A user-friendly web tool for custom analysis of continuous glucose monitoring data

Abstract

Continuous Glucose Monitoring (CGM) systems have revolutionized diabetes management by providing real-time blood glucose tracking. However, there is a need for free, easily accessible tools for analysis of CGM data in relation to specific events like meals or exercise, allowing improved understanding of the effects of lifestyle factors and physiological changes on glucose control. Currently, the complexity of such analyses often requires extensive technical skills, thus restricting use among the majority of researchers and clinicians. Developing user-friendly web applications to facilitate this analysis could significantly broaden accessibility and utility. To address this, we developed Diametrics, a web-based application designed to make CGM data analysis both accessible and user-friendly. Diametrics supports a variety of CGM devices and data formats, offering a flexible platform suitable for diverse clinical and research needs. Its intuitive interface allows users to navigate and analyze data with ease, without requiring extensive technical knowledge. Diametrics is free to use at https://diametrics.org and is accompanied by comprehensive documentation and instruction videos. All underlying code is publicly available at https://github.com/cafoala/diametrics-webapp. As well as having the standard features available across existing online CGM analysis tools, Diametrics has a number of novel features (Figure 1). These include capacity for simultaneous upload of multiple CGM files, customizable analysis options that can cater to specific research or clinical questions, and interactive data visualizations. Beyond simple extraction and analysis of clinical CGM metrics, Diametrics has the unique capacity for custom integration and analysis of glucose data related to specific events such as meals, exercise, or medication intake. This functionality not only enhances the usability of CGM technology but also opens new avenues for personalized diabetes management and research by significantly improving our understanding of glucose dynamics at points of interest. A case study demonstrating the functionality of Diametrics is available at https://youtu.be/bfiQRGhCLh4. We validated Diametrics through a comparative analysis with the iglu R package, a well-established tool for CGM data analysis [1]. Utilizing data from 418 participants from three studies [2-4], we examined agreement between Diametrics and iglu in computation of metrics recommended by the American Diabetes Association [5], including average glucose levels, time in ranges, and glycemic variability indices. We observed high concordance between Diametrics and iglu, with very high correlation (r>0.999) and near perfect agreement for all metrics [6]. This high level of concordance underscores the accuracy of Diametrics in replicating essential CGM metrics, validating its efficacy. In conclusion, Diametrics represents a significant advancement in the field of diabetes technology. By simplifying and democratizing the analysis of CGM data, it holds the promise of enhancing diabetes management and research, making advanced data analysis accessible to a broader audience. Diametrics has the potential to be a valuable tool for both clinicians and researchers, facilitating better outcomes in diabetes care and fostering further research into personalized diabetes management strategies.