CoChat
Sign Up
Cited by 212Mobile Health and mHealth ApplicationsDiabetes Management and EducationHealth, Technology, Consumer Behavior

Mobile phone applications and their use in the self-management of Type 2 Diabetes Mellitus: a qualitative study among app users and non-app users

Open access
Jeffrey, Bronte, Bagala, Melina, Creighton, Ashley, Leavey, Tayla, Nicholls, Sarah, Wood, Crystal, Longman, Jo, Barker, Jane +1 more·2019·Diabetology & Metabolic Syndrome
DOI 10.1186/s13098-019-0480-4PMC PMC6794726
Open this paper in CoChat — free accountOpen PDF

Abstract

Background: Mobile phone applications (apps) have been shown to successfully facilitate the self-management of chronic disease. This study aims to evaluate firstly the experiences, barriers and facilitators to app usage among people with Type 2 Diabetes Mellitus (T2DM) and secondly determine recommendations to improve usage of diabetes apps. Methods: Participants were aged ≥ 18 years with a diagnosis of T2DM for ≥ 6 months. Semi-structured phone-interviews were conducted with 16 app and 14 non-app users. Interviews were based on the Technology Acceptance Model, Health Information Technology Acceptance Model (HITAM) and the Mobile Application Rating Scale. Data were analysed using deductive content analysis. Results: Most app-users found apps improved their T2DM self-management and health. The recommendation of apps by health professionals, as well as positive interactions with them, improved satisfaction; however, only a minority of patients had practitioners involved in their app use. All non-app users had never had the concept discussed with them by a health professional. Facilitators to app use included the visual representation of trends, intuitive navigation and convenience (for example, discretion and portability). Barriers to app use were participant’s lack of knowledge and awareness of apps as healthcare tools, perceptions of disease severity, technological and health literacy or practical limitations such as rural connectivity. Factors contributing to app use were classified into a framework based on the Health Belief Model and HITAM. Recommendations for future app design centred on educational features, which were currently lacking (e.g. diabetes complications, including organ damage and hypoglycaemic episodes), monitoring and tracking features (e.g. blood glucose level monitoring with trends and dynamic tips and comorbidities) and nutritional features (e.g. carbohydrate counters). Medication reminders were not used by participants. Lastly, participants felt that receiving weekly text-messaging relating to their self-management would be appropriate. Conclusions: The incorporation of user-centred features, which engage T2DM consumers in self-management tasks, can improve health outcomes. The findings may guide app developers and entrepreneurs in improving app design and usability. Given self-management is a significant factor in glycaemic control, these findings are significant for GPs, nurse practitioners and allied health professionals who may integrate apps into a holistic management plan which considers strategies outside the clinical environment.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s13098-019-0480-4

Cites

41 of 41 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. [18]Katz D, Dalton N, Price B. Failing the challenge: diabetes apps & long-term daily adoption. In: 8th International conference on advanced technologies & treatments for diabetes (ATTD 2015), Paris. 2015.
  2. [37]Boyle L Grainger R Hall RM Krebs JD Use of and beliefs about mobile phone apps for diabetes self-management: surveys of people in a hospital diabetes clinic and diabetes health professionals in New Zealand JMIR mHealth uHealth 2017 5 6 e85 28666975 PMC5511364

Cited by

Sorted by citations

Related

Authors
Jeffrey, B., Bagala, M., Creighton, A., Leavey, T., Nicholls, S., Wood, C., Longman, J., Barker, J., & Pit, S.
Affiliations
  • Western Sydney University (AU)
  • The University of Sydney (AU)
Corresponding author
Pit, Sabrina
ORCID
Venue
Diabetology & Metabolic Syndrome
Publisher
Springer Science and Business Media LLC
Published
2019-10-16
Volume
11
Issue
1
Pages
84-84
ISSN
1758-5996 (ISSN-L)
DOI
10.1186/s13098-019-0480-4
PMC
PMC6794726
OpenAlex
W2980451355
Cited by
212
References
41
Citations by year
2026: 16 · 2025: 31 · 2024: 29 · 2023: 52 · 2022: 42 · 2021: 31 · 2020: 11
FWCI
28.63
Type
journal article
Language
en
Open access
Licence
Locations
  • Diabetology & Metabolic Syndrome (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • Diabetes Australia
  • Southern Cross University
  • Western Sydney University
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
  • Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
Keywords
mobile health apps, type 2 diabetes, self-management, provider recommendation, technology acceptance, Health Belief Model, health literacy, text messaging, blood glucose monitoring

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review