NCT Number: NCT05564728
Diabetes education and self-management support can be delivered via mobile phones. This protocol aims to assess the feasibility and acceptability of Well Feet, a conversational agent, as a diabetic foot care companion. By utilizing feedback and responses to evaluative questions posted on the app's interface, the investigators intend to examine the app's technical, functional, and operational feasibility.
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Notify Me21 year and older
All sexes
Observational
Nanyang Technological University, Center for Population Health Sciences, Lee Kong Chian School of Medicine, Singapore
Diabetes puts patients with diabetes at risk of foot complications. Besides well managed diabetes, providing diabetes foot care education and self-management support is key to reducing the risk of developing diabetic foot ulcers, a serious and costly complication of diabetes. Although, education and self-management support for people with diabetes can improve patients' quality of life, they are still commonly not provided or inadequate. Digital technologies have the potential to offer a new convenient, interactive, and engaging mode of self-management education and support.
This study aims to examine the feasibility of the Well Feet app for diabetes foot care education and self-management support in promoting optimal foot care behaviour. In recognizing that many people with diabetes, especially the elderly, require the support of informal carers, the app also targets their knowledge and support needs.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
for patients:
Inclusion criteria
for carers:
Exclusion criteria
for patients and carers:
Well Feet is developed based on adaptive learning frameworks to deliver diabetes foot care education through a conversational agent. The learning path for each participant will be customised based on their responses to pre-module quizzes.
Other names: Well Feet
Time frame: end-of-trial (1 month)
A validated questionnaire, MHealth App Usability Questionnaire, will be used to determine the usefulness and applicability of an app with a conversational agent/chatbot among patients and carers. The questionnaire consists of 3 subscales, which are ease of use (5 items), interface and satisfaction (7 items), and usefulness (6 items). Participants rate each of the items using a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). The usability of the app is determined by the total and average of all statements-the higher the overall average, the better the usability of the app. However, if the average score is lower than 4, it means that the usability of the app is not good
Time frame: end-of-trial (1 month)
A focus group discussion among the app user will be done to collect feedback on the user experience at the end of the trial. Qualitative data on app usability, app applicability, app relevance and user feedback will be retrieved from the focus group discussion.
Time frame: baseline and end-of-trial (1 month)
A validated questionnaire, Foot Care Knowledge Questionnaire (FTC) will be used to evaluate changes in patient's and carer's knowledge on foot care from baseline to the end-of-trial. The questionnaire consists of 12 items to be rate true or false and the score will be presented in term of percentage of correct answer. Higher percentage of correct answers means higher level of foot care knowledge.
Time frame: baseline and end-of-trial (1 month)
A validated questionnaire, Nottingham Assessment of Functional Footcare (Revised 2015) will be used to assess changes in patient's level of foot care related self-management behaviour from baseline to the end-of-trial. This questionnaire consists of 29 items on a 5-point Likert scale. The total number of scores will be added up and multiply the score by 1.115 to obtain the final score. Higher score means better self-management care.
Time frame: baseline and end-of-trial (1 month)
A validated questionnaire, Brief Assessment Scale for Caregivers (BASC) will be used to determine changes in carer's quality of life and level of burden from caring for a person with diabetes. This questionnaire consists of 14 items clustered into 5 factors. The mean BASC score is computed by summing up the non-NA items in each factor, then dividing by the number of items that were not missing. This gave a score scaled from 0 to 3, with a higher score indicating better caregiver outcomes.
Time frame: end-of-trial (1 month)
Data on login frequency will be retrieved from the backend of the app at the end of trial.
Time frame: end-of-trial (1 month)
Data on total number of minutes spent on the app will be retrieved from the backend of the app at the end of trial.
Time frame: end-of-trial (1 month)
Data on total number of education module accessed will be retrieved from the backend of the app at the end of trial.
Time frame: end-of-trial (1 month)
Data on number of education module completed will be retrieved from the backend of the app at the end of trial.
Time frame: end-of-trial (1 month)
At the end of each module, users will be directed to a module quiz. Data on total scores for the module quizzes will be collected from the backend of the app at the end of trial.
Time frame: end-of-trial (1 month)
At the end of each module, users will be prompted to rate their learning experience using a 5-point Likert scale. Data of the ratings on each of the modules will be collected from the backend of the app at the end of trial.
Time frame: end-of-trial (1 month)
Upon completion of all learning modules, users will be prompted to rate their overall experience of using the app using a 5-point Likert scale. Data of the overall experience of app rating will be retrieved from the backend of the app at the end of trial.
Time frame: end-of-trial (1 month)
A validated questionnaire, Chatbot Usability Questionnaire, will be used to determine the usefulness and applicability of a conversational agent/chatbot among patients and carers at the end of the trial. This questionnaire consists of 16 items on a 5-point Likert scale. The mean score will be calculated using CUQ calculator available on the Ulster University website. This higher mean score means better chatbot usability.
Nanyang Technological University
Other
A Feasibility Study of a Conversational Agent App for Empowering Foot Care Literacy Among People With Diabetes and Their Carers
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