About this Journal Submit a Manuscript Table of Contents
International Journal of Pediatrics
Volume 2014 (2014), Article ID 328076, 9 pages
http://dx.doi.org/10.1155/2014/328076
Research Article

Innovation through Wearable Sensors to Collect Real-Life Data among Pediatric Patients with Cardiometabolic Risk Factors

1Université de Montréal Hospital Research Center, Centre de Recherche du CHUM (CRCHUM), Tour St-Antoine S02-340, 850 St-Denis, Montreal, QC, Canada H2X 0A9
2Social and Preventive Medicine Department, Université de Montréal, Montreal, QC, Canada H3N 1X7
3CHU Sainte-Justine Research Center, Montreal, QC, Canada H3T 1C5
4Department of Exercise Science, Concordia University, Montreal, QC, Canada H4B 1R6
5Department of Kinesiology, University of Montreal, Montreal, QC, Canada H3T 1J4
6Division of Endocrinology, Department of Pediatrics, CHU Sainte-Justine and Université de Montréal, Montreal, QC, Canada H3T 1C5
7Division of Cardiology, Department of Pediatrics, CHU Sainte-Justine and Université de Montréal, Montreal, QC, Canada H3T 1C5
8Synemorphose Inc., Montreal, QC, Canada H4C 3H2
9Division of Genetics, Department of Pediatrics, CHU Sainte-Justine and Université de Montréal, Montreal, QC, Canada H3T 1C5

Received 5 August 2013; Revised 16 October 2013; Accepted 16 October 2013; Published 6 January 2014

Academic Editor: M. Genel

Copyright © 2014 Kestens Yan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background. While increasing evidence links environments to health behavior, clinicians lack information about patients’ physical activity levels and lifestyle environments. We present mobile health tools to collect and use spatio-behavioural lifestyle data for personalized physical activity plans in clinical settings. Methods. The Dyn@mo lifestyle intervention was developed at the Sainte-Justine University Hospital Center to promote physical activity and reduce sedentary time among children with cardiometabolic risk factors. Mobility, physical activity, and heart rate were measured in free-living environments during seven days. Algorithms processed data to generate spatio-behavioural indicators that fed a web-based interactive mapping application for personalised counseling. Proof of concept and tools are presented using data collected among the first 37 participants recruited in 2011. Results. Valid accelerometer data was available for 5.6 ( ) days in average, heart rate data for 6.5 days, and GPS data was available for 6.1 (2.1) days. Spatio-behavioural indicators were shared between patients, parents, and practitioners to support counseling. Conclusion. Use of wearable sensors along with data treatment algorithms and visualisation tools allow to better measure and describe real-life environments, mobility, physical activity, and physiological responses. Increased specificity in lifestyle interventions opens new avenues for remote patient monitoring and intervention.