Literature DB >> 23427866

What are the quality of life-related benefits and losses associated with real-time continuous glucose monitoring? A survey of current users.

William H Polonsky1, Danielle Hessler.   

Abstract

BACKGROUND: How does real-time (RT) continuous glucose monitoring (CGM) affect quality of life (QOL)? We explored the types and frequencies of diabetes-specific QOL changes resulting from RT-CGM as reported by current users and investigated what patient-reported factors predict these changes. SUBJECTS AND METHODS: Current RT-CGM users (n = 877) completed an online questionnaire investigating perceived QOL benefits/losses since RT-CGM initiation and RT-CGM attitudes and behavior. Exploratory factor analysis (EFA) examined the 16 QOL benefit/loss items to identify underlying factors. Regression analyses examined associations between demographics and RT-CGM attitudes and behavior with the QOL factors emerging from the EFA.
RESULTS: Three major QOL factors emerged: Perceived Control over Diabetes, Hypoglycemic Safety, and Interpersonal Support. QOL improvement was common for Perceived Control over Diabetes and Hypoglycemic Safety (86% and 85% of respondents, respectively), although less common for Interpersonal Support (37%). Consistent independent predictors of perceived benefits were greater confidence in using RT-CGM data (P<0.001), satisfaction with device accuracy (P ≤ 0.05) and usability (P<0.01), older age (P<0.01), more frequent receiver screen views (P<0.05), and use of multiple daily injections (Hypoglycemic Safety and Interpersonal Support, P ≤ 0.05).
CONCLUSIONS: Diabetes-specific QOL benefits resulting from RT-CGM were common. Major predictors of QOL benefits were satisfaction with device accuracy and usability and trust in one's ability to use RT-CGM data, suggesting that "perceived efficacy," for both device and self, are key QOL determinants. Psychoeducational strategies to boost confidence in using RT-CGM data and provide reasonable device expectations might enhance QOL benefits.

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Year:  2013        PMID: 23427866     DOI: 10.1089/dia.2012.0298

Source DB:  PubMed          Journal:  Diabetes Technol Ther        ISSN: 1520-9156            Impact factor:   6.118


  33 in total

1.  Are Systematic Reviews and Meta-Analyses Appropriate Tools for Assessing Evolving Medical Device Technologies?

Authors:  David Price; Claudia Graham; Christopher G Parkin; Thomas A Peyser
Journal:  J Diabetes Sci Technol       Date:  2015-09-29

2.  Analysis: The Accuracy and Efficacy of the Dexcom G4 Platinum Continuous Glucose Monitoring System.

Authors:  Cornelis A J van Beers; J H DeVries
Journal:  J Diabetes Sci Technol       Date:  2015-04-27

3.  Impact of Human Factors Testing on Medical Device Design: Validation of an Automated CGM Sensor Applicator.

Authors:  Robert North; Christine Pospisil; Ryan J Clukey; Christopher G Parkin
Journal:  J Diabetes Sci Technol       Date:  2019-02-14

4.  Development of a Novel Tool to Support Engagement With Continuous Glucose Monitoring Systems and Optimize Outcomes.

Authors:  Katharine D Barnard-Kelly; William H Polonsky
Journal:  J Diabetes Sci Technol       Date:  2019-05-21

5.  Salient characteristics of youth with type 1 diabetes initiating continuous glucose monitoring.

Authors:  Gabriela H Telo; Lisa K Volkening; Deborah A Butler; Lori M Laffel
Journal:  Diabetes Technol Ther       Date:  2015-03-06       Impact factor: 6.118

6.  Role of continuous glucose monitoring in the management of glycogen storage disorders.

Authors:  Mrudu Herbert; Surekha Pendyal; Mugdha Rairikar; Carine Halaby; Robert W Benjamin; Priya S Kishnani
Journal:  J Inherit Metab Dis       Date:  2018-05-25       Impact factor: 4.982

Review 7.  Psychosocial Aspects of Continuous Glucose Monitoring: Connecting to the Patients' Experience.

Authors:  Thomas Kubiak; Caroline G Mann; Katherine C Barnard; Lutz Heinemann
Journal:  J Diabetes Sci Technol       Date:  2016-06-28

8.  Impact of Frequent and Persistent Use of Continuous Glucose Monitoring (CGM) on Hypoglycemia Fear, Frequency of Emergency Medical Treatment, and SMBG Frequency After One Year.

Authors:  James J Chamberlain; Dana Dopita; Emily Gilgen; Annie Neuman
Journal:  J Diabetes Sci Technol       Date:  2015-09-09

9.  Baseline Psychosocial Characteristics Predict Frequency of Continuous Glucose Monitoring in Youth with Type 1 Diabetes.

Authors:  Dayna E McGill; Lisa K Volkening; Deborah A Butler; Kara R Harrington; Michelle L Katz; Lori M Laffel
Journal:  Diabetes Technol Ther       Date:  2018-05-04       Impact factor: 6.118

10.  Biochemical, Physiological and Psychological Changes During Endurance Exercise in People With Type 1 Diabetes.

Authors:  Neil E Hill; Christopher Campbell; Paul Buchanan; Midge Knight; Ian F Godsland; Nick S Oliver
Journal:  J Diabetes Sci Technol       Date:  2016-09-30
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