Predicting Free-living Energy Expenditure Using a Miniaturized Ear-Worn Sensor: An Evaluation Against Doubly Labelled Water
Loubna Bouarfa, Louis Atallah, Richard Mark Kwasnicki, Claire Pettitt, Gary Frost, Guang-Zhong Yang
Imperial College London, UK
Volume: 61, Issue: 2, Page(s): 566 – 575
Physical activity is a major determinant of health and quality of life in our rapidly ageing society. It is defined as bodily movement produced by skeletal muscles that require energy expenditure. Accurate estimation of daily total energy expenditure (EE) is a prerequisite for assessing many 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 was first derived and validated in a controlled setting using healthy subjects involving different physical activities. Ten different activities were assessed showing a ten-fold cross validation error of 0.24. Furthermore, the EE prediction model shows a mean absolute deviation (MAD) below 1.2 the Metabolic Equivalent of Tasks [METs]. The same model was applied to the free-living setting with a different population but with similar demographics for validation. The results were compared against those derived from Doubly Labelled Water (DLW). In free-living settings, the predicted daily EE has a correlation of R = 0,74; p = 0,008 and a MAD of MAD = 272 kcal/day. These results demonstrate that laboratory-derived prediction models can be used to predict EE under free-living conditions.