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. 1. Introduction The rising prevalence of obesity and cardiometabolic risk observed among youth has led to predictions of decreased life expectancy among the next generation of North Americans, a first in history [1]. The American Heart Association has reclassified obesity as a “major, modifiable” risk factor for coronary heart disease (CHD) and diabetes [2]. This condition is modifiable through dietary and physical activity changes [3]. Classical clinical interventions promoting a healthy lifestyle are based primarily on counseling not always tailored to individual’s profile and on structured exercise programs that have proven to be complex, costly to maintain, and have long-term poor adherence. Sustainable interventions need to focus on interindividual specificity [4, 5] and the development of personalized activity plans [6]. Advances in mobile health and wearable devices offer new ways to collect and interpret data on environments, behaviours, physiology and well-being. Recently, a clinical cardiac rehabilitation intervention among adults using a wearable Electrocardiogram (EKG), a
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