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  • Title: Predicting free-living energy expenditure using a miniaturized ear-worn sensor: an evaluation against doubly labeled water.
    Author: Bouarfa L, Atallah L, Kwasnicki RM, Pettitt C, Frost G, Yang GZ.
    Journal: IEEE Trans Biomed Eng; 2014 Feb; 61(2):566-75. PubMed ID: 24108707.
    Abstract:
    Accurate estimation of daily total energy expenditure (EE)is a prerequisite for assisted weight management and assessing certain health conditions. The use of wearable sensors for predicting free-living EE is challenged by consistent sensor placement, user compliance, and estimation methods used. This paper examines whether a single ear-worn accelerometer can be used for EE estimation under free-living conditions.An EE prediction model as first derived and validated in a controlled setting using healthy subjects involving different physical activities. Ten different activities were assessed showing a tenfold cross validation error of 0.24. Furthermore, the EE prediction model shows a mean absolute deviation(MAD) below 1.2 metabolic equivalent of tasks. The same model was applied to a free-living setting with a different population for further validation. The results were compared against those derived from doubly labeled water. In free-living settings, the predicted daily EE has a correlation of 0.74, p 0.008, and a MAD of 272 kcal day. These results demonstrate that laboratory-derived prediction models can be used to predict EE under free-living conditions [corrected].
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