Paper Title: Mechanisms of short video selection behavior in elderly hypertensives under health information overload: a cognitive load theory
Authors: Ruina Guo, Arina Anis Azlan, Emma Mohamad
Corresponding Author: Arina Anis Azlan (arina@ukm.edu.my)/Malaysia
Abstract
The proliferation of digital health information through short video platforms creates cognitive overload challenges for elderly hypertensive patients managing chronic conditions, compromising effective health information processing and decision-making capabilities. This research investigates the mechanisms of short video selection behavior among elderly hypertensive patients under health information overload, employing cognitive load theory integrated with artificial intelligence analytics to optimize content delivery strategies. A mixed-methods design involving 128 elderly participants (mean age, 71.3 years) from Jiangsu Province utilized behavioral tracking, physiological monitoring, and AI-powered content analysis over a two-week period. The study employed ensemble machine learning algorithms, integrated cognitive load assessment, and structural equation modeling to examine selection pathways and predictive mechanisms. Results demonstrate that cognitive load substantially impacts information processing efficiency, with performance declining from 89.4% accuracy under low cognitive load to 41.2% under high load scenarios. The artificial intelligence framework achieved exceptional predictive performance with 94.2% training accuracy, 92.8% validation accuracy, and 91.5% test accuracy. Feature importance analysis reveals that cognitive variables dominate prediction mechanisms, accounting for 63% of the total importance distribution, compared to behavioral features (23%) and demographic factors (14%). Working memory emerges as the most influential predictor (importance score: 0.847, contributing 18.3% to prediction accuracy), followed by processing speed (16.8%) and attention allocation (15.2%). The research establishes evidence-based guidelines for cognitive-centered health communication design, enabling personalized digital health interventions that optimize content complexity, delivery timing, and presentation modalities based on individual cognitive capacities, ultimately advancing therapeutic outcomes for vulnerable elderly populations through intelligent, adaptive content delivery systems.
Keywords
Cognitive load theory, Artificial intelligence, Health information processing, Elderly hypertensive patients, Digital health communication
