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Int J Pediatr. 2014;2014:328076. doi: 10.1155/2014/328076. Epub 2014 Jan 6.

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

Author information

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

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 (SD = 1.62) 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.

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