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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    This study models data gaps in continuous glucose monitoring (CGM) sensors for diabetes management. A Markov model accurately simulates missing data, aiding the development of reliable diabetes applications.

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    Area of Science:

    • Biomedical Engineering
    • Medical Informatics
    • Diabetes Technology

    Background:

    • Continuous glucose monitoring (CGM) is vital for diabetes management, providing real-time glucose data.
    • Understanding and modeling sensor errors, particularly data gaps, is crucial for developing robust diabetes applications.
    • Data gaps in CGM arise from disconnections or temporary sensor malfunctions, impacting data continuity.

    Purpose of the Study:

    • To develop and validate a statistical model for simulating data gaps in continuous glucose monitoring (CGM) sensor data.
    • To accurately characterize the occurrence and duration of data gaps in CGM data for type 1 diabetes simulators.
    • To assess the utility of a Markov model in replicating real-world data gap patterns.

    Main Methods:

    • Analysis of data gap statistics (frequency, distribution, duration) from 167 adult Dexcom G6 CGM sensor users.
    • Development of a two-state Markov model to simulate data gap occurrence.
    • Comparison of real-world data gap statistics with those generated by the Monte Carlo simulation of the Markov model.

    Main Results:

    • The developed two-state Markov model effectively describes the statistics of data gaps in CGM data.
    • Simulated data gap patterns closely matched the observed occurrence and duration in real-world data.
    • The model provides a reliable method for generating realistic data gap scenarios.

    Conclusions:

    • A two-state Markov model accurately simulates data gaps in continuous glucose monitoring (CGM) data.
    • This modeling approach is valuable for creating realistic datasets for testing and developing diabetes management technologies.
    • The findings support the use of simulated data gaps in the design of type 1 diabetes simulators and applications.