People with mild conditions, more serious chronic ones, or in rehabilitation, are being remotely monitored and advised in their common, every-day settings by medical professionals. Professionals of para-medical domains (wellness, fitness, nutrition) monitor and coach their clientele. People themselves monitor their quality of life, keeping track of adverse events like migraines or athletic injuries and the effect of those on their lifestyle. Such systems are most important in the post-emergency COVID-19 stage the world is now entering, where large portions of the population need to be monitored for symptoms, incurring the smallest possible strain to the health systems. In such cases the authorities and healthcare institutions need to monitor and advise the entire population while not in the national health system, but at home. The data of importance in such outbreaks are not only the objective measurements but also the subjectively reported intensity of symptoms, i.e. what the people feel about themselves. Such information properly analysed can yield groups of people to be advised differently depending on their lifestyle and symptoms or other reported information.

All the above situations have in common the (self) monitoring of people for long periods of time in their everyday life, for collecting and analysing real-world data (RWD) in order to coach them. The collected RWD fall in two categories: measured (objective) and reported (subjective). Objective RWD are physiological ones (vitals, physical activity), measured using wearables and other IoT devices. Subjective RWD include quality of life self-assessments and the different adverse conditions, as collected using questionnaires. The subjective RWD usually are the outcomes of people’s lifestyle, as it is measured by past objective RWD. While there are multiple systems for measuring physical activity, they are usually centred around the subject being monitored, and report to him or her. There is little reasoning about the subject’s lifestyle. There is no collection of subjective RWD, nor any correlation of them to the objectively measured ones nor any attempt to model subjects into categories, handling each category differently.

Involvement of UC partners

iSPRINT is a high tech SME involved in a variety of ehealth services. The company applies Signal Processing and Machine Learning / Artificial Intelligence techniques on signals collected from IoT devices to better understand Physical Data and link them to clinical outcomes and drug effectiveness. These algorithms are then integrated into systems encompassing both software and hardware, building systems that observe and understand. iSPRINT will provide the Healthentia platform (eClinical platform sprung from two EU co-funded projects eWALL2013, GOAL2016) and will be involved in the definition of the use-case scenarios and the evaluation, the business relevance, the technical requirements and possible solution architectures for the eHealth use case.