A novel tool to predict youth who will show recommended usage of diabetes technologies

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Abstract

Background and Objective:

Controversy exists regarding which individuals will benefit most from commencement of diabetes technologies such as continuous subcutaneous insulin infusion (CSII) or continuous glucose monitoring systems (CGMS), such as ‘real-time’ sensor-augmented pumping (SAP). Because higher usage correlates with haemoglobin A1c (HbA1c) achieved, we aimed to predict future usage of technologies using a questionnaire-based tool.

Subjects:

The tool was distributed to two groups of youth with type 1 diabetes; group A (n = 50; mean age 12 ± 2.5 yr) which subsequently commenced ‘real-time’ CGMS and group B (n = 47; mean age 13 ± 3 yr) which commenced CSII utilisation.

Methods:

For the CGMS group, recommended usage was ≥5 days (70%) per week [≥70% = high usage (HU); <70% = low usage (LU)], assessed at 3 months. In the CSII group, HU was quantified as entering ≥5 blood sugars per day to the pump and LU as <5 blood sugars per day, at 6 months from initiation. Binary logistic regression with forward stepwise conditional was used to utilise tool scales and calculate an applied formula.

Results:

Of the CGMS group, using gender, baseline HbA1c, and two subscales of the tool generated a formula which predicted both high and low usage with 92% accuracy. Twelve (24%) showed HU vs. 38 who exhibited LU at 3 months.

Results:

Of the CSII group, 32 (68%) exhibited HU vs. 15 who exhibited LU at 6 months. Four tool items plus gender predicted HU/LU with 95% accuracy.

Conclusions:

This pilot study resulted in successful prediction of individuals who will and those who will not go on to show recommended usage of CSII and CGMS.

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