AI-driven personalised mobile technology for nutrition support and health monitoring in cancer and non-cancer populations
Publication Date
July 25, 2026
Creator
Dalayya Shekinah
Abstract
With cancer being one of the leading causes of death in Malaysia, and given the country’s widespread adoption of mobile technology, this research aims to develop and evaluate an AI-driven mobile health application that facilitates health monitoring and personalised dietary management for both cancer and non-cancer users. The goal is to establish a reliable, evidence-based, and contextually relevant framework for implementing mobile health technologies that are effective and acceptable to the Malaysian population, leveraging AI capabilities to tailor nutrition recommendations to individual user needs.
A mixed-methods research design was employed to include web scraping and sentiment analysis technique, quantitative surveys, and qualitative interviews to provide both breadth and depth of understanding of users’ dietary habits, health monitoring behaviours, and mobile technology use, thereby informing the development of an effective, contextually relevant, and user-centred AI-driven mobile health application. The research commenced with a comprehensive requirement-gathering phase involving web scraping of social media platforms to analyse public perceptions of plant-based diets and identify user needs for a nutrition application designed for cancer and non-cancer users. Additional requirements were gathered via literature reviews, expert consultations, and surveys of cancer and non-cancer patients, providing insights that guided the development of an AI-driven application for diet and health management.
Based on the findings, an AI-driven mobile app for personalised nutrition and health monitoring was designed and developed, grounded in the principles of Protection Motivation Theory (PMT). The AI-driven nutrition app was evaluated through a three-phase study involving expert interviews, a three-week field trial with a total of 139 participants: 120 non-cancer and 19 cancer participants, and post-trial surveys and interviews with 12 cancer participants. The post-surveys included: 1) the mHealth App Usability Questionnaire (MAUQ) to assess the usability of the app, 2) PMT-based surveys to assess users’ perceptions, confidence, and barriers in using the app, and 3) AI Technology Acceptance Model (AI-TAM) to evaluate user attitudes toward AI components of the app, focusing on trust, perceived effectiveness, and intention to use.
Findings from the study demonstrated that the app had good usability. Quantitative analysis using AI-TAM and PMT indicated that perceived usefulness, trust in AI, and self-efficacy were the strongest predictors of user acceptance and motivation toward AI-based health applications. The qualitative data from cancer patients underwent thematic analysis, revealing key themes such as “trust in AI recommendations”. These insights highlight the psychosocial and behavioural factors that must be considered in designing AI-assisted dietary systems.
Overall, the study findings demonstrated that AI-enhanced dietary and health monitoring systems can allow awareness of healthy food choices, and adherence to personalised nutrition plans. This research contributes towards a synthesised design framework for AI-driven nutrition app development for future developers, offering an AI model that bridges preventive healthcare, dietary management, and intelligent user-centred design.
Item Type
ethesis
Thesis Type
PhD
Supervisors
University of Nottingham, Malaysia
University of Nottingham, Malaysia
Subjects (LC)
Associated Schools / Departments
Related Publication URL(s)
UoN Repository URI
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