Background.While increasing evidence links environments to health behavior, clinicians lack information about patients� physical\r\nactivity levels and lifestyle environments. We present mobile health tools to collect and use spatio-behavioural lifestyle data for\r\npersonalized physical activity plans in clinical settings. Methods.The Dyn@mo lifestyle intervention was developed at the Sainte-\r\nJustine University Hospital Center to promote physical activity and reduce sedentary time among children with cardiometabolic\r\nrisk factors. Mobility, physical activity, and heart rate were measured in free-living environments during seven days. Algorithms\r\nprocessed data to generate spatio-behavioural indicators that fed a web-based interactive mapping application for personalised\r\ncounseling. Proof of concept and tools are presented using data collected among the first 37 participants recruited in 2011. Results.\r\nValid accelerometer data was available for 5.6 (SD = 1.62) days in average, heart rate data for 6.5 days, and GPS data was\r\navailable for 6.1 (2.1) days. Spatio-behavioural indicators were shared between patients, parents, and practitioners to support\r\ncounseling. Conclusion. Use of wearable sensors along with data treatment algorithms and visualisation tools allow to better\r\nmeasure and describe real-life environments, mobility, physical activity, and physiological responses. Increased specificity in\r\nlifestyle interventions opens new avenues for remote patient monitoring and intervention.
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