Literature DB >> 32848350

Use of Ecological Momentary Assessment to Measure Self-Monitoring of Blood Glucose Adherence in Youth With Type 1 Diabetes.

Jennifer L Warnick1, Sarah C Westen1, Anastasia Albanese-O'Neill1, Stephanie L Filipp1, Desmond Schatz1, Michael J Haller1, David M Janicke1.   

Abstract

OBJECTIVE: Daily self-monitoring of blood glucose (SMBG) is essential for type 1 diabetes management yet is challenging during adolescence. Ecological momentary assessment (EMA) is the repeated sampling of behaviors and experiences in real time in the natural environment. The purpose of this study was to evaluate 1) the validity of self-reported SMBG values via text message-delivered EMA surveys compared with objective SMBG values via glucose meters and 2) in-the-moment motivators and barriers to performing SMBG in a pediatric type 1 diabetes population.
METHODS: Youth (n = 62, aged 11-21 years) with type 1 diabetes received three text messages daily for 10 days containing surveys inquiring about SMBG engagement. Objective SMBG values were downloaded from glucose meters.
RESULTS: On average, participants reported performing SMBG 4 times/day. Of the self-reported SMBG values, 39.6% were accurate. Inaccurate values included additions (i.e., self-reported value with no objective value), omissions (i.e., objective value with no self-reported value), and alterations (difference between self-report and objective SMBG values ≥10 mg/dL). Of the matched pairs of self-reported and objective SMBG values, 41.3% were altered. Bland-Altman plots determined that the mean difference between self-reported and objective glucose data were -5.43 mg/dL. Participants reported being motivated to check their blood glucose because it was important for their health, and reported barriers included wanting to ignore the task, forgetting, and not having devices.
CONCLUSION: Youth's self-reported SMBG values may not align with objective readings. The results of this study can facilitate future research to determine individual factors related to SMBG and accuracy of self-reporting.
© 2020 by the American Diabetes Association.

Entities:  

Year:  2020        PMID: 32848350      PMCID: PMC7428657          DOI: 10.2337/ds19-0041

Source DB:  PubMed          Journal:  Diabetes Spectr        ISSN: 1040-9165


  26 in total

1.  Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

Authors:  Paul A Harris; Robert Taylor; Robert Thielke; Jonathon Payne; Nathaniel Gonzalez; Jose G Conde
Journal:  J Biomed Inform       Date:  2008-09-30       Impact factor: 6.317

2.  Building health behavior models to guide the development of just-in-time adaptive interventions: A pragmatic framework.

Authors:  Inbal Nahum-Shani; Eric B Hekler; Donna Spruijt-Metz
Journal:  Health Psychol       Date:  2015-12       Impact factor: 4.267

3.  Diabetes-specific emotional distress among adolescents: feasibility, reliability, and validity of the problem areas in diabetes-teen version.

Authors:  Jill Weissberg-Benchell; Jeanne Antisdel-Lomaglio
Journal:  Pediatr Diabetes       Date:  2011-03-28       Impact factor: 4.866

4.  Using mobile phones to measure adolescent diabetes adherence.

Authors:  Shelagh A Mulvaney; Russell L Rothman; Mary S Dietrich; Kenneth A Wallston; Elena Grove; Tom A Elasy; Kevin B Johnson
Journal:  Health Psychol       Date:  2011-10-03       Impact factor: 4.267

Review 5.  Anxiety in children and adolescents with type 1 diabetes.

Authors:  Shideh Majidi; Kimberly A Driscoll; Jennifer K Raymond
Journal:  Curr Diab Rep       Date:  2015-08       Impact factor: 4.810

6.  Predictors of acute complications in children with type 1 diabetes.

Authors:  Arleta Rewers; H Peter Chase; Todd Mackenzie; Philip Walravens; Mark Roback; Marian Rewers; Richard F Hamman; Georgeanna Klingensmith
Journal:  JAMA       Date:  2002-05-15       Impact factor: 56.272

7.  Modeling mood variation associated with smoking: an application of a heterogeneous mixed-effects model for analysis of ecological momentary assessment (EMA) data.

Authors:  Donald Hedeker; Robin J Mermelstein; Michael L Berbaum; Richard T Campbell
Journal:  Addiction       Date:  2009-02       Impact factor: 6.526

Review 8.  Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments.

Authors:  Kristin E Heron; Joshua M Smyth
Journal:  Br J Health Psychol       Date:  2009-07-28

9.  Compliance with blood glucose monitoring in children with type 1 diabetes mellitus.

Authors:  D P Wilson; R K Endres
Journal:  J Pediatr       Date:  1986-06       Impact factor: 4.406

10.  Type 1 Diabetes in Children and Adolescents: A Position Statement by the American Diabetes Association.

Authors:  Jane L Chiang; David M Maahs; Katharine C Garvey; Korey K Hood; Lori M Laffel; Stuart A Weinzimer; Joseph I Wolfsdorf; Desmond Schatz
Journal:  Diabetes Care       Date:  2018-08-09       Impact factor: 19.112

View more
  2 in total

1.  Ecological Momentary Assessment: A Systematic Review of Validity Research.

Authors:  Lesleigh Stinson; Yunchao Liu; Jesse Dallery
Journal:  Perspect Behav Sci       Date:  2022-05-06

2.  Technological Ecological Momentary Assessment Tools to Study Type 1 Diabetes in Youth: Viewpoint of Methodologies.

Authors:  Mary Katherine Ray; Alana McMichael; Maria Rivera-Santana; Jacob Noel; Tamara Hershey
Journal:  JMIR Diabetes       Date:  2021-06-03
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.