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HUPA-UCM diabetes dataset.

J Ignacio Hidalgo1, Jorge Alvarado2, Marta Botella3

  • 1Universidad Complutense de Madrid, Profesor José García Santesmases 9, Madrid, Spain.

Data in Brief
|July 1, 2024
PubMed
Summary
This summary is machine-generated.

This dataset offers valuable insights into type 1 diabetes management. It includes continuous glucose monitoring, insulin, and lifestyle data to develop predictive models for glucose levels and sleep impacts.

Keywords:
DiabetesGlucose predictionMachine learningT1DM

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Area of Science:

  • Biomedical Informatics
  • Endocrinology
  • Data Science

Background:

  • Type 1 diabetes mellitus (T1DM) management requires continuous monitoring of glucose levels and understanding influencing factors.
  • Integrating physiological and lifestyle data can enhance predictive modeling for T1DM.

Purpose of the Study:

  • To present a comprehensive dataset for T1DM research, encompassing continuous glucose monitoring (CGM), insulin, nutrition, and activity data.
  • To facilitate the development of predictive models for glucose fluctuations, hypoglycemia, and hyperglycemia in individuals with T1DM.
  • To investigate the intricate relationships between sleep patterns and glycemic control in T1DM.

Main Methods:

  • Collected data from 25 individuals with T1DM over at least 14 days.
  • Utilized FreeStyle Libre 2 CGMs for glucose readings and Fitbit Ionic smartwatches for activity and sleep tracking.
  • Recorded insulin doses and meal carbohydrate intake (grams).

Main Results:

  • The dataset includes synchronized CGM, insulin, meal, step, calorie, heart rate, and sleep data.
  • This data enables the exploration of correlations between lifestyle factors, sleep, and glycemic variability.
  • Previous analyses have demonstrated the utility of this dataset for machine learning-based glucose prediction.

Conclusions:

  • This dataset provides a rich resource for advancing T1DM research and personalized management strategies.
  • It supports the development of sophisticated predictive models for glucose control and the identification of key influencing variables.
  • Further research using this dataset can elucidate the impact of sleep on glycemic outcomes in T1DM.