CoChat
Sign Up
Cited by 43Mobile Health and mHealth ApplicationsHealth, Technology, Consumer BehaviorTechnology Adoption and User Behaviour

Barriers for User Acceptance of Mobile Health Applications for Diabetic Patients: Applying the UTAUT Model

Open access
Petersen, Fazlyn, Jacobs, Mariam, Pather, Shaun·2020·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-45002-1_6PMC PMC7134297
Open this paper in CoChat — free account

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 · 10.1007/978-3-030-45002-1_6

Cites

5 of 5 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

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Among patients with type 2 diabetes in South Africa, lower performance expectancy, lower effort expectancy, less social influence, and fewer facilitating conditions explained the generally lower intention to use telehealth services.

    “Similarly, among patients with type 2 diabetes in South Africa [16], lower performance expectancy, lower effort expectancy, less social influence, and fewer facilitating conditions explained the generally lower intention to use telehealth services.”

    From Applying the Unified Theory of Acceptance and Use of Technology to Identify Factors Associated With Intention to Use Teledelivered Supportive Care Among Recently Diagnosed Breast Cancer Survivors During COVID-19 in Hong Kong: Cross-Sectional Survey (2024) · cites this paper as [16]

    The Results (Themes Identified from the UTAUT Model; page unavailable) and Conclusion say that all four UTAUT constructs help explain barriers to accepting mobile health applications among diabetic patients in low-socioeconomic areas of the Western Cape. The claim overextends this finding to South African patients generally and to telehealth services; its description of less social influence is also imprecise, since respondents reported encouragement from family and friends but preferred their in-person support to an app. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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: 21

  1. [3]Berkowitz S Low socioeconomic status is associated with increased risk for hypoglycemia in diabetes patients: the Diabetes Study of Northern California (DISTANCE) J. Health Care Poor Underserv. 2014 25 2 478 490 PMC4034138 24858863
  2. [4]Ruddock JS Poindexter M Gary-Webb TL Walker EA Davis NJ Innovative strategies to improve diabetes outcomes in disadvantaged populations Diabet. Med. 2016 33 723 733 27194172
  3. [10]Statistics South Africa: National Poverty Lines 2018. Statistical release P0310, no. July, pp. 1–10 (2018)
  4. [13]Hou C Xu Q Diao S Hewitt J Li J Carter B Mobile phone applications and self-management of diabetes: a systematic review with meta-analysis, meta-regression of 21 randomized trials and GRADE Diabetes Obes. Metab. 2018 20 8 2009 2013 29582538
  5. [15]Deacon, A.J., Chee, J.J., Chang, W.J.R., Harbourne, B.A.: Mobile applications for diabetes mellitus self-management: a systematic narrative analysis. In: Successes and Failures in Telehealth Conference, SFT-2017, pp. 17–30 (2017)
  6. [17]Petersen, F., Pather, S., Tucker, W.D.: User acceptance of ICT for diabetes self-management in the Western Cape, South Africa. In: African Conference of Information Systems and Technology (ACIST), pp. 1–11 (2018)
  7. [18]Nelson LA Mulvaney SA Gebretsadik T Ho YX Johnson KB Osborn CY Disparities in the use of a mHealth medication adherence promotion intervention for low-income adults with type 2 diabetes J. Am. Med. Inform. Assoc. 2016 23 1 12 18 26186935 PMC5009937
  8. [19]Hoque R Sorwar G Understanding factors influencing the adoption of mHealth by the elderly: an extension of the UTAUT model Int. J. Med. Inform. 2017 101 75 84 28347450
  9. [21]Singh K Many mobile health apps target high-need, high-cost populations, but gaps remain Health Aff. 2016 35 12 2310 2318 27920321
  10. [23]Aranda-Jan CB Mohutsiwa-Dibe N Loukanova S Systematic review on what works, what does not work and why of implementation of mobile health (mHealth) projects in Africa BMC Public Health 2014 14 1 188 24555733 PMC3942265
  11. [24]Scheibe M Reichelt J Bellmann M Kirch W Acceptance factors of mobile apps for diabetes by patients aged 50 or older: a qualitative study Med. 2.0 2015 4 1 e1 25733033 PMC4376102
  12. [25]Zhang Y Factors influencing patients’ intentions to use diabetes management apps based on an extended unified theory of acceptance and use of technology model: web-based survey J. Med. Internet Res. 2019 21 8 e15023 31411146 PMC6711042
  13. [33]Coetzer, J.: Application of HCI design principles in overcoming information illiteracy: case of a m-health application for a rural community in South Africa. In: 2018 International Conference on Intelligent and Innovative Computing Applications, ICONIC 2018, pp. 1–7 (2018)
  14. [34]Huang Z Soljak M Boehm BO Car J Clinical relevance of smartphone apps for diabetes management: a global overview Diabetes Metab. Res. Rev. 2018 34 4 e2990 29431916
  15. [36]Saunders M Lewis P Thornhill A Understanding research philosophies and approaches Res. Methods Bus. Stud. 2009 2009 106 136
  16. [37]Marshall MN Sampling for qualitative research Family Pract. 1996 13 6 522 525 9023528
  17. [38]Cresswell JW Research Design: Qualitative, Quantitative, and Mixed Methods Approaches 2014 3 Thousand Oaks SAGE Publications
  18. [41]Ahmed T Lucas H Khan AS Islam R Bhuiya A Iqbal M eHealth and mHealth initiatives in Bangladesh: a scoping study BMC Health Serv. Res. 2014 14 1 260 24934164 PMC4072608
  19. [42]Akhlaq A McKinstry B Bin Muhammad K Sheikh A Barriers and facilitators to health information exchange in low- and middle-income country settings: a systematic review Health Policy Plan. 2016 31 9 1310 1325 27185528
  20. [44]Health Systems Trust: South African Health Review 2013/2014 (2014)
  21. [46]Rogers EM Diffusion of Innovations 2003 New York Free Press

Cited by

Sorted by citations

Authors
Petersen, F., Jacobs, M., & Pather, S.
Affiliations
University of the Western Cape (ZA)
Corresponding author
Petersen, Fazlyn
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2020
Volume
LNCS-12067
Issue
Part II
Pages
61-72
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-45002-1_6
PMC
PMC7134297
OpenAlex
W3015482928
Cited by
43
References
5
Citations by year
2025: 6 · 2024: 7 · 2023: 11 · 2022: 6 · 2021: 12 · 2020: 1
FWCI
43.46
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • PubMed Central (publishedVersion)
  • HAL (submittedVersion, cc-by)
  • UWC Research Repository (University of the Western Cape) (submittedVersion)
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
Keywords
mobile health apps, diabetes self-management, user acceptance, UTAUT model, type 2 diabetes, performance expectancy, effort expectancy, social influence, facilitating conditions, technology anxiety, resistance to change, trust in technology, health disparities, Western Cape

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