Research on the Design of an Age-Friendly Interface for Blood Sugar Control Software Based on Cognitive Load and Genetic Algorithms

Objective To address the challenges faced by elderly diabetic users in adapting to blood sugar control software, we propose an interface optimization method based on cognitive load and genetic algorithms to reduce interaction cognitive load. Methods We constructed a geometric potential-cognitive load association assessment model, converted the interface into computable gene sequences using slice tree encoding, and defined a matrix based on the cognitive load association strength of elements related to blood glucose management tasks. We then used genetic algorithms to iteratively optimize and generate layout schemes. An empirical study was conducted using a blood glucose control app interface as the object of study, and the effectiveness was validated through usability testing. Results The optimized interface significantly reduced cognitive load for elderly users, with improvements in task completion efficiency and operational accuracy. The application of this method effectively achieved the goal of aging-friendly optimization for blood sugar control software, providing a quantitative optimization pathway based on cognitive load for similar applications.