Development of mixed method/technology approach improving integral personalized workplace wellbeing monitoring and ex ante guidance for effective data-driven and timely prevention

Background: The effectiveness of programs to reduce burnout and workplace stress is limited. Companies are in need 1) to be able to manage wellbeing of workers in a more personalized and data-driven way, 2) to be in control over which resources contribute to wellbeing most efficiently and cost-effectively, in order to 3) be more creative, effective and healthy in their business.  

Goal: Development of an innovative approach based upon complex systems theory,  using mixed methodologies and technologies, to identify subgroups of workplace wellbeing in timeseries of data, detecting predictors within these subgroups, detecting most appropriate, identifying timely and potentially effective (set of) actions for both employee and organization, in order to design an effective collective approach towards workplace wellbeing while improving production, and prevention costs.  

Approach: In IWW phase 3 (this project), a research version of WellatWork monitor - based upon the prototype Wellbeing Dashboard (phase 1-2) was developed in IWW2 to collect the appropriate longitudinal wellbeing data of employees working at three partner-organisations. Several innovative model- and data driven system dynamics approaches were developed, and insights appropriately integrated, to identify subgroups and early predictors within these subgroups. Our aim was to develop a combination of predictive models (scenario simulations), systemic time series data, and real-world data and studies which together can accelerate designing effective preventive programs for specific subgroups while giving feedback on status of wellbeing of different end users (individual, team, (HR) management) (diagnosis) as well as scenarios of (possible) effects of interventions over time (prognosis).  

Impact: This project already led to the actual marketing of WellatWork monitor (version 1) by the partners involved. Further data-analyses and development for better insights in predictors and early warning signals for specific subgroups of employees (in different states of health and wellbeing) in order to improve workplace wellbeing, higher performance, reduction of absenteeism, and cost savings will be needed Results will be shared and opportunities for collecting the appropriate data and marketing as a learning and guidance tool will be further explored.