The eMouveRecherche application competes with research devices to evaluate energy expenditure, physical activity and still time in free-living conditions

Abstract

The proliferation of smartphones is creating new opportunities to monitor and interact with human subjects in free-living conditions since smartphones are familiar to large segments of the population and facilitate data collection, transmission and analysis. From accelerometry data collected by smartphones, the present work aims to estimate time spent in different activity categories and the energy expenditure in free-living conditions. Our research encompasses the definition of an energy-saving function (Pred) considering four physical categories of activities (still, light, moderate and vigorous), their duration and metabolic cost (MET). To create an efficient discrimination function, the method consists of classifying accelerometry-transformed signals into categories and of associating each category with corresponding Metabolic Equivalent Tasks. The performance of the Pred function was compared with two previously published functions (f(η,d)aedes,f(η,d)nrjsi), and with two dedicated sensors (Armband® and Actiheart®) in free-living conditions over a 12-h monitoring period using 30 volunteers. Compared to the two previous functions, Pred was the only one able to provide estimations of time spent in each activity category. In relative value, all the activity categories were evaluated similarly to those given by Armband®. Compared to Actiheart®, the function underestimated still activities by 10.1% and overestimated light- and moderate-intensity activities by 7.9% and 4.2%, respectively. The total energy expenditure error produced by Pred compared to Armband® was lower than those given by the two previous functions (5.7% vs. 14.1% and 17.0%). Pred provides the user with an accurate physical activity feedback which should help self-monitoring in free-living conditions.

eMouveRecherche: the first scientific application to promote light-intensity activity for the prevention of chronic diseases

“…Because of the work done beforehand [15,16], the results of the present study are expected to be accurate and able to detect small differences in behavior. The results showed different activity profiles between the two weight statuses recorded for one day of spontaneous activity.…”

“…The eMouveRecherche application collects the smartphone accelerometry data at 6 Hz. The raw accelerometry data were then sent via Internet to the ActivCollector Web platform (https:// activcollector.clermont.inra.fr) where they were immediately compiled and analyzed according to the algorithms designed in [15,16]. The algorithms were validated under controlled and free-living conditions against reference methods (Armband and Fitmate Pro).…”

“…They were specifically designed to estimate, on the one hand, the daily-living activities of normal weight adults and, on the other, the activities of overweight/obese adults in free-living conditions. The algorithms were validated against reference methods commonly used in energy metabolism and recently published [15,16].…”

Research Progress on Measurements of Energy Expenditure in Physical Activity

“…Accelerometers built into smartphones and wearables are a simple alternative, at a lower cost and can be measured continuously. Guidoux et al [25] proposed the Pred EE algorithm for measuring the energy expenditure of different types of activities under free-living condition, compared with Armband, the total EE measurement error is 5.7%. However, the accuracy of the measurements obtained with these devices still needs to be improved.…”

Comparison of Active and Sedentary Bout Lengths in Normal and Overweight Adults using eMouverecherche

“…In a previous work, we developed the eMouveRecherche application to accurately quantify four types of behavior: Immobility, light-, moderate-and vigorous-intensity activity from accelerometry data collected by smartphones. Specific algorithms to quantify time spent in these four categories were developed in two distinct populations and tested in controlled and free-living conditions [15,16]. The algorithms were scientifically validated with less than 5% of error in absolute value against the reference method (indirect calorimetry) or research device (Armband®).…”