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This article explains what time time dimensions are, why they matter for health research and business data analysis, and how they solve the common but frustrating problem of analyzing time-series data that sits at variable time scales. Ready-to-use code AI assistant prompts to generate these tables is freely available in the EFDC AI Prompt Database on GitHub, with support for multiple platforms including Power BI / Power Query (M), Python, R, SQL, JavaScript, Lua, PowerShell, and Bash.

This article lists some of the common glucose meters being utilized by U-M researchers, as well as upcoming devices, as of the date of this writing.

The DepressionCenter/mobile-tech-illustrations code repository is an open source collection of diagrams and 3D models showcasing mobile technologies devices used in research, such as wearables, nearables, intermittent wearables, and smartphone sensors. Initially created for the MeTRIC Symposium, it features interactive human body models with wearable placements, network node diagrams showing compatibility between different glucose and insulin devices, and more.

Continuous glucose monitors (CGMs) provide important metrics about the health of study participants. They can provide a time-series of estimated glucose values, and metrics around hypoglycemia episodes (low blood sugar). However, CGMs may falsely read low glucose values and issue "low glucose" alerts when they are being depressed, commonly known as "compression lows." This articles explores some of the common causes and solutions.