Diabetes, GestationalNCT07458997
Usability Evaluation of Gen AI-based Nutrition Chatbot for Pregnant Women
A study testing a culturally tailored nutrition AI chatbot for pregnant women in people with Diabetes, Gestational and Pre-eclampsia, run by Hong Kong Metropolitan University at 1 site in Hong Kong.
Trial summary
Background: Pregnancy imposes significant physical demands, with complications like gestational diabetes (GDM) and pre-eclampsia posing serious risks. Nutrition is crucial for mitigation, but accessing reliable guidance remains challenging. Read more about this study
- Who is it for?
- Ages 18 years and older, women only
- Conditions the study is for
- Diabetes, Gestational, Pre-eclampsia, AI Chatbot for Prenatal Nutrition Guidance
- What is being tested?
- a culturally tailored nutrition AI chatbot for pregnant women
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 100 participants
- Where
- 1 siteHong Kong
- Study dates
- 1 Dec 2026to 31 Aug 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Women only
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Pregnant women aged 18 years or older
- Able to provide informed consent in the study language
- Own a smartphone with internet access and the WeChat application
Who cannot join
- Current enrollment in other nutrition intervention studies
- Severe mental health conditions that may impair technology use or ability to provide informed consent
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Hong Kong1 site
Hong Kong Metropolitan University
Hong Kong
Bronya Luk, DHSc, +852 39708758, bluk@hkmu.edu.hk
About this study
Official title: Usability Evaluation of Gen AI-based Nutrition Chatbot for Pregnant Women: A Pilot Quasi-experimental Study
Background: Pregnancy imposes significant physical demands, with complications like gestational diabetes (GDM) and pre-eclampsia posing serious risks. Nutrition is crucial for mitigation, but accessing reliable guidance remains challenging. This study evaluates the feasibility of an AI chatbot providing nutritional guidance for managing these conditions.
Methods: In a quasi-experimental design, 100 pregnant women will self-select into either the intervention group (n=50, using an AI chatbot) or control group (n=50, receiving standard care). The primary outcome is usability measured by the System Usability Scale (SUS) at 12 weeks, with an expected mean difference of ≥13 points. Secondary outcomes include technology acceptance (Technology Acceptance Model), user engagement, information accuracy, and changes in dietary knowledge/behaviors. Quantitative data will be analyzed using intention-to-treat and t-tests. Semi-structured interviews with 20 participants will explore user experiences through thematic analysis.
Read more
Expected Results: The AI chatbot is anticipated to demonstrate superior usability and high user acceptance (TAM >5.0/7), with improvements in dietary knowledge and behavior. Qualitative findings will provide insights into benefits, barriers, and engagement factors.
Conclusion: This study will establish an evidence base on AI chatbot feasibility and acceptance for prenatal nutrition, informing tool optimization and future large-scale trials.
Read the detailed description
Objectives: This study primarily aims to evaluate the usability of a nutrition AI chatbot for pregnant women by comparing System Usability Scale (SUS) scores between intervention and control groups. Secondary objectives include assessing technology acceptance (Technology Acceptance Model), engagement patterns, information quality (accuracy, comprehensibility, consistency), and changes in nutritional knowledge.
Design: A quasi-experimental design with two parallel groups (n=50 each) will be employed. Using self-selection, participants will choose to enroll in the intervention group (access to an AI chatbot plus routine care) or the control group (access to a standardized WeChat information service plus routine care). Routine care for all participants includes standard prenatal clinic visits and printed nutritional materials.
The WeChat service for the control group will be operated by a trained research assistant using a pre-defined script during two scheduled windows daily, providing information quoted from the official nutritional leaflets. This isolates the mode of information delivery (AI versus human-facilitated messaging) as the primary variable.
Participants: Inclusion criteria: pregnant women aged ≥18 years, able to consent, owning a smartphone with internet access. Exclusion criteria: enrollment in other nutrition interventions or severe mental health conditions impairing technology use. A purposive subsample of 20 participants (10 per group) will complete qualitative interviews.
Sample Size: Based on an expected mean SUS score of 78 (SD=12) in the intervention group and 65 (SD=15) in the control group (Cohen's d=0.95), 23 participants per group are required for 90% power at alpha=0.05. Accounting for 50% attrition, 50 participants per group will be recruited. Propensity score matching will be applied to reduce selection bias using variables including age, gestational age, parity, education, and baseline technology use.
Measurements: The primary outcome, usability, will be measured using the System Usability Scale (SUS) and the Chatbot Usability Questionnaire (CUQ) at 12 weeks. Technology acceptance will be assessed using the Technology Acceptance Model (TAM). Nutritional knowledge will be evaluated at baseline and 12 weeks using a 15-item questionnaire and the FIGO Nutrition Checklist. Information accuracy and consistency will be assessed by an expert panel rating 150 chatbot responses and repeated submission of 20 test questions. Engagement will be analyzed via application usage logs measuring adherence, intensity, and persistence. Semi-structured interviews will explore user experiences in depth.
Data Analysis: Quantitative data will be analyzed using intention-to-treat principles. Primary analysis will compare mean SUS scores between groups using independent samples t-tests, with effect sizes calculated as Cohen's d. Secondary outcomes will be analyzed using similar approaches, with chi-square tests for proportions and linear mixed models for nutritional knowledge change scores. Missing data will be addressed through multiple imputation. Qualitative interview transcripts will be analyzed using thematic analysis with dual independent coding.
Study Timeline: Participants will be enrolled over a 6-month period, with each participant completing a 12-week intervention and follow-up period.
- Being tested
- a culturally tailored nutrition AI chatbot for pregnant women
- Keywords
- AI chatbot, prenatal nutrition, gestational diabetes, pre-eclampsia, feasibility, usability, technology acceptance, mixed methods
- Medical subject headings
- Diabetes, Gestational, Pre-Eclampsia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
a culturally tailored nutrition AI chatbot for pregnant womenBehavioural
A culturally tailored nutrition AI chatbot for pregnant women , and the AI chatbot support will be available 24/7
| Study group | Role | What they receive |
|---|---|---|
| The intervention group | Experimental | The intervention group will access the the nutrition AI chatbot. Receives: Behavioral: a culturally tailored nutrition AI chatbot for pregnant women |
| The control group | No intervention | The control group will receive routine care along with access to a standardized WeChat information service. To ensure a fair comparison, the WeChat service for the control group will be operated by a trained research assistant using a pre-defined script and protocol. The assistant will respond to enquiries during two pre-scheduled windows per day (e.g., 10:00-12:00 and 14:00-16:00) by providing information directly quoted or paraphrased from the official nutritional leaflets. |
What is being measured
The primary outcome is the main question the study is designed to answer.
System Usability Scale (SUS)
Usability will be assessed using the System Usability Scale (SUS), a 10-item questionnaire with five-point Likert responses. SUS yields a total score ranging from 0 to 100, with higher scores indicating better perceived usability. Scores will be compared between groups at 12 weeks.
Measured: 12weeks
Secondary outcomes (5)
Mean Score on the Technology Acceptance Model (TAM) Questionnaire
Technology acceptance will be assessed using the Technology Acceptance Model (TAM) questionnaire. This 12-item instrument measures two domains: perceived usefulness (6 items) and perceived ease of use (6 items). Each item is rated on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). Domain scores are calculated as the mean of items within each domain, with higher scores indicating greater perceived usefulness or ease of use.
Measured: 12 weeks
Proportion of Participants Achieving Adequate Engagement Adherence
Engagement adherence will be measured using application usage logs. Adequate adherence is defined as using the platform at least 3 days per week for at least 10 out of the 12-week intervention period. The proportion of participants meeting this threshold will be reported.
Measured: 12 weeks
Mean Number of Platform Logins per Week
Engagement intensity will be measured using application usage logs. The average number of logins per week over the 12-week intervention period will be calculated for each participant and reported as a group mean.
Measured: 12 weeks
Mean Number of Queries Submitted per Participant
Engagement intensity will also be assessed by the total number of queries (questions or requests) submitted by each participant to the platform over the 12-week intervention period, reported as a group mean.
Measured: 12 weeks
Proportion of Chatbot Responses Rated as Accurate by Clinical Expert Panel
A panel of clinical experts will rate a sample of 150 chatbot responses for accuracy. Responses will be rated as accurate or inaccurate based on alignment with current clinical guidelines. The proportion of responses rated as accurate will be reported.
Measured: 12 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Mar 2026
- Study start1 Dec 2026 (estimated)
- Primary completion31 Aug 2027 (estimated)
- Study completion31 Aug 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Diabetes, GestationalStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Supportive care
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Model description
- A quasi-experimental design will be employed with two groups of 50 pregnant women. The intervention group will access the AI chatbot in addition to routine care, while the control group will receive routine care along with access to a standardized WeChat information service.
- Masking
- None (open label)
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Hong Kong Metropolitan University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Dr Bronya LUK Hi Kwan, Hong Kong Metropolitan University
- Investigators
- Bronya Luk, DHSc, Principal investigator, School of Nursing and Health Sciences, Hong Kong Metropolitan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07458997
- Sponsor's protocol ID
- HE-RD/2025/2.34
- Other IDs
- HE-RD/2025/2.34
- Status last verified
- Oct 2026
- First submitted
- 24 Feb 2026
- Last updated
- 23 Oct 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The informed consent form signed by participants did not include provisions for the public sharing of individual-level data. Furthermore, the ethical approval granted by the \[Name of Your Ethics Committee/IRB\] imposes restrictions on the dissemination of data to protect participant confidentiality. Therefore, only aggregated and anonymized results will be published.
Study at a glance
From the registry record, last updated by the sponsor on 23 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Usability Evaluation of Gen AI-based Nutrition Chatbot for Pregnant Women: A Pilot Quasi-experimental Study
- ClinicalTrials.gov ID
- NCT07458997
- Acronym / sponsor ID
- HE-RD/2025/2.34
- Keywords
- AI chatbot, prenatal nutrition, gestational diabetes, pre-eclampsia, feasibility, usability, technology acceptance, mixed methods
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Hong Kong Metropolitan UniversityUniversity, hospital or other
- Study start
- 1 Dec 2026 (estimated)
- Primary completion
- 31 Aug 2027 (estimated)
- Study completion
- 31 Aug 2027 (estimated)
- First posted
- 9 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Background: Pregnancy imposes significant physical demands, with complications like gestational diabetes (GDM) and pre-eclampsia posing serious risks. Nutrition is crucial for mitigation, but accessing reliable guidance remains challenging. This study evaluates the feasibility of an AI chatbot providing nutritional guidance for managing these conditions.
Methods: In a quasi-experimental design, 100 pregnant women will self-select into either the intervention group (n=50, using an AI chatbot) or control group (n=50, receiving standard care). The primary outcome is usability measured by the System Usability Scale (SUS) at 12 weeks, with an expected mean difference of ≥13 points. Secondary outcomes include technology acceptance (Technology Acceptance Model), user engagement, information accuracy, and changes in dietary knowledge/behaviors. Quantitative data will be analyzed using intention-to-treat and t-tests. Semi-structured interviews with 20 participants will explore user experiences through thematic analysis.
Expected Results: The AI chatbot is anticipated to demonstrate superior usability and high user acceptance (TAM >5.0/7), with improvements in dietary knowledge and behavior. Qualitative findings will provide insights into benefits, barriers, and engagement factors.
Conclusion: This study will establish an evidence base on AI chatbot feasibility and acceptance for prenatal nutrition, informing tool optimization and future large-scale trials.
Detailed description
Objectives: This study primarily aims to evaluate the usability of a nutrition AI chatbot for pregnant women by comparing System Usability Scale (SUS) scores between intervention and control groups. Secondary objectives include assessing technology acceptance (Technology Acceptance Model), engagement patterns, information quality (accuracy, comprehensibility, consistency), and changes in nutritional knowledge.
Design: A quasi-experimental design with two parallel groups (n=50 each) will be employed. Using self-selection, participants will choose to enroll in the intervention group (access to an AI chatbot plus routine care) or the control group (access to a standardized WeChat information service plus routine care). Routine care for all participants includes standard prenatal clinic visits and printed nutritional materials.
The WeChat service for the control group will be operated by a trained research assistant using a pre-defined script during two scheduled windows daily, providing information quoted from the official nutritional leaflets. This isolates the mode of information delivery (AI versus human-facilitated messaging) as the primary variable.
Participants: Inclusion criteria: pregnant women aged ≥18 years, able to consent, owning a smartphone with internet access. Exclusion criteria: enrollment in other nutrition interventions or severe mental health conditions impairing technology use. A purposive subsample of 20 participants (10 per group) will complete qualitative interviews.
Sample Size: Based on an expected mean SUS score of 78 (SD=12) in the intervention group and 65 (SD=15) in the control group (Cohen's d=0.95), 23 participants per group are required for 90% power at alpha=0.05. Accounting for 50% attrition, 50 participants per group will be recruited. Propensity score matching will be applied to reduce selection bias using variables including age, gestational age, parity, education, and baseline technology use.
Measurements: The primary outcome, usability, will be measured using the System Usability Scale (SUS) and the Chatbot Usability Questionnaire (CUQ) at 12 weeks. Technology acceptance will be assessed using the Technology Acceptance Model (TAM). Nutritional knowledge will be evaluated at baseline and 12 weeks using a 15-item questionnaire and the FIGO Nutrition Checklist. Information accuracy and consistency will be assessed by an expert panel rating 150 chatbot responses and repeated submission of 20 test questions. Engagement will be analyzed via application usage logs measuring adherence, intensity, and persistence. Semi-structured interviews will explore user experiences in depth.
Data Analysis: Quantitative data will be analyzed using intention-to-treat principles. Primary analysis will compare mean SUS scores between groups using independent samples t-tests, with effect sizes calculated as Cohen's d. Secondary outcomes will be analyzed using similar approaches, with chi-square tests for proportions and linear mixed models for nutritional knowledge change scores. Missing data will be addressed through multiple imputation. Qualitative interview transcripts will be analyzed using thematic analysis with dual independent coding.
Study Timeline: Participants will be enrolled over a 6-month period, with each participant completing a 12-week intervention and follow-up period.
Study design
- Primary purpose
- Supportive care
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- A quasi-experimental design will be employed with two groups of 50 pregnant women. The intervention group will access the AI chatbot in addition to routine care, while the control group will receive routine care along with access to a standardized WeChat information service.
| Arm | Type | Receives |
|---|---|---|
| The intervention group | Experimental | Behavioral: a culturally tailored nutrition AI chatbot for pregnant womenThe intervention group will access the the nutrition AI chatbot. |
| The control group | No intervention | –The control group will receive routine care along with access to a standardized WeChat information service. To ensure a fair comparison, the WeChat service for the control group will be operated by a trained research assistant using a pre-defined script and protocol. The assistant will respond to enquiries during two pre-scheduled windows per day (e.g., 10:00-12:00 and 14:00-16:00) by providing information directly quoted or paraphrased from the official nutritional leaflets. |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Pregnant women aged 18 years or older
- Able to provide informed consent in the study language
- Own a smartphone with internet access and the WeChat application
Key exclusion criteria (2 listed)
- Current enrollment in other nutrition intervention studies
- Severe mental health conditions that may impair technology use or ability to provide informed consent
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| a culturally tailored nutrition AI chatbot for pregnant womenA culturally tailored nutrition AI chatbot for pregnant women , and the AI chatbot support will be available 24/7 | Behavioural | The intervention group |
Primary outcome
System Usability Scale (SUS)
Time frame: 12weeks
Usability will be assessed using the System Usability Scale (SUS), a 10-item questionnaire with five-point Likert responses. SUS yields a total score ranging from 0 to 100, with higher scores indicating better perceived usability. Scores will be compared between groups at 12 weeks.
5 secondary outcomes
Mean Score on the Technology Acceptance Model (TAM) Questionnaire
Time frame: 12 weeks
Proportion of Participants Achieving Adequate Engagement Adherence
Time frame: 12 weeks
Mean Number of Platform Logins per Week
Time frame: 12 weeks
Mean Number of Queries Submitted per Participant
Time frame: 12 weeks
Proportion of Chatbot Responses Rated as Accurate by Clinical Expert Panel
Time frame: 12 weeks
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Oct 2026.
- Caropreso, L., de Azevedo Cardoso, T., Eltayebani, M., \& Frey, B. N. (2020). Preeclampsia as a risk factor for postpartum depression and psychosis: a systematic review and meta-analysis. Archives of Women's Mental Health, 23(4), 493-505.Link provided by the sponsor.
- Charlton, M. (2016). The evolving management of gestational diabetes. The Hong Kong Practitioner, 38(2).Link provided by the sponsor.
- Feghali, M. N., Abebe, K. Z., Comer, D. M., Caritis, S., Catov, J. M., \& Scifres, C. M. (2018). Pregnancy outcomes in women with an early diagnosis of gestational diabetes mellitus. Diabetes Research and Clinical Practice, 138, 177-186.Link provided by the sponsor.
- Gao, C., Sun, X., Lu, L., Liu, F., \& Yuan, J. (2019). Prevalence of gestational diabetes mellitus in mainland China: A systematic review and meta-analysis. Journal of Diabetes Investigation, 10(1), 154-162.Link provided by the sponsor.
- Hyzy, M., Bond, R., Mulvenna, M., Bai, L., Dix, A., Leigh, S., \& Hunt, S. (2022). System Usability Scale Benchmarking for Digital Health Apps: Meta-analysis. JMIR MHealth and UHealth, 10(8).Link provided by the sponsor.
- Individual participant dataSharing planned: No. The informed consent form signed by participants did not include provisions for the public sharing of individual-level data. Furthermore, the ethical approval granted by the [Name of Your Ethics Committee/IRB] imposes restrictions on the dissemination of data to protect participant confidentiality. Therefore, only aggregated and anonymized results will be published.
Related trials for Diabetes, Gestational
Recruiting
Fueling Labor: Protein Supplementation for Intrapartum Glucose Control
Diabetes During Pregnancy
Not yet recruiting
A Randomized Trial to Reduce Blood Pressure at One Year Postpartum After Preeclampsia
Adverse Pregnancy Outcomes, Cardiovascular Diseases
Not yet recruiting
The WAIT Trial: Waiting Versus Active Induction at Term in Women With A1 Gestational Diabetes Mellitus
Gestational Diabetes Mellitus (GDM)
Recruiting
Effects Of Core Canister Training in Pregnant Women With History of Gestational Diabetes Mellitus
Antenatal Care Model, Gestatiaonl Diabetes Mellitus
Recruiting
RIGHTEST iFree 2 CGM in Pregnant Women With Diabetes Study
Diabetes Mellitus, Type 1 Diabetes, Diabetes Mellitus, Type 2
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Bronya Luk, DHSc principal investigator
School of Nursing and Health Sciences, Hong Kong Metropolitan University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing a culturally tailored nutrition AI chatbot for pregnant women in people with Diabetes, Gestational and Pre-eclampsia, run by Hong Kong Metropolitan University at 1 site in Hong Kong.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 23 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Hong Kong. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Bronya Luk, DHSc (+852 39708758 ext. +852 39708758, bluk@hkmu.edu.hk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
System Usability Scale (SUS) (time frame: 12weeks). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 Oct 2026.