Evaluating the Usability of mHealth Apps: An Evaluation Model Based on Task Analysis Methods and Eye Movement Data

Area of Science:

• Health Informatics
• Human-Computer Interaction
• Diabetes Management Technology

Background:

• Mobile health (mHealth) apps are increasingly vital for managing chronic diseases like diabetes.
• China faces a significant diabetes burden, necessitating user-friendly mHealth solutions.
• Existing usability evaluation methods may not fully capture the user experience of mHealth applications.

Purpose of the Study:

• To develop and validate a novel usability evaluation model for mHealth apps focused on self-management of diabetes.
• To assess the effectiveness of the proposed model in improving app usability.
• To provide a framework for enhancing the user-friendliness of diabetes management mHealth applications.

Main Methods:

• A hybrid methodology combining task analysis (error logs, post-task questionnaires) and eye-tracking data.
• Development and evaluation of a prototype blood glucose recording application using the proposed model.
• Comparative analysis of usability before and after prototype modifications based on evaluation findings.

Main Results:

• Task analysis-based improvements enhanced interaction usability by approximately 24%.
• Eye-tracking data analysis for hotspot movement acceleration improved information access usability by about 15%.
• The model demonstrated feasibility and effectiveness in identifying areas for usability enhancement.

Conclusions:

• The presented usability evaluation model offers a robust approach for assessing and improving mHealth apps for diabetes self-management.
• Integrating task analysis and eye-tracking provides comprehensive insights into user interaction and information accessibility.
• This research contributes to the development of more effective and user-centered mHealth tools for a growing diabetic population.