Long-term monitoring of personal health parameters for the prevention of cardiovascular diseases

Networked consumer health devices such as activity trackers, sport watches networked scales, or sleep monitors provide unprecedented insights into health parameters under real-life conditions. In combination with their connection to internet services they facilitate unprecented applications. However, their use is challenged by, amongst others, unknown reliability and relevance of measurements, and a lack of integration of diverse data sources to provide reasonable and meaningful information. Many health professionals are therefore critical and see these devices as toys rather than tools. This book investigates how smart health devices can be part of a social-technical system for the prevention of cardiovascular diseases. A comprehensive concept is suggested, addressing three key aspects: The human is a central factor in such a system, having a dual role as both, the ultimate consumer of the health information, and as a producer of data, interacting with the devices to collect health data in daily life. The user’s interaction with multiple devices over extended periods of time is therefore investigated to understand the impact on measurements and on the functioning of the overall preventative system. In the light of the user’s role the properties and quality of the data delivered by smart health devices in relation to the applications’ requirements is researched. A data quality model is suggested, comprising three dimensions, accuracy, relevance, and availability. There is a trade-off between these three dimensions, induced by the user’s effort for interacting with the devices. Approaches are suggested how this trade-off can be balanced, and how data quality issues can be mitigated. Requirements for the use of data in preventative applications are identified and a conceptual framework is suggested to process data from multiple diverse smart health devices for provision of information for cardiovascular prevention. The framework is based on decomposition of physical into logical devices, enrichment of data into primary health features, and aggregation over time into secondary health features for utilization of information in preventative applications. This enables interventions and applications for, amongst others, long-term change and persistence of health behaviours, improving self-knowledge and health literacy, and decision support and coaching.