTwenty-three obese [mean ± SEM body mass index (kg/m2): 36.9 ± 1.3] pregnant women (aged 28.3 ±1.1 y) used a smartphone application to capture images of their food selection and plate waste in free-living conditions for ≥6 d in early (13-16 wk) and late (35-37 wk) pregnancy. Energy intake was evaluated by the smartphone application SmartIntake and compared with simultaneous assessment of TDEE obtained by doubly labeled water. Accuracy was defined as reported energy intake compared with TDEE (percentage of TDEE). Ecological momentary assessment prompts were used to enhance data reporting. Two-one-sided t tests for the 2 methods were used to assess equivalency, which was considered significant when accuracy was >80%.ResultsEnergy intake reported by the SmartIntake application was 63.4% ± 2.3% of TDEE measured by doubly labeled water (P = 1.00). Energy intake reported as snacks accounted for 17% ± 2% of reported energy intake. Participants who used their own phones compared with participants who used borrowed phones captured more images (P = 0.04) and had higher accuracy (73% ± 3% compared with 60% ± 3% of TDEE; P = 0.01). Reported energy intake as snacks was significantly associated with the accuracy of SmartIntake (P = 0.03). To improve data quality, excluding erroneous days of likely underreporting (<60% TDEE) improved the accuracy of SmartIntake, yet this was not equivalent to TDEE (-22% ± 1% of TDEE; P = 1.00).ConclusionsEnergy intake in obese, pregnant women obtained with the use of an electronic reporting method (SmartIntake) does not accurately estimate energy intake compared with doubly labeled water. However, accuracy improves by applying criteria to eliminate erroneous data. Further evaluation of electronic reporting in this population is needed to improve compliance, specifically for reporting frequent intake of small meals. This trial was registered at www.clinicaltrials.gov as NCT01954342.

Project description:Experimentally establishing a group's body weight maintenance energy requirement is an important component of metabolism research. At present, the reference approach for measuring the metabolizable energy intake (MEI) from foods required for body weight maintenance in non-confined subjects is the doubly-labeled water (DLW)-total energy expenditure (TEE) method. In the current study, we evaluated an energy-intake weight balance method as an alternative to DLW that is more flexible and practical to apply in some settings.The hypothesis was tested that MEI from foods observed in a group of subjects maintaining a constant energy intake while keeping their weight within ±1 kg over 10 days is non-significantly different from DLW-measured TEE (TEEDLW). Six non-obese subjects evaluated as part of an earlier study completed the inpatient protocol that included a 3-day initial adjustment period.The group body weight coefficient of variation (X ± SD) during the 10-day balance period was 0.38 ± 0.10% and the slope of the regression line for body weight versus protocol day was non-significant at 1.8 g/day (R2, 0.002, p = 0.98). MEI from foods observed during the 10-day balance period (2390 ± 543 kcal/day) was non-significantly different (p = 0.96) from TEE measured by DLW (2373 ± 713 kcal/day); the MEI/TEEDLW ratio was 1.03 ± 0.15 (range 0.87-1.27) and the correlation between MEI from foods and TEEDLW was highly significant (R2, 0.88, p = 0.005).A carefully managed 10-day protocol that includes a constant MEI level from foods with weight stability (±1 kg) will provide a group's body weight maintenance energy requirement similar to that obtained with DLW. This approach opens the possibility of conducting affordable weight balance studies, shorter in duration than those previously reported, that are needed to answer a wide range of questions in clinical nutrition. Trial registration The study is registered at http://www.clinicaltrials.gov (NCT01672632; August 20, 2012).

Project description:BACKGROUND:Wearable trackers for monitoring physical activity (PA) and total sleep time (TST) are increasingly popular. These devices are used not only by consumers to monitor their behavior but also by researchers to track the behavior of large samples and by health professionals to implement interventions aimed at health promotion and to remotely monitor patients. However, high costs and accuracy concerns may be barriers to widespread adoption. OBJECTIVE:This study aimed to investigate the concurrent validity of 6 low-cost activity trackers for measuring steps, moderate-to-vigorous physical activity (MVPA), and TST: Geonaut On Coach, iWown i5 Plus, MyKronoz ZeFit4, Nokia GO, VeryFit 2.0, and Xiaomi MiBand 2. METHODS:A free-living protocol was used in which 20 adults engaged in their usual daily activities and sleep. For 3 days and 3 nights, they simultaneously wore a low-cost tracker and a high-cost tracker (Fitbit Charge HR) on the nondominant wrist. Participants wore an ActiGraph GT3X+ accelerometer on the hip at daytime and a BodyMedia SenseWear device on the nondominant upper arm at nighttime. Validity was assessed by comparing each tracker with the ActiGraph GT3X+ and BodyMedia SenseWear using mean absolute percentage error scores, correlations, and Bland-Altman plots in IBM SPSS 24.0. RESULTS:Large variations were shown between trackers. Low-cost trackers showed moderate-to-strong correlations (Spearman r=0.53-0.91) and low-to-good agreement (intraclass correlation coefficient [ICC]=0.51-0.90) for measuring steps. Weak-to-moderate correlations (Spearman r=0.24-0.56) and low agreement (ICC=0.18-0.56) were shown for measuring MVPA. For measuring TST, the low-cost trackers showed weak-to-strong correlations (Spearman r=0.04-0.73) and low agreement (ICC=0.05-0.52). The Bland-Altman plot revealed a variation between overcounting and undercounting for measuring steps, MVPA, and TST, depending on the used low-cost tracker. None of the trackers, including Fitbit (a high-cost tracker), showed high validity to measure MVPA. CONCLUSIONS:This study was the first to examine the concurrent validity of low-cost trackers. Validity was strongest for the measurement of steps; there was evidence of validity for measurement of sleep in some trackers, and validity for measurement of MVPA time was weak throughout all devices. Validity ranged between devices, with Xiaomi having the highest validity for measurement of steps and VeryFit performing relatively strong across both sleep and steps domains. Low-cost trackers hold promise for monitoring and measurement of movement and sleep behaviors, both for consumers and researchers.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure