The aging population and the postponement of retirement age are increasing the demand for longer and more sustainable employability of employees. Physical and psychological aspects such as lifestyle and chronic illnesses can hinder the sustainable employability of employees.
Preventive medical research can map out health aspects with regard to sustainable employability and can identify indicators in relation to work at an early stage and be able to make adjustments where possible. However, the many factors that influence sustainable employability do not yet make a reliable and predictive outcome demonstrable.
In the earlier TFF project SHIFT (Sustained Health and Innovative strategies For Technological interventions), a proof-of-concept was developed for these problems of a Decision Support Tool for sustainable employability that works on the basis of fictitious data from a data collection. The project will be further expanded in a study in which the clinical validity of the model is investigated based on data from practice. The aim of the research is to validate the prediction model for sustainable use and make it evidence-based.
Topic
Health monitoring, Artificial Intelligence, Decision Support Systems.
Goals
Previous research has shown that machine learning systems are better able to predict return to work in sick employees than a doctor can do on the basis of questionnaires. In SHIFT-2, the most important parameters for sustainable employability will be identified and verified, which will serve as the basis for the further development of the Decision Support Tool from SHIFT from technical readiness level (TRL) level 4-5 to TRL 7.
Partners
- Saxion Lectoraat Gezondheid en Bewegen
- Saxion Lectoraat Ambient Intelligence
- Immens Advies
- HeartMath Benelux
Project page
Shift 2: sustained health and innovative strategies for technological interventions
Duration
2018 - 2020
Financing
This project was made possible by TechForFuture.
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