From Fitbits tracking steps and heart rate to apps like Headspace and Calm helping with sleep and stress, wearable tech and health apps have become part of everyday life. Many companies now offer these tools through corporate subscriptions, hoping to boost employee health and productivity. On the surface, it seems like a win-win: healthier employees should perform better. But simply giving people access to these tools and expecting them to manage their health on their own often falls short. Without real support - especially from managers - employees struggle to make meaningful changes to how they work, which may be the root of their stress and health issues. As a result, many end up pushing through, neglecting their well-being until they reach breaking point. This is especially true in competitive environments where long hours are seen as a sign of dedication and rewarded with promotions or bonuses.
This brings up an important question: if traditional wellness initiatives aren’t enough to drive lasting improvements in health and productivity, what more can organizations do? Researchers at the KIN Center for Digital Innovation looked to elite sports, where biometric data is already used to improve performance and well-being. What can organizations learn from how athletes and coaches use this data?
That question is at the heart of a new study by Lorna Downie, a PhD candidate in the AI@Work research group, together with KIN colleagues Marleen Huysman and Ella Hafermalz, and Stella Pachidi from King’s College London. Their paper, On the Right Track? Studying the Use of Biometric Data to Manage People in Organizations, has been published in Academy of Management Discoveries this June. The paper is part of Downie’s PhD research, based on years of observations and interviews with athletes and coaches preparing for the 2024 Summer Olympics and Paralympics.
Downie and the team discovered that while biometric data can indeed boost performance, its effective use requires more than just monitoring numbers on a dashboard. Why? Because biometric data has unique characteristics which together set it apart from traditional performance data.
First, biometric data is temporal. Some devices track data continuously, while others take snapshots at specific moments. Managers and employees must decide whether to act on every minor fluctuation in the data or wait for clear patterns to emerge.
Second, biometric data is intimate. Metrics like heart rate, sleep patterns, and stress levels are personal. These are not typical things employees would openly share with their managers. To collect and be able to use this data effectively, a high level of transparency and trust is required.
Finally, biometric data lacks context. A spike in heart rate, for example, could signify stress, excitement, or simply too much caffeine. Without context, these numbers don’t tell the full story. To understand what’s really happening, managers and employees need to engage in thoughtful conversations about the data.
So, what does this mean for organizations? It means that collaboration is key. Making use of biometric data takes time, effort, and trust from both sides, which can create some tough decisions for managers. With so much data coming in, managers may need to choose who gets their attention first. Moreover, what happens if biometric data uncovers a worker’s health issue? Managers may find themselves with responsibilities they hadn’t signed up for. This research highlights the complex trade-offs involved in using biometric data, going far beyond the usual debate over privacy versus efficiency.
Where does this leave us? The research shows that biometric data has the potential to improve both performance and well-being. However, these benefits don’t happen automatically. They require close collaboration between managers and employees to interpret and act on the data effectively - a process that can be exhausting. The real question, as the research points out, is whether the time, effort, and trust required to make use of biometric data are worth the performance gains - or if the added complexity creates more challenges than it solves.
Want to read more? Take a look at the full paper here.
Lorna Downie is a PhD candidate in the AI@Work research group at the KIN Center for Digital Innovation. She explores how data and algorithms are shaping the way we work and organise. Her PhD project - supported by the BI Norwegian Business School and funded by the Research Council of Norway (RCN) - dives into the high-performance world of elite sport. As part of her research, Lorna has been embedded with several top sports organizations, following how they use data and technology to train for, compete in, and review their performance around the Paris Olympic and Paralympic Games.