Evidence map›Paper›PMID 35537057›Full record

ArticleJMIR diabetes2022

Evaluating the Implementation of the GREAT4Diabetes WhatsApp Chatbot to Educate People With Type 2 Diabetes During the COVID-19 Pandemic: Convergent Mixed Methods Study.

Robert Mash, Darcelle Schouw, Alex Emilio Fischer

Abstract read
In one paragraph

Article in JMIR diabetes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Review
  7. Article
  8. Article
  9. Evaluating the implementation of group empowerment for people with type 2 diabetes at Grassy Park Community Day Centre, Cape Town: A convergent mixed-methods study.South African family practice : official journal of the South African Academy of Family Practice/Primary Care · 2025
    Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. [ChatGPT: curse or blessing in nursing care?]Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2023
    Review
  17. Health Chatbots in Africa: Scoping Review.Journal of medical Internet research · 2023
    Article
  18. Factors influencing insulin initiation in primary care facilities in Cape Town, South Africa.South African family practice : official journal of the South African Academy of Family Practice/Primary Care · 2023
    Article
  19. Comprehensive patient education and counselling for non-communicable diseases in primary care, Western Cape.South African family practice : official journal of the South African Academy of Family Practice/Primary Care · 2023
    Article
  20. Piloting of virtual group education for diabetes in Cape Town: An exploratory qualitative study.South African family practice : official journal of the South African Academy of Family Practice/Primary Care · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Robert MashFaculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.ORCID https://orcid.org/0000-0001-7373-0774
Darcelle SchouwFaculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.ORCID https://orcid.org/0000-0002-0713-8956
Alex Emilio FischerAviro Health, Cape Town, South Africa.ORCID https://orcid.org/0000-0002-6882-7245

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn South Africa, diabetes is a leading cause of morbidity and mortality, which was exacerbated during the COVID-19 pandemic. Most education and counseling activities were stopped during the lockdown, and the GREAT4Diabetes WhatsApp Chatbot was innovated to fill this gap.

objectiveThis study aimed to evaluate the implementation of the chatbot in Cape Town, South Africa, between May and October 2021.

methodsConvergent mixed methods were used to evaluate the implementation outcomes: acceptability, adoption, appropriateness, feasibility, fidelity, cost, coverage, effects, and sustainability. Quantitative data were derived from the chatbot and analyzed using the SPSS. Qualitative data were collected from key informants and analyzed using the framework method assisted by Atlas-ti. The chatbot provided users with 16 voice messages and graphics in English, Afrikaans, or Xhosa. Messages focused on COVID-19 infection and self-management of type 2 diabetes.

resultsThe chatbot was adopted by the Metro Health Services to assist people with diabetes who had restricted health care during the lockdown and were at a higher risk of hospitalization and death from COVID-19 infection. The chatbot was disseminated via health care workers in primary care facilities and local nonprofit organizations and via local media and television. Two technical glitches interrupted the dissemination but did not substantially affect user behavior. Minor changes were made to the chatbot to improve its utility. Many patients had access to smartphones and were able to use the chatbot via WhatsApp. Overall, 8158 people connected with the chatbot and 4577 (56.1%) proceeded to listen to the messages, with 12.56% (575/4577) of them listening to all 16 messages, mostly within 32 days. The incremental setup costs were ZAR 255,000 (US $16,876) and operational costs over 6 months were ZAR 462,473 (US $30,607). More than 90% of the users who listened to each message found them useful. Of the 533 who completed the whole program, 351 (71.1%) said they changed their self-management a lot and 87.6% (369/421) were more confident. Most users changed their lifestyles in terms of diet (315/414, 76.1%) and physical activity (222/414, 53.6%). Health care workers also saw benefits to patients and recommended that the service continues. Sustainability of the chatbot will depend on the future policy of the provincial Department of Health toward mobile health and the willingness to contract with Aviro Health. There is the potential to go to scale and include other languages and chronic conditions.

conclusionsThe chatbot shows great potential to complement traditional health care approaches for people with diabetes and assist with more comprehensive patient education. Further research is needed to fully explore the patient's experience of the chatbot and evaluate its effectiveness in our context.

Indexed as

COVID-19diabeteseHealthmHealthmobile healthmobile phonepatient education and counselingprimary careSouth Africatelemedicine

Identifiers

PMID35537057
PMCPMC9236126

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.