ABSTRACT
Organizations worldwide have experienced the impact of the COVID-19 Pandemic on employee wellness, stress, and organizational performance. Working both onsite and remotely has benefits and limitations for organizations and employees alike and can be a cause of stress, negatively impacting wellness and performance. The age-old debate about who is responsible for managing employee stress and wellness still prevails. However, both organizations and employees would be unwise to ignore this. The relationship between wellness, stress, and performance is well known, with numerous variables impacting this relationship. Leaders and HR departments have had to become creative in managing employee stress, performance, and wellness. Webinars, communiques, posters, workshops, Pilates classes, newsletters, and the like have been used to help enhance employee wellness. While it has not always been easy to measure wellness and other variables, advances in sensor technology have enabled the collection of physiological and behavioral data via wearables, computing 86devices, and physical and digital sensors on a regular and reliable basis. Wearables include smartwatches and fitness trackers, which typically have direct contact with the user’s body. Software for recording subjective and objective data relating to individual wellness and productivity is widely accessible. Streams of data relating to health, activity, productivity, sleep, meditation, nutrition, geolocation, and environmental variables are generated. Technologies for the synchronization of data, Bluetooth, Wi-Fi, and mobile data, facilitate the compilation of vast databases of individual and population data relating to physiological, behavioral, and environmental factors. This data is shared with applications where it is analyzed to provide greater insight into the user’s wellness. Powerful automatic program interfaces enable the integration of data from multiple applications, and machine learning technology seeks patterns in this data. Many applications thus generate hypotheses against which the data related to the individual’s context may be tested. In this way, personalized insights may be provided to individuals on aspects of their wellness and behavior, such as specific patterns of stress that coincide with screen time, food intake, air temperature, or physical coherence. Notifications related to these integrated insights are sent to the user to indicate when they need to have a break or get active; when they are most productive or stressed; how much time they spend on social media or procrastinate, or when they are most energized. These are all very useful for the organization and the employee in managing wellness. This chapter will establish a conceptual framework for the collection of a stream of employee wellness and productivity data, including objective and subjective measurements to help manage employee wellness. Data from a single-participant case study will be used to explore insights generated by various machine learning algorithms about the subject’s wellness and productivity and the results of various adjustments made by the subject in relation to improving wellness and productivity. Having this information empowers employees to manage their own wellness and allows researchers the opportunity to enhance the framework in anticipation of further research in this area.
