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

TY - JOUR

T1 - A novel tool to predict youth who will show recommended usage of diabetes technologies

AU - Neylon, Orla M.

AU - Skinner, Timothy C.

AU - O'Connell, Michele A.

AU - Cameron, Fergus J.

PY - 2016/5/1

Y1 - 2016/5/1

N2 - 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±2.5yr) which subsequently commenced 'real-time' CGMS and group B (n=47; mean age 13±3yr) which commenced CSII utilisation. Methods: For the CGMS group, recommended usage was ≥5days (70%) per week [≥70%=high usage (HU); <70%=low usage (LU)], assessed at 3months. 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 6months 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 3months. Of the CSII group, 32 (68%) exhibited HU vs. 15 who exhibited LU at 6months. 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.

AB - 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±2.5yr) which subsequently commenced 'real-time' CGMS and group B (n=47; mean age 13±3yr) which commenced CSII utilisation. Methods: For the CGMS group, recommended usage was ≥5days (70%) per week [≥70%=high usage (HU); <70%=low usage (LU)], assessed at 3months. 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 6months 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 3months. Of the CSII group, 32 (68%) exhibited HU vs. 15 who exhibited LU at 6months. 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.

KW - CGMS

KW - CSII

KW - HbA1c

KW - Self-care

KW - Type 1 diabetes

UR - http://www.scopus.com/inward/record.url?scp=84922985292&partnerID=8YFLogxK

U2 - 10.1111/pedi.12253

DO - 10.1111/pedi.12253

M3 - Journal article

C2 - 25643603

AN - SCOPUS:84922985292

SN - 1399-543X

VL - 17

SP - 174

EP - 183

JO - Pediatric Diabetes

JF - Pediatric Diabetes

IS - 3

ER -