FitVault Case Study
Unlock the potential of wearable data for research institutions and academic organizations.

FitVault specializes in normalizing data from various wearable device brands, enabling the development of evidence-based algorithms that provide users with valid and meaningful feedback.

We focus on specific health markers, such as daily physical activity, exercise training and fitness, sleep health, and cardiometabolic health, as logged across all supported devices. Our goal is to build on these algorithms to enhance our overall health score (known as Health ID) and accurately estimate health behavior risks and changes over time.

In today's world, technology advancements have made commercial health trackers widely popular, allowing individuals to monitor their physical activity, exercise, and sleep patterns. This increased interest also benefits companies concerned with client health risk (e.g. health insurers) by granting access to objective health markers that previously relied on self-reporting. However, the challenge lies in making sense of the vast amount of data, interpreting it in the context of normative values, and determining appropriate interventions for effective behavior change.

FitVault, a data aggregation and normalization company, collects information from wearable devices (e.g. Fitbit, Polar, Suunto, Apple, Samsung, Garmin etc) to provide users with evidence-based feedback on their data as it evolves. Users are motivated to make behavior changes to improve their health by their performance and progress, both personally and compared to peers with similar demographics and activity levels.


In a successful collaboration with the University of Cape Town (UCT), a renowned institution with expertise in physical activity, exercise training, sleep, and obesity as they relate to overall health and well-being, we demonstrated the potential of our wearable device-derived data. Together, we developed algorithms that estimate health risk in these fields, using the latest scientific evidence. UCT's capacity to test and modify these four algorithms through validation studies has further strengthened our solution to assess and provide users with feedback regarding:

Each algorithm uses data (passive and user-defined) generated by wearable fitness devices and provides the user with a score out of 100 for the corresponding health parameter. The algorithms have been tested against in-house data from UCT related to physical activity/inactivity, high/low fitness, healthy/disordered sleep, and cardiometabolic disease risk.

This fruitful partnership with UCT is testament to FitVault's ability to collaborate with institutions and organizations on exciting projects. We invite research institutions and academic organizations to explore our technology as part of their own research projects.

If you are interested in leveraging FitVault's expertise and technology to enhance your research, please reach out to us.

Together, we can unlock the potential of wearable data and contribute to the advancement of health and well-being on a global scale.

The FitVault app

FitVault is scaleable and flexible to suit any use case that requires detailed insights from multiple sources of health data. Enhance your product with the single solution for health data collection, correlation and analysis.



Apply for the Beta version