Conceptualization of a personalized ecoach for wellness promotion

Abstract

Evidence-based health promotion programs implement clinical practice guidelines built upon results of clinical trials with a definite number of participants, collected during a specific period of time. Wearable technologies allow for continuous observation of wellness parameters of multiple citizens, combined with monitoring of activities and context parameters involved in citizens' wellness. A statistical inference model can describe the relation between multidimensional activities and context parameters, the wellness of an individual and a comparable reference group, utilizing machine learning techniques and knowledge from continuous observations of multiple citizens. This paper presents a holistic concept of a coach system, namely eCoach, that combines specialized medical evidence available from randomized control trials, with individual and reference knowledge to create and reinforce wellness-based recommendations. The eCoach adapts these recommendations in a continuous personalized coaching dialog addressing citizen's needs and preferences.