Innovation through Wearable Sensors to Collect Real-Life Data among Pediatric Patients with Cardiometabolic Risk Factors
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 i...
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doaj-bea594ebbcf7423986549bcbfff2041a2020-11-25T00:20:19ZengHindawi LimitedInternational Journal of Pediatrics1687-97401687-97592014-01-01201410.1155/2014/328076328076Innovation through Wearable Sensors to Collect Real-Life Data among Pediatric Patients with Cardiometabolic Risk FactorsKestens Yan0Barnett Tracie1Mathieu Marie-Ève2Henderson Mélanie3Bigras Jean-Luc4Thierry Benoit5Maxime St-Onge6Lambert Marie7Université de Montréal Hospital Research Center, Centre de Recherche du CHUM (CRCHUM), Tour St-Antoine S02-340, 850 St-Denis, Montreal, QC, H2X 0A9, CanadaCHU Sainte-Justine Research Center, Montreal, QC, H3T 1C5, CanadaCHU Sainte-Justine Research Center, Montreal, QC, H3T 1C5, CanadaDivision of Endocrinology, Department of Pediatrics, CHU Sainte-Justine and Université de Montréal, Montreal, QC, H3T 1C5, CanadaDivision of Cardiology, Department of Pediatrics, CHU Sainte-Justine and Université de Montréal, Montreal, QC, H3T 1C5, CanadaSocial and Preventive Medicine Department, Université de Montréal, Montreal, QC, H3N 1X7, CanadaSynemorphose Inc., Montreal, QC, H4C 3H2, CanadaDivision of Genetics, Department of Pediatrics, CHU Sainte-Justine and Université de Montréal, Montreal, QC, H3T 1C5, CanadaBackground. 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.http://dx.doi.org/10.1155/2014/328076 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kestens Yan Barnett Tracie Mathieu Marie-Ève Henderson Mélanie Bigras Jean-Luc Thierry Benoit Maxime St-Onge Lambert Marie |
spellingShingle |
Kestens Yan Barnett Tracie Mathieu Marie-Ève Henderson Mélanie Bigras Jean-Luc Thierry Benoit Maxime St-Onge Lambert Marie Innovation through Wearable Sensors to Collect Real-Life Data among Pediatric Patients with Cardiometabolic Risk Factors International Journal of Pediatrics |
author_facet |
Kestens Yan Barnett Tracie Mathieu Marie-Ève Henderson Mélanie Bigras Jean-Luc Thierry Benoit Maxime St-Onge Lambert Marie |
author_sort |
Kestens Yan |
title |
Innovation through Wearable Sensors to Collect Real-Life Data among Pediatric Patients with Cardiometabolic Risk Factors |
title_short |
Innovation through Wearable Sensors to Collect Real-Life Data among Pediatric Patients with Cardiometabolic Risk Factors |
title_full |
Innovation through Wearable Sensors to Collect Real-Life Data among Pediatric Patients with Cardiometabolic Risk Factors |
title_fullStr |
Innovation through Wearable Sensors to Collect Real-Life Data among Pediatric Patients with Cardiometabolic Risk Factors |
title_full_unstemmed |
Innovation through Wearable Sensors to Collect Real-Life Data among Pediatric Patients with Cardiometabolic Risk Factors |
title_sort |
innovation through wearable sensors to collect real-life data among pediatric patients with cardiometabolic risk factors |
publisher |
Hindawi Limited |
series |
International Journal of Pediatrics |
issn |
1687-9740 1687-9759 |
publishDate |
2014-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2014/328076 |
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