Diabetes, Diabetes Mellitus, Type 2, Diabetic Retinopathy
Conditions
Brief summary
We plan to explore the usability of Generative AI-Chatbot for Diabetic Patient
Detailed description
Diabetes is rapidly spreading, affecting a significant number of adults, with a staggering total of 537 million diabetic individuals. This condition gives rise to various complications that can lead to diabetic retinopathy, foot ulcers, cardiac problems, and kidney damage. However, many of these complications can be mitigated by providing patients with accurate information concerning their diet, stress management, and weight control. The recent advancements in Generative Artificial Intelligence-based chatbots have demonstrated their efficacy as intelligent assistants across various aspects of human life. In this study, we aim to assess the effectiveness of these Language Models in assisting patients. Our research plan entails the interaction between patients and chatbots like ChatGPT, both with and without human support, followed by evaluations of these interactions by specialists. Additionally, we will gather feedback from patients regarding their experiences and perceptions of the chatbot interactions.
Interventions
BEHAVIORALExploring AI-Chatbot
All participants will be provided access to AI-Chatbot and will be asked to enquire their daily life problems related to diabetes. They will also be asked to review the replies of the Chatbot after their interaction.
Sponsors
Pakistan Council of Scientific and Industrial Research
Study design
Observational model
CASE_ONLY
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to 100 Years
Healthy volunteers
No
Inclusion criteria
* Present physically in Pakistan * Adults (18 years or older) * Diabetic Patient
Exclusion criteria
* Adults unable to consent * Individuals who are not yet adults (infants, children, teenagers) * Prisoners
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Usability of the Chatbot for diabetic patient | One time | To assess the usability of the Chatbot, we will employ the mHealth App Usability Questionnaire (MAUQ) to gather feedback from patients following their interaction. Our study will utilize Table 4 of this questionnaire, which consists of three sections: ease of use, interface, and satisfaction and usefulness. Specifically, we will focus our evaluation on 10 out of the 18 questions presented in this table. The selected questions are S1, S2, S6, S7, S9, S11, S12, S13, S14, and S18. Patients will provide their responses on a scale of 1 to 5, where 1 indicates very poor and 5 denotes very good. |
| Internet Speed | One time | Minimum downloading speed of internet will be measured during the chat. This will be recorded in Mega bits per second. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Analyzing Chat response generated by AI Chatbot | One time | Likert scale will be used to evaluate the chat response of Chatbot. The following parameters will be evaluated by the specialists for each response namely Clear, Complete and Correct. Clear and Completeness will be evaluated on a range of 1-5, where 1 means poor quality and 5 means very good quality response. The correctness of each response will be further analyzed as Safe and latest. It will be evaluated in binary terms i.e. Yes or No. |
Countries
Pakistan
Contacts
Primary ContactGhulam Mustafa
Outcome results
None listed