Literature DB >> 25643603

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

Orla M Neylon1, Timothy C Skinner2, Michele A O'Connell1, Fergus J Cameron1.   

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. 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.
© 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Entities:  

Keywords:  CGMS; CSII; HbA1c; self-care; type 1 diabetes

Mesh:

Year:  2015        PMID: 25643603     DOI: 10.1111/pedi.12253

Source DB:  PubMed          Journal:  Pediatr Diabetes        ISSN: 1399-543X            Impact factor:   4.866


  4 in total

1.  Predicting Success with a First-Generation Hybrid Closed-Loop Artificial Pancreas System Among Children, Adolescents, and Young Adults with Type 1 Diabetes: A Model Development and Validation Study.

Authors:  Gregory P Forlenza; Tim Vigers; Cari Berget; Laurel H Messer; Rayhan A Lal; Marina Basina; David M Maahs; Korey Hood; Bruce Buckingham; Darrell M Wilson; R Paul Wadwa; Kimberly A Driscoll; Laura Pyle
Journal:  Diabetes Technol Ther       Date:  2021-12-01       Impact factor: 7.337

2.  In-home nighttime predictive low glucose suspend experience in children and adults with type 1 diabetes.

Authors:  Laurel H Messer; Peter Calhoun; Bruce Buckingham; Darrell M Wilson; Irene Hramiak; Trang T Ly; Marsha Driscoll; Paula Clinton; David M Maahs
Journal:  Pediatr Diabetes       Date:  2016-04-29       Impact factor: 4.866

3.  Measuring Self-Efficacy in the Context of Pediatric Diabetes Management: Psychometric Properties of the Self-Efficacy for Diabetes Scale.

Authors:  Jason Van Allen; Amy E Noser; Andrew K Littlefield; Paige L Seegan; Mark Clements; Susana R Patton
Journal:  J Pediatr Psychol       Date:  2018-03-01

4.  Use of continuous glucose monitoring for sport in type 1 diabetes.

Authors:  Alif Abdulrahman; Janisha Manhas; Hannah Linane; Mark Gurney; Catriona Fitzgerald; Esther O'Sullivan
Journal:  BMJ Open Sport Exerc Med       Date:  2018-12-17
  4 in total

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