Digital biomarkers, potential tools for workplace wellness programmes

Wearable devices and their associated digital biomarkers show significant promise in helping employees improve their health and wellbeing in the workplace.

Despite technological, logistical, and managerial advances in all industries, multiple sources strongly claim that the average employee is only productive for 3 to 4 hours per day (Curtin, 2016). Companies of all shapes and sizes are aware of this and are constantly looking for new ways to improve the productivity of their workforce. In most traditional environments, pushing for an increase in productivity also leads to an increase in stress, one of the main factors behind employee fatigue and, almost paradoxically, decreased productivity.

Striking a balance between maximising productivity and wellbeing simultaneously is one of the key goals for any modern company and, luckily, we now know that these two objectives are not opposed to each other. An employee’s overall health is tightly linked with how they perform in the workplace (Isham, Mair, and Jackson, 2020). With mental health being a massive component of how an employee affects and is affected by the workplace, all interventions focused on maximising production should keep in mind the importance of how focusing solely on productivity could be (no pun intended) counterproductive. Increasing an employee’s workload, without consideration for their mental health, could neutralise any potential improvement on productivity. Monitoring and understanding the relationship between mental health, wellbeing and productivity is quickly becoming a necessity for all companies, leading to widespread interest in tools that could help employers better comprehend their employees.

Modern tools for modern needs

Screening interviews and discussions with employees are definitely useful in assessing both wellbeing and productivity, but modern needs require more sophisticated solutions. Tools like the actigraphs one could find in wearable devices and/or smartphones are being thoroughly researched for the scale at which companies could help their employees reach peak productivity and wellbeing. Actigraphs are wearable sensors that constantly monitor an individual’s physical activity, originally intended to be used for fitness or to measure and evaluate sleeping patterns (Sadeh, 2011). These sensors are now commonplace features in the vast majority of wearable devices, like the Apple Watch or the Fitbit family of products. Nowadays, with the wide acceptance and interest in wearable technology, research groups and companies around the world are looking at how the information that can be collected with these devices could be translated into valuable insights for the workplace. For the more pragmatic and analytic decision-makers, wearable devices, particularly those that can generate objective values representative of an individual’s daily activity are the missing piece in the productivity/wellbeing puzzle. Modern interventions, as simple as requiring employees to participate in X minutes of non-sedentary activity per day, can now be monitored in real time and correlated with how employees perceive their mood and productivity. Not only that, but by the sole fact that these devices are worn by the end-user, employees are also reminded of how they should be conscious about their physical, mental and emotional health.

A joint research effort by Microsoft and the University of California looked into how different digital health interventions could improve both the company’s productivity and their employees’ health states. For approximately 12 days, 40 employees were monitored and assessed on different metrics related mainly to their wellbeing, mood, mental health, and sleep patterns. The exploratory project collected data that coincided with common knowledge, for instance, by showing that better sleep, as measured through actigraph readings, is strongly related to a more positive mood as determined through the PANAS mood scale evaluation (Mark et al., 2016). Also, a strong correlation was found between a negative mood state and how concentrated employees felt throughout the day, proving the potential that sensors, digital biomarkers, and other modern tools could have. A holistic digital health ecosystem, like the one implemented in the 2016 study, could combine subjective, qualitative data, like interviews and questionnaires, with objective, quantitative metrics, like non-sedentary activity and workplace productivity, to create a more accurate representation of the workforce.

Smart(er) interventions

Comprehending the task at hand is a key first step in moving towards a solution that effectively fulfils all of the interested parties’ needs. By generating this complete representation of an employee’s health state, employers could act in more effective and efficient ways, to minimise health risks, increase productivity, and/or maximise wellbeing. Further research is needed before companies are capable of designing interventions that could be measured and evaluated with medical devices, but their relevance in minimising risks has been identified already. Researchers from Massachusetts General Hospital, Harvard Medical School, and Penn State managed to develop a machine-learning algorithm that, by extracting data from actigraphs, could predict whether an individual had been diagnosed with a mood disorder (Jacbson, Weingarden & Wilhelm, 2019). Despite the algorithm’s high degree of accuracy (89%), the researchers did not go as far as to claim that it could be used for diagnostic and/or screening purposes. Digital biomarkers are advancing quickly, and some tools are evolving towards one day being the gold standard for the diagnosis of certain diseases, but they could also be used in the near future in less drastic settings.

Employers are, and perhaps always will be, in need of innovations capable of increasing productivity. There’s no guarantee that wearable devices or wellness apps could be the key, but if certain technologies are capable of assessing an individual’s wellbeing and predicting how they will perform at their job, these could be extremely useful for companies worldwide. By monitoring sleep and activity patterns and combining that knowledge with other relevant biomarkers, employees could show how their current work schedules are not contributing to their productivity and employers could look into rearranging resources to benefit their employees’ productivity-wellbeing balance.

1. Curtin, M. 2016. In an 8-Hour Day, the Average Worker Is Productive for This Many Hours. Inc Magazine.
2. Isham, A., Mair, S. and Jackson, T., 2020. Wellbeing and productivity: a review of the literature. Report for the Economic and Social Research Council.
3. Sadeh, A., 2011. The role and validity of actigraphy in sleep medicine: an update. Sleep medicine reviews, 15(4), pp.259–267.
4. Mark, G., Czerwinski, M., Iqbal, S. and Johns, P., 2016, April. Workplace indicators of mood: Behavioral and cognitive correlates of mood among information workers. In Proceedings of the 6th International Conference on Digital Health Conference (pp. 29–36).
5. Jacobson, N.C., Weingarden, H. and Wilhelm, S., 2019. Digital biomarkers of mood disorders and symptom change. NPJ digital medicine, 2(1), pp.1–3.

This article was written by Adrián Garcia, a Science Editor at Harper Institute.