Glucose Control, Disease Burden, and Educational Gaps in People With Type 1 Diabetes: Exploratory Study of an Integrated Mobile Diabetes App
Open accessAbstract
Background: Self-monitoring and self-management, crucial for optimal glucose control in type 1 diabetes, requires many disease-related decisions per day and imposes a substantial disease burden on people with diabetes. Innovative technologies that integrate relevant measurements may offer solutions that support self-management, decrease disease burden, and benefit diabetes control. Objective: The objective of our study was to evaluate a prototype integrated mobile phone diabetes app in people with type 1 diabetes. Methods: In this exploratory study, we developed an app that contained cloud-stored log functions for glucose, carbohydrates (including a library), insulin, planned exercise, and mood, combined with a bolus calculator and communication functions. Adults with diabetes tested the app for 6 weeks. We assessed the feasibility of app use, user experiences, perceived disease burden (through questionnaires), insulin dose and basal to bolus ratio, mean glucose level, hemoglobin A1c, and number of hypoglycemic events. Results: A total of 19 participants completed the study, resulting in 5782 data entries. The most frequently used feature was logging blood glucose, insulin, and carbohydrates. Mean diabetes-related emotional problems (measured with the Problem Areas in Diabetes scale) scores decreased from 14.4 (SD 10.0) to 12.2 (SD 10.3; P=.04), and glucose control improved, with hemoglobin A1c decreasing from 7.9% (mean 62.3, SD 8 mmol/mol) to 7.6% (mean 59.8, SD 7 mmol/mol; P=.047). The incidence of hypoglycemic events did not change. Participants were generally positive about the app, rating it as “refreshing,” and as providing structure by reinforcing insulin-dosing principles. The app revealed substantial knowledge gaps. Logged data enabled additional detailed analyses. Conclusions: An integrated mobile diabetes app has the potential to improve diabetes self-management and provide tailored educational support, which may decrease disease burden and benefit diabetes control.
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Of the 27 papers, 7 focused on T1DM, with 308 patients in the intervention groups and 129 in the control groups.
“Of the 27 papers, 7 were focused on T1DM (308 patients in the intervention groups and 129 patients in the control groups) [43-49]”
From Effectiveness of Disease-Specific mHealth Apps in Patients With Diabetes Mellitus: Scoping Review (2021) · cites this paper as [46]
Tack et al did not find any significant differences in hypoglycemic events (P=.21).
“In addition, Tack et al [46] did not find any significant differences in hypoglycemic events (P=.21).”
From Effectiveness of Disease-Specific mHealth Apps in Patients With Diabetes Mellitus: Scoping Review (2021) · cites this paper as [46]
The average HbA1c in the T1DM studies with significant within-group improvements was 7.73%.
“In 3 of the 7 studies, significant improvements of HbA1c levels within the intervention groups were found (mean difference: –1.1%, P<.001 [47]; –0.3%, P<.001 [45]; and –0.3%, P=.04 [46]), yielding an HbA1c of 7.73% on average.”
From Effectiveness of Disease-Specific mHealth Apps in Patients With Diabetes Mellitus: Scoping Review (2021) · cites this paper as [46]
In 3 of the 7 T1DM studies, significant improvements of HbA1c levels within the intervention groups were found.
“In 3 of the 7 studies, significant improvements of HbA1c levels within the intervention groups were found (mean difference: –1.1%, P<.001 [47]; –0.3%, P<.001 [45]; and –0.3%, P=.04 [46]), yielding an HbA1c of 7.73% on average.”
From Effectiveness of Disease-Specific mHealth Apps in Patients With Diabetes Mellitus: Scoping Review (2021) · cites this paper as [46]
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Effectiveness of Disease-Specific mHealth Apps in Patients With Diabetes Mellitus: Scoping Review
Eberle, C., Löhnert, M., & Stichling, S. (2021) · JMIR mHealth and uHealth · doi:10.2196/23477 · Cited by 160 · see citing works
“In addition, Tack et al [46] did not find any significant differences in hypoglycemic events ( P= .21).”
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- Authors
- Tack, C. J., Lancee, G. J., Heeren, B., Engelen, L. J., Hendriks, S., Zimmerman, L., De Massari, D., van Gelder, M. M., & van de Belt, T. H.
- Affiliations
- Radboud University Medical Center (NL)
- ORCID
- Venue
- JMIR Diabetes
- Publisher
- JMIR Publications Inc.
- Published
- 2018-11-23
- Volume
- 3
- Issue
- 4
- Pages
- e17
- ISSN
- 2371-4379 (ISSN-L)
- PMC
- PMC6286423
- OpenAlex
- W2900536151
- Cited by
- 18
- References
- 23
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 3 · 2022: 6 · 2021: 2 · 2020: 2 · 2019: 1
- FWCI
- 0.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Diabetes (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion, cc-by)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Funders
- Radboud Universitair Medisch Centrum
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- type 1 diabetes, diabetes self-management, glycemic control, disease burden, hypoglycemia, HbA1c, insulin dose, bolus calculator, diabetes education, user experience, Problem Areas in Diabetes scale
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