A Novel Smartphone Accelerometer Application for Low-Intensity Activity and Energy Expenditure Estimations in Overweight and Obese Adults.

Physical inactivity and sedentary behaviors are on the rise worldwide and contribute to the current overweight and obesity scourge. The loss of healthy life style benchmarks and the lack of the need to move make it necessary to provide feedback about physical and sedentary activities in order to promote active ways of life. The aim of this study was to develop a specific function adapted to overweight and obese people to identify four physical activity (PA) categories and to estimate the associated total energy expenditure (TEE). This function used accelerometry data collected from a smartphone to evaluate activity intensity and length, and TEE. The performance of the proposed function was estimated according to two references (Armband® and FitmatePro®) under controlled conditions (CC) for a 1.5-h scenario, and to the Armband® device in free-living conditions (FLC) over a 12-h monitoring period. The experiments were carried out with overweight and obese volunteers: 13 in CC and 27 in FLC. The evaluation differences in time spent in each category were lower than 7% in CC and 6% in FLC, in comparison to the Armband® and FitmatePro® references. The TEE mean gap in absolute value between the function and the two references was 9.3% and 11.5% in CC, and 8.5% according to Armband® in FLC.

A smartphone-driven methodology for estimating physical activities and energy expenditure in free living conditions.

This paper introduces a function dedicated to the estimation of total energy expenditure (TEE) of daily activities based on data from accelerometers integrated into smartphones. The use of mass-market sensors such as accelerometers offers a promising solution for the general public due to the growing smartphone market over the last decade. The TEE estimation function quality was evaluated using data from intensive numerical experiments based, first, on 12 volunteers equipped with a smartphone and two research sensors (Armband and Actiheart) in controlled conditions (CC) and, then, on 30 other volunteers in free-living conditions (FLC). The TEE given by these two sensors in both conditions and estimated from the metabolic equivalent tasks (MET) in CC served as references during the creation and evaluation of the function. The TEE mean gap in absolute value between the function and the three references was 7.0%, 16.4% and 2.7% in CC, and 17.0% and 23.7% according to Armband and Actiheart, respectively, in FLC. This is the first step in the definition of a new feedback mechanism that promotes self-management and daily-efficiency evaluation of physical activity as part of an information system dedicated to the prevention of chronic diseases.