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

Orla Neylon, Timothy Skinner, Michele O'Connell, F Cameron

    Research output: Contribution to journalArticleResearchpeer-review

    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.

    Original languageEnglish
    Pages (from-to)174-183
    Number of pages10
    JournalPediatric Diabetes
    Volume17
    Issue number3
    DOIs
    Publication statusPublished - 2016

    Fingerprint

    Subcutaneous Infusions
    Insulin
    Technology
    Blood Glucose
    Hemoglobins
    Type 1 Diabetes Mellitus
    Logistic Models

    Cite this

    Neylon, Orla ; Skinner, Timothy ; O'Connell, Michele ; Cameron, F. / A novel tool to predict youth who will show recommended usage of diabetes technologies. In: Pediatric Diabetes. 2016 ; Vol. 17, No. 3. pp. 174-183.
    @article{68cfcc27c5154aa8af154ea378b25109,
    title = "A novel tool to predict youth who will show recommended usage of diabetes technologies",
    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.",
    author = "Orla Neylon and Timothy Skinner and Michele O'Connell and F Cameron",
    year = "2016",
    doi = "10.1111/pedi.12253",
    language = "English",
    volume = "17",
    pages = "174--183",
    journal = "Pediatric Diabetes",
    issn = "1399-543X",
    publisher = "Wiley-Blackwell",
    number = "3",

    }

    A novel tool to predict youth who will show recommended usage of diabetes technologies. / Neylon, Orla; Skinner, Timothy; O'Connell, Michele; Cameron, F.

    In: Pediatric Diabetes, Vol. 17, No. 3, 2016, p. 174-183.

    Research output: Contribution to journalArticleResearchpeer-review

    TY - JOUR

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

    AU - Neylon, Orla

    AU - Skinner, Timothy

    AU - O'Connell, Michele

    AU - Cameron, F

    PY - 2016

    Y1 - 2016

    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 ± 2.5 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.

    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 ± 2.5 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.

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

    U2 - 10.1111/pedi.12253

    DO - 10.1111/pedi.12253

    M3 - Article

    VL - 17

    SP - 174

    EP - 183

    JO - Pediatric Diabetes

    JF - Pediatric Diabetes

    SN - 1399-543X

    IS - 3

    ER -