Abstract summary 

Health and Wellbeing Living Labs are a valuable research infrastructure for exploring innovative solutions to tackle complex healthcare challenges and promote overall wellbeing. A knowledge gap exists in categorizing and understanding the types of ICT tools and technical devices employed by Living Labs.Define a comprehensive taxonomy that effectively categorizes and organizes the digital data collection and intervention tools employed in Health and Wellbeing Living Lab research studies.A modified consensus-seeking Delphi study was conducted, starting with a pre-study involving a survey and semistructured interviews (N=30) to gather information on existing equipment. The follow-up three Delphi rounds with a panel of living lab experts (R1 N=18, R2 - 3 N=15) from 10 different countries focused on achieving consensus on the category definitions, ease of reading, and included subitems for each category. Due to the controversial results in the 2nd round of qualitative feedback, an online workshop was organized to clarify the contradictory issues.The resulting taxonomy included 52 subitems, which were divided into three levels as follows: The first level consists of 'devices for data monitoring and collection' and 'technologies for intervention.' At the second level, the 'data monitoring and collection' category is further divided into 'environmental' and 'human' monitoring. The latter includes the following third-level categories: 'biometrics,' 'activity and behavioral monitoring,' 'cognitive ability and mental processes,' 'electrical biosignals and physiological monitoring measures,' '(primary) vital signs,' and 'body size and composition.' At the second level, 'technologies for intervention' consists of 'assistive technology,' 'extended reality - XR (VR & AR),' and 'serious games' categories.A common language and standardized terminology are established to enable effective communication with living labs and their customers. The taxonomy opens a roadmap for further studies to map related devices based on their functionality, features, target populations, and intended outcomes, fostering collaboration and enhancing data capture and exploitation.

Authors & Co-authors:  Petsani Santonen Merino-Barbancho Epelde Bamidis Konstantinidis

Study Outcome 

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