The CGM AC app offers healthcare professionals and researchers a user-friendly platform to analyze CGM data and derive essential metrics for monitoring glucose levels. Designed to process glucose data collected at 5-minute intervals, this app calculates two key metrics: AC_Mean and AC_Var, derived from autocorrelation coefficients, alongside the mean (Mean) and standard deviation (Std) of glucose levels. Users can upload CGM data in a specific format, whereupon the app swiftly processes the data and generates insightful results. The app's intuitive interface allows for seamless analysis of multiple glucose profiles, providing valuable insights into glucose variability and autocorrelation patterns. Healthcare professionals can utilize this app to monitor glucose trends in patients with diabetes, assess treatment effectiveness, and make informed clinical decisions. Researchers can leverage its capabilities to conduct studies on glucose dynamics and explore correlations between glucose patterns and various health outcomes. Overall, the CGM AC app serves as a powerful tool for enhancing glucose monitoring and advancing research in diabetes management.

See owner's GitHub repository for more information: https://github.com/HikaruSugimoto/CGM_AC.

Sugimoto, H., Hironaka, K., Nakamura, T., Yamada, T., Miura, H., Otowa-Suematsu, N., Fujii, M., Hirota, Y., Sakaguchi, K., Ogawa, W., & Kuroda, S. (2023). Improved Detection of Decreased Glucose Handling Capacities via Novel Continuous Glucose Monitoring-Derived Indices: AC_Mean and AC_Var. medRxiv. Advance online publication. https://doi.org/10.1101/2023.09.18.23295711

This application was not uploaded by the author, but through their publicly available Github repository, https://github.com/HikaruSugimoto/CGM_AC.