Background The challenges of accurate estimation of energy intake (EI) are well-documented, with self-reported values 12%–20% below expected values. New approaches rely on gold-standard assessments of the other components of energy balance, energy expenditure (EE) and energy storage (ES), to estimate EI. Objectives The purpose of this study was to evaluate the validity, repeatability, and measurement error of consumer devices when estimating energy balance in a free-living population. Methods Twenty-four healthy adults (14 women, 10 men; mean ± SD age: 30.7 ± 8.2 y) completed two 14-d assessment periods, including assessments of EE and ES using gold-standard [doubly labeled water (DLW) and DXA] and commercial devices [Fitbit Alta HR activity monitor (Alta) and Fitbit Aria wireless body composition scale (Aria)], and of EI by dietician-administered recalls. Accuracy and validity were assessed using Spearman correlation, interclass correlation, mean absolute percentage error, and equivalency testing. We also applied linear measurement error modeling including error in gold-standard devices and within-subject repeated-measures design to calibrate consumer devices and quantify error. Results There was moderate to strong agreement for EE between the Fitbit Alta and DLW at each time point (rs = 0.82 and 0.66 for Times 1 and 2, respectively). There was weak agreement for ES between the Fitbit Aria and DXA (rs = 0.15 and 0.49 for Times 1 and 2, respectively). Correlations between methods to assess EI ranged from weak to strong, with agreement between the DXA/DLW-calculated EI and dietary recalls being the highest (rs = 0.63 for Time 1 and 0.73 for Time 2). Only EE from the Fitbit Alta at Time 1 was equivalent to the DLW value using equivalency testing. Conclusions Commercial devices provide estimates of energy balance in free-living adults with varying degrees of validity compared to gold-standard techniques. EE estimates were the most robust overall, whereas ES estimates were generally poor.
Inverse prediction models have commonly been developed to handle scalar data from physical experiments. However, it is not uncommon for data to be collected in functional form. When data is collected in functional form, it must be aggregated to fit the form of traditional methods, which often results in a loss of information. For expensive experiments, this loss of information can be costly. This paper introduces the functional inverse prediction (FIP) framework, a general approach which uses the full information in functional response data to provide inverse predictions with probabilistic prediction uncertainties obtained with the bootstrap. The FIP framework is a general methodology that can be modified by practitioners to accommodate many different applications and types of data. We demonstrate the framework, highlighting points of flexibility, with a simulation example and applications to weather data and to nuclear forensics. Results show how functional models can improve the accuracy and precision of predictions.
This study compared and calibrated metabolic equivalents (METs) per day from 24-hour physical activity recall (24hPAR) with accelerometry. A sub-sample of 74 adults of both sexes, residents of Brasília, Brazil, from a larger study had same day measurements of accelerometry and 24hPAR data. METs values were assessed by accelerometry (7 consecutive days of use) and by 24hPAR (minimum of one and maximum of 2 per person). A script was written in the R statistical software to analyse the recall and accelerometer data. The script ran a simple linear regression to visualize the relationship between total METs/ day for the two methods and to execute the recall measurement error correction. Most of participants were female (54.1%), with at least university graduate (94.6%) and mean age of 34.8 years (±11.83). The correlation coefficient obtained between 24hPAR and accelerometer was r = 0.55, considered moderate and significant (p < 0.001). A majority of the participants (77%) underestimated METs values compared to accelerometry when answering the questionnaire. Calibration of 24hPAR allowed us to approximate MET values to the accelerometer. The calibration equation to correct total METs/ day for measurement error is (total 24hPAR METs/day -10.6)/0.619. The 24hPAR is a decent tool to assess PA level in large adults' samples. However, compared with accelerometer, it underestimates METs values, which can be corrected with the use of the calibration equation provided in this study.
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