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Toward a Personalized Basal Tuner for Detecting Basal Rate Inaccuracies in Type 1 Diabetes Mellitus Without Meal

Daniel Gasca Garcia1, Hood Thabit2,3, Paul W Nutter1

  • 1Department of Computer Science, University of Manchester, Kilburn Building, Oxford Rd, Manchester, M13 9PL, United Kingdom, 44 7565135239.

JMIR Diabetes
|November 28, 2025
PubMed
Summary
This summary is machine-generated.

An adaptive algorithm accurately identifies basal rate (BR) inaccuracies in type 1 diabetes management without meal data. This personalized basal tuner (PBT) improves BR settings, reducing hypo- or hyperglycemia risks.

Keywords:
PBTT1DMadaptive basal insulinblood glucose managementmeal detectionpersonalized basal tunertype 1 diabetes mellitus

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Area of Science:

  • Endocrinology
  • Biomedical Engineering
  • Data Science

Background:

  • Basal rate (BR) adjustment is critical for type 1 diabetes mellitus (T1DM) management, comprising 30-50% of daily insulin needs.
  • Current closed-loop systems revert to manual mode (user's usual pump BR) during failures, posing risks.
  • Access to closed-loop systems is limited in low- and middle-income countries and among the uninsured.
  • Accurate BR adjustment remains challenging, often leading to hypoglycemia or hyperglycemia.

Purpose of the Study:

  • To propose an adaptive algorithm for identifying BR inaccuracies using continuous glucose monitoring (CGM) data.
  • To develop a method that does not require meal intake information for BR assessment.
  • To evaluate the practicality and precision of this novel approach compared to existing methods.

Main Methods:

  • Utilized the OhioT1DM dataset for algorithm implementation.
  • Excluded bolus insulin profiles lacking meal data and calculated hourly blood glucose (BG) relative levels.
  • Assessed deviations from recommended BR using reliability measures and BG relative change.
  • Employed noninferiority analysis and classification precision metrics for evaluation.

Main Results:

  • The no-meal method demonstrated noninferiority in 9 out of 12 participants (using a 20% noninferiority margin).
  • Classification precision averaged 73.9% for correctly identifying meals per participant.
  • Analysis revealed that 99.3% of BG relative values unaffected by external factors indicated incorrect BR settings.

Conclusions:

  • The proposed personalized basal tuner (PBT) algorithm offers a superior method for BR analysis without meal data.
  • Optimally set BR is essential to prevent persistent hypo- or hyperglycemia during closed-loop system failures.
  • Future work aims to implement this algorithm clinically, particularly in resource-limited settings, to improve glycemic control and time in range.