Skip to content

cMIND AI Web Tool Usability Study (Hong Kong)

Development and Validation of a Web-Based AI System for Assessing the Cantonese-Style Mediterranean Diet (cMIND) Index

Status
Not yet recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT07735013
Enrollment
20
Registered
2026-07-29
Start date
2027-09-01
Completion date
2028-02-01
Last updated
2026-07-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Healthy Aging, Mild Cognitive Impairment (MCI), Nutrition

Keywords

cMIND diet, Cantonese Mediterranean diet, AI dietary assessment, web-based AI tool, meal photo analysis, Cantonese mixed dishes, usability testing, older adults, congitive health, mild cognitive impairment, healthy ageing, gerontechnology, nutrition monitoring, dietary adherence, AI food recognition, Cantonese cuisine, brain health diet, feasibility study, acceptability study, Hong Kong older adults

Brief summary

This study is testing a new web-based tool that uses artificial intelligence (AI) to help older adults in Hong Kong check how healthy their Cantonese-style meals are for brain health. The tool is based on the cMIND diet, a Chinese-adapted version of a known healthy eating pattern that may support memory and thinking skills. Participants will use the web app to take photos of their usual meals for at least 10 days over two weeks. The AI will automatically identify ingredients and give a score showing how well the meal follows the cMIND diet. The study will also ask participants to complete a short questionnaire and a brief interview to find out how easy and useful the tool is for older adults. The purpose of this small study is to see whether the AI tool is user-friendly and acceptable for older people. Results will help improve the tool for future use to support healthy ageing and brain health.

Detailed description

Mild cognitive impairment (MCI) is common among older adults and can progress to dementia. Diet plays an important role in brain health. The cMIND diet is a culturally adapted Chinese version of the Mediterranean-DASH diet, designed to support cognitive function. However, many older adults find it difficult to track their adherence to this diet using traditional methods. This study is developing and testing a simple web-based AI tool to help older adults in Hong Kong monitor their Cantonese-style meals. Users take photos of their meals (such as dim sum, stir-fries, or congee) using the web app. The AI automatically identifies ingredients in mixed dishes and calculates a cMIND adherence score (0-12), giving immediate personalised feedback on how well the meal supports brain health. The main part of the study is a small usability and acceptability test. We will recruit 20 community-dwelling older adults aged 60 years and above who regularly eat Cantonese-style meals. Participants should not have a self-reported diagnosis of dementia or Alzheimer's disease, or other psychiatric/medical conditions that would interfere with participation or valid outcome assessment. Eligible participants will receive a 20-minute training session on how to use the web tool. They will then use the app to photograph their usual meals for at least 10 days over a two-week period, without changing their normal eating habits. At the end of the two weeks, participants will complete a short online questionnaire about the ease of use and usefulness of the tool. They will also take part in one short individual interview (about 20 minutes, audio-recorded) to share their experiences and suggestions. This low-risk study aims to understand whether older adults find the AI tool easy and acceptable to use in daily life. The results will help improve the prototype for future larger studies. All data will be kept strictly confidential, and ethics approval has been obtained from the Hong Kong Metropolitan University Research Ethics Committee (Reference: HE-FRSE/2026/08).

Interventions

OTHERWeb-Based AI Dietary Assessment System

A web-based AI software prototype designed specifically for older adults in Hong Kong. Users upload photographs of their usual Cantonese-style mixed meals (e.g., dim sum assortments, stir-fries with overlapping ingredients, or congee with toppings). The AI system automatically recognises multiple ingredients and sauces, estimates nutritional content using local food composition data, and calculates a cMIND adherence score (range 0-12). Immediate personalised feedback on dietary quality for brain health is provided. The tool is intended for dietary self-monitoring and does not involve any drug, physical device, or medical treatment.

Sponsors

Hong Kong Metropolitan University
Lead SponsorOTHER

Study design

Observational model
CASE_ONLY
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
60 Years to No maximum
Healthy volunteers
Yes

Inclusion criteria

* Aged 60 years or above * Community-dwelling in Hong Kong * Able to provide informed consent * Basic ability to use a smartphone or tablet (with assistance if needed) * Consuming Cantonese-style meals as the primary dietary pattern for ≥ 5 days per week for the past 3 months or longer)

Exclusion criteria

* Severe visual or motor impairment that prevents taking meal photos even with assistance * Self-reported diagnosis of dementia or Alzheimer's disease, or other psychiatric/medical conditions that would interfere with participation or valid outcome assessment * Current participation in other interventional nutrition or technology studies

Design outcomes

Primary

MeasureTime frameDescription
Usability and Acceptability of the Web-Based AI ToolAssessed at the end of the 2-week testing periodParticipants' perceived ease of use and acceptability of the AI web tool for photographing Cantonese meals and receiving cMIND dietary feedback.

Secondary

MeasureTime frameDescription
Feasibility of Meal Photo-TakingOver the 2-week testing periodProportion of participants able to complete at least 10 days of meal photos over the two-week period.
Qualitative User FeedbackAt the end of the 2-week testing periodParticipants' experiences, challenges, and suggestions regarding the web tool, collected through semi-structured interviews.

Contacts

CONTACTChow Ka-Man, Ph.D.
akmchow@hkmu.edu.hk852+ 39708721
PRINCIPAL_INVESTIGATORChow Ka-Man, Ph.D.

Hong Kong Metropolitan University

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Jul 30, 2026