







































Ruina Guo et al. /Future Technology                                                                                     August 2025| Volume 04 | Issue 03 | Pages 
107-118 

107 

 

 

 

Article 

Mechanisms of short video selection behavior in 

elderly hypertensives under health information 

overload: a cognitive load theory 
Ruina Guo1, Arina Anis Azlan1,2*, Emma Mohamad1,2  

1Centre for Research in Media and Communication, Faculty of Social Sciences and Humanities, Universiti Kebangsaan 

Malaysia, Selangor, Malaysia 
2Universiti Kebangsaan Malaysia, Komunikasi Kesihatan (Healthcomm) - UKM Research Group, Selangor, Malaysia 

A R T I C L E   I N F O 
 

Article history: 
Received 09 April 2025  
Received in revised form 
22 May 2025 
Accepted 04 June 2025 
 
Keywords:  
Cognitive load theory, Artificial intelligence, 
Health information processing, 
Elderly hypertensive patients, 
Digital health communication 
 
*Corresponding author 
Email address: 
arina@ukm.edu.my 
 
DOI: 10.55670/fpll.futech.4.3.11 

A B S T R A C T 
 

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. 

1. Introduction 

Rapidly populating age groups around the globe have 
increased the occurrence of hypertension in elderly people, 
which is now a global issue for many healthcare systems. 
Recent epidemic research shows that approximately 70-75% 
of adults over the age of 60 suffer from hypertension [1]. 
Studies show that those who fall into the 65+ age group will 
reach 1.5 billion by 2050, marking this as one of the most 
significant public health challenges. However, the 
proliferation of digital health information through short video 

platforms creates unprecedented cognitive challenges for 
elderly hypertensive patients. Research demonstrates that 
information overload reduces cognitive processing efficiency 
by 25-40% in elderly populations, with hypertensive patients 
experiencing additional cognitive burden due to medication 
effects and age-related working memory decline. The rapid-
fire presentation format of short videos often exceeds the 
cognitive processing capacity of elderly users, creating a 
critical gap between available health information and 
effective health communication for this vulnerable 

Open Access Journal 

 

 

ISSN 2832-0379 

August 2025| Volume 04 | Issue 03 | Pages 107-118 

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Ruina Guo et al. /Future Technology                                                                                     August 2025| Volume 04 | Issue 03 | Pages 107-118 

108 

 

population. The combination of hypertension with ageing 
populations gives rise to greater healthcare needs that most 
ancient medical systems, catering to the elderly's intricate 
comorbidity patterns, struggle to accommodate effectively. 

As indicated in the reference [2], the advent of digital 
technologies has revolutionised the management of 
healthcare services, especially for chronic conditions such as 
hypertension. The availability of wearable gadgets and 
mobile applications allows for real-time blood pressure 
monitoring and remote participation in healthcare activities, 
as noted in the reference [3]. This represents a move from 
episodic clinical encounters to continuous health 
surveillance. However, there are multiple barriers that limit 
effective hypertension management among the elderly, 
including gaps in awareness, adherence to treatment, and 
cognitive impairment [4]. Significant discrepancies in the 
management of hypertension have been documented with 
respect to different elderly cohorts by health science research 
[5]. Furthermore, frail elderly patients face additional 
challenges due to multiple illnesses, disabilities, and the 
medication-associated risk of complications [6]. Conventional 
methods overlook the complexities of managing hypertension 
in older adults [7], while the widespread availability of health 
information through digital means, and especially via short 
videos, poses both advantages and disadvantages for 
educating patients [8]. The creation of short-form video 
platforms has transformed how people consume information, 
but the abundance of health-related content available can 
lead to information overload, potentially straining the 
cognitive abilities of older users. 

Cognitive Load Theory (CLT) is helpful for analysing 
information processing phenomena over the data overload 
threshold [9]. CLT suggests that there is a limit to human 
working memory, and because of this, information 
presentation should be tailored to support learning and 
decision-making [10]. The theory makes distinctions between 
intrinsic, extraneous, and germane cognitive load, largely 
pertaining to the impacts each has on information processing 
[11]. In the context of digital environments, interactive 
elements can heighten user interest as well as cognitive 
workload at the same time [12]. The impact of consuming 
digital media has been linked to attentional fragmentation, 
potential cognitive deterioration, and a host of other issues in 
older adults [13], which raises the question of how their 
cognitive load impacts the health information processing 
done under such content engagement [14]. In recent 
conceptualisations of health sciences, issues related to the 
integration of motivation and emotions alongside cognition 
were acknowledged in relation to health information 
processing due to its multi-faceted reality [15]. Working-age-
related cognitive changes, such as declines in working 
memory and processing speed, increase the difficulty of 
navigating health information for chronic condition-
managing elderly populations. This cognitive vulnerability is 
particularly pronounced in short video environments where 
visual, auditory, and textual information streams compete for 
limited cognitive resources simultaneously. 

The technologies of artificial intelligence have opened 
doors to unprecedented opportunities to analyse and 
improve the delivery of health information services to elderly 
patients suffering from chronic conditions. Machine learning 
algorithms are capable of detecting engagement patterns 
with health content that are far more advanced than 
traditional research techniques [16]. The use of AI-powered 
adaptation systems has been associated with reduced mental 
effort from designated tasks by sheer content screening and 

presentation based on personal preferences and cognitive 
ability [17]. Yet, the use of AI in health communication for 
older adults is mostly unexplored, particularly in the context 
of short-form video content. Studies stemming from the 
health sciences and AI domains demonstrate extremely 
prudent computational aids in elucidating the selection 
processes of information in digital environments [18] 
relevant to elderly hypertensive patients [19]. 

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. The 
study pursues a tripartite research agenda: elucidating 
cognitive load mechanisms during health information 
processing through short video platforms with emphasis on 
working memory limitations and attention allocation 
patterns; establishing causal pathways that demonstrate how 
cognitive load variations systematically influence information 
selection behaviors and content preferences; and developing 
an AI-driven content recommendation framework that 
optimizes health information delivery through dynamic 
adaptation based on real-time cognitive capacity assessment. 
The research innovation centers on integrating cognitive load 
theory with machine learning algorithms to create 
personalized health communication systems specifically 
designed for elderly populations with chronic conditions. This 
investigation provides evidence-based design principles for 
cognitive-centered health communication, predictive models 
that enable real-time content optimization, and 
comprehensive theoretical frameworks that bridge the 
domains of cognitive psychology, health communication, and 
artificial intelligence. 

2. Data and methods 

2.1 Research design and data collection 
To analyze the factors influencing the short video 

selection behavior of elderly hypertensive patients, this study 
integrated qualitative and quantitative methods, including 
interviews, surveys, and digital behavior analysis. This 
protocol was designed based on existing methodologies 
concerning the processing of digital health information by 
older adults [20] and subsequently underwent review by the 
institutional ethics committee. Elderly participants aged 65 
and above have been diagnosed with hypertension and were 
recruited from three community health centres located in 
Jiangsu Province, China. Inclusion criteria comprised: age ≥

65 years, confirmed hypertension diagnosis, smartphone 
ownership, and minimum six-month experience with health-
related short video consumption. Exclusion criteria included: 
severe cognitive impairment (MMSE <24), visual/auditory 
impairments preventing video engagement, and unstable 
cardiovascular conditions.  

The study received institutional ethics approval 
(Protocol: IRB-2023-HSR-047) following the Declaration of 
Helsinki principles. These centres were chosen due to their 
considerable elderly patient populations and diverse 
programs to support digital literacy. Inclusion criteria defined 
engagement with short video services over health-related 
content for a minimum of six months. This resulted in a final 
sample of 128 participants (72 females, 56 males) with an 
average age of 71.3 years (SD = 4.8). The behavioral analysis 
incorporated fifteen feature variables systematically 
captured throughout the observation period, as detailed in 
Table 1. 

 
 



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The data collection process was completed in three steps, 
as shown in Figure 1. The protocol involved three sequential 
phases: baseline assessment including demographic surveys 
and cognitive screening (Days 1-2), primary observation with 
continuous behavioral monitoring during 45-minute daily 
sessions (Days 3-12), and post-observation validation 
interviews (Days 13-14). To start, health information-seeking 
behaviour, particularly in short videos, was explored through 
semi-structured interviews. Their responses detailed vividly 
how they dealt with barriers, the content they preferred, and 
what was useful to them in considering information [21]. 
Later on, participants filled in recognised instruments 
measuring health literacy, digital skills, and self-efficacy 
regarding the management of hypertension. Core data 
collection took place over a two-week period when 
participants were required to passively view short videos. 
Participants had access to a library of 120 health-related 
videos across different platforms. Purpose-built monitoring 
software recorded the time spent watching the videos, how 
the videos were interacted with, and the specific videos 
chosen. The monitoring infrastructure utilized 120 
standardized health videos (30-180 seconds, complexity 
levels 1-5) with physiological sensors, including Empatica E4 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

wristbands (64Hz sampling), Polar H10 heart monitors 
(1000Hz), and Tobii Pro eye-trackers (60Hz) for 
comprehensive data capture. During controlled viewing 
sessions, participants’ attention and emotional response 
were objectively measured by capturing eye tracking, skin 
conductance, and video engagement. The incorporation of 
automated content analysis using artificial intelligence tools 
improved research procedures by classifying video traits and 
retrieving semantic attributes relevant to viewing behaviour 
[22]. This method of technology facilitated the discovery of 
more sophisticated content preference patterns not captured 
through crude monitoring. Cognitive load assessment was 
implemented subjectively using the NASA Task Load Index 
and objectively by quantifying response time during 
concurrent secondary tasks. Such extensive data sets, 
including the comprehensive multi-modal dataset obtained 
through this design, are invaluable for studying the highly 
intricate relationship between cognitive load and information 
selection strategies of elderly patients suffering from 
hypertension. Quality assurance included inter-rater 
reliability assessment (κ >0.80), daily technical calibration, 
and data privacy protection following Chinese Personal 
Information Protection Law requirements. 

Table 1. Behavioral feature variables and measurement specifications 

Variable Definition Measurement Type Range α 

Viewing Duration Video watching time Tracking software Continuous 0-300s 0.92 

Interaction Frequency User interactions per 
session 

Behavioral logging Discrete 0-50 0.89 

Complexity Preference Preferred content 
complexity 

Likert scale Ordinal 1-5 0.84 

Access Pattern Daily consumption 
timing 

Timestamp analysis Categorical 6 periods 0.91* 

Attention Allocation Gaze distribution 
percentage 

Eye-tracking Continuous 0-100% 0.87 

Pause Frequency Video pausing 
behavior 

Analytics platform Discrete 0-20 0.93 

Replay Behavior Content rewatching 
frequency 

Video analytics Discrete 0-10 0.88 

Sharing Intent Information sharing 
willingness 

Self-report Ordinal 1-7 0.82 

Cognitive Load Physiological stress 
response 

Multi-sensor Continuous 0-10 0.90 

Processing Speed Response time to 
queries 

Reaction timer Continuous 0.5-5s 0.86 

Retention Rate Content recall 
accuracy 

Memory test Continuous 0-100% 0.91 

Sustained Attention Continuous 
engagement duration 

Monitoring system Continuous 0-180s 0.89 

Choice Consistency Selection pattern 
reliability 

Algorithm analysis Continuous 0-1 0.85 

Social Responsiveness Peer influence 
susceptibility 

Interaction tracking Ordinal 1-5 0.83 

Tech Adaptation Navigation learning 
speed 

Task timing Continuous 30-300s 0.87 

     α = Cronbach's alpha; * = Cohen's kappa 

 



Ruina Guo et al. /Future Technology                                                                                     August 2025| Volume 04 | Issue 03 | Pages 107-118 

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Phase 1:Participant Recruitment and Preliminary Assessment

Purposive Sampling

(n=128, Age 65+, Diagnosed 

hypertension)

Semi-structured Interviews

(Exploring usage habits and 

attitudes)

Standardized Questionnaires

(Health literacy, Digital 

competence)

Phase 2:Digita Behavior Tracking

Curated Video Library

(120 health-related videos)

Two-week Monitoring 

Period

(Viewing patterns and 

selections)

Al-enabled Content Analysis

(Semantic features 

extraction)

Phase 3: Cognitive Load Assessment

Physiological Measurements

(Eye tracking, Skin 

response)

Subiective Assessment

(NASA Task Load Index)

Objective Measures

(Response time to secondary 

tasks)

Research Design and Data Collection Framework

Comprehensive Multi-modal Dataset for Analysis  
 

Figure 1. Research design and data collection framework 

2.2  Artificial intelligence analysis methods and 
cognitive load measurement 
This study employed sophisticated artificial intelligence 

methods for analyzing short video segments and evaluating 
cognitive load in elderly patients suffering from hypertension. 
As previously mentioned, the system of AI content analysis 
used deep learning algorithms for extracting a myriad of 
features like visual intricacy, storyline architecture, and even 
story prominence from short health-related videos, such as 
their descriptions [23]. Information from each video was 
processed by a custom-designed convolutional neural 
network that extracted visual features and recurrent neural 
networks that focused on the text and voice, resulting in a 
richly featured video. This study applied the AI techniques 
outlined in Table 2 alongside the methods for measuring 
cognitive load highlighted and defined in the table. The multi-
layered feature extraction approach enabled fine-grained 
analysis of content characteristics that potentially influence 
information processing in elderly viewers. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Cognitive load was operationalized using a hybrid 
measurement approach that integrated both objective 
physiological indicators and subjective self-report 
instruments [24]. Quantitative measurement employed 
synchronized physiological monitoring: pupillometry (2-
8mm range), heart rate variability (LF/HF ratios >2.5 
indicating stress), and electrodermal activity (≥ 0.05 μ S 

response amplitudes). Baseline values were established 
during 60-second pre-viewing periods. These data streams 
were synchronized and processed using a specialized 
algorithm that calculated the Integrated Cognitive Load Index 
(ICLI) according to the formula: 

/

1

1 N
pi b LF HF

i b b b

EP P H
ICLI

N P H E
  

=

     −
=  +  +      

     
  (1) 

Where ICLI represents the integrated cognitive load 
index, α, β, and γ are weighting coefficients derived from 
calibration procedures, P is pupil diameter, with 𝑃𝑏  as a 
baseline, H denotes heart rate variability ratio metrics with 
𝐻𝑏  as baseline, E indicates electrodermal activity 
measurements with 𝐸𝑝 representing peak response and 𝐸𝑏 as 

a baseline. Psychometric validation established robust ICLI 
properties: convergent validity with NASA Task Load Index (r 
= 0.78, p < 0.001), test-retest reliability (r = 0.84, 95% CI: 
0.79-0.88), and 89.3% classification accuracy across cognitive 
load conditions. Optimal weighting coefficients were α = 0.45 
(pupillometry), β = 0.35 (heart rate variability), and γ = 0.20 
(electrodermal activity). This comprehensive measurement 
technique enabled the accurate distribution of cognitive loads 
that the subjects experienced while engaging with the video 
content. AI integration employed dual-stream processing for 
real-time physiological analysis and content feature 
extraction, achieving 91.7% cognitive load prediction 
accuracy with sub-200ms response latency. AI algorithms 
then analysed how different features of the videos related to 
patterns of cognitive load, determining from which pieces of 
content information processing complexity was optimised 
[25]. Machine learning models using transfer learning 
approaches adapted existing frameworks to the domain of 
health information processing among older adults. This 
research integration marks an important milestone in the 
application of AI in health communication studies for 
vulnerable groups.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Table 2. Artificial intelligence methods and cognitive load measurement techniques 

      
Category Method Description Data Type Application 

AI Content 
Analysis 

CNN-LSTM Hybrid 
Deep learning architecture combining 
convolutional and recurrent networks 

Video frames and 
audio 

Visual complexity and temporal 
feature extraction 

Transformer-based 
NLP 

Pre-trained language models fine-tuned for 
health terminology 

Text transcripts 
Semantic content analysis and health 

literacy assessment 

Multimodal fusion 
Attention mechanism for cross-modal feature 

integration 
Combined 
modalities 

Holistic content representation 

Cognitive Load 
Measurement 

Pupillometry High-frequency pupil diameter tracking Continuous Momentary cognitive load fluctuation 
Heart Rate 
Variability 

LF/HF ratio analysis 
Frequency 

domain 
Sustained mental workload 

Electrodermal 
Activity 

Skin conductance response analysis Event-related Emotional and attentional engagement 

NASA Task Load 
Index 

Six-dimension subjective rating scale Self-report Perceived mental demand and effort 

AI-Cognitive 
Integration 

Temporal 
alignment 

Dynamic time warping for signal 
synchronization 

Time series Multi-stream data integration 

Feature 
importance 

SHAP value calculation for explainable AI Post-hoc analysis 
Identifying critical content features 

affecting cognitive load 

Personalized 
modeling 

Federated learning with privacy preservation 
Individual 

profiles 
Adaptive cognitive load prediction 

 

 



Ruina Guo et al. /Future Technology                                                                                     August 2025| Volume 04 | Issue 03 | Pages 107-118 

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Cognitive load inference utilized sliding window analysis 
(10-second windows), identifying load spikes when multiple 
indicators exceeded thresholds simultaneously. Machine 
learning algorithms distinguished content-induced load from 
baseline cognitive effort. 

3. Results 

3.1 Behavioral characteristics of short video usage 
among elderly hypertensive patients 
This study captures several unique behaviours 

associated with short-form video consumption among elderly 
hypertensive patients using AI-based content classification 
and sophisticated behavioural tracking systems. The analysis 
shows that elderly hypertensive patients display divergent 
patterns of engagement that set them apart from younger 
patients, as well as from the general baseline standard of 
health information seeking defined through older media 
types. Statistical analysis employed ANOVA for group 
comparisons and chi-square tests for categorical variables. 
Sample size (N=128) was determined through power analysis 
for detecting medium effect sizes (Cohen's d = 0.5) with 80% 
power at α = 0.05. Figure 2 illustrates the comprehensive 
behavioral profile through four key dimensions. The viewing 
duration distribution, as shown in Figure 2(a), demonstrates 
that elderly hypertensive patients predominantly engage 
with content ranging from 60-90 seconds, with peak 
engagement occurring at 75-90 seconds, where 91 
participants showed optimal attention retention. The error 
bars in Figure 2(a) represent 95% confidence intervals, 
providing statistical precision for participant distribution 
across viewing duration categories. ANOVA revealed 
significant age-related differences in viewing duration 
(F(2,125) = 12.47, p < 0.001, η² = 0.17), with younger 
participants (65-70 years) showing longer engagement (M = 
87.3s, 95% CI: 82.1-92.5) than older groups (p < 0.001). This 
temporal preference pattern indicates that elderly users 
require sufficient processing time while maintaining focus 
within manageable content segments.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

The distribution reveals a clear preference threshold, 
with engagement declining precipitously beyond 120 
seconds, suggesting cognitive load limitations inherent to this 
demographic. Content complexity preferences, depicted in 
Figure 2(b), reveal an overwhelming preference for simplified 
health information presentation. The 95% confidence 
intervals displayed in Figure 2(b) demonstrate the statistical 
reliability of preference measurements across complexity 
levels. The study documents that 85% of participants 
demonstrated a strong affinity for very low complexity 
content, while 72% preferred low complexity materials. 
Medium complexity content received moderate acceptance at 
45%, whereas high and very high complexity formats showed 
minimal adoption rates of 23% and 8%, respectively. Chi-
square analysis confirmed significant associations between 
complexity preferences and education level (χ²(8) = 23.64, p 
= 0.003, Cramer's V = 0.31). This complexity gradient reflects 
the critical importance of cognitive accessibility in health 
information design for elderly populations, particularly those 
managing chronic conditions requiring consistent 
information processing. 

The temporal engagement analysis presented in Figure 
2(c) identifies distinct circadian patterns in short video 
consumption behavior. Morning hours (8:00-10:00 AM) 
demonstrate peak engagement levels reaching approximately 
42%, followed by a gradual decline during midday periods to 
13-15%, and a subsequent evening resurgence (7:00-9:00 
PM), achieving 30% engagement. Cosinor analysis validated 
significant circadian variation (p < 0.001, R² = 0.68) with 
morning peak at 42.3% (95% CI: 38.7-45.9) and evening peak 
at 29.8% (95% CI: 26.4-33.2). This bimodal distribution 
aligns with established elderly activity patterns and suggests 
optimal timing strategies for health information 
dissemination through short video platforms. Figure 2(d) 
presents the behavioral clustering matrix identifying four 
distinct user phenotypes with heterogeneous engagement 
patterns.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 Figure 2. Short Video Usage Behavioral Patterns Among Elderly Hypertensive Patients (N=128). (a) Viewing Duration Distribution 

with 95% CI. (b) Content Complexity Preference with 95% CI. (c) Temporal Engagement with 95% CI. (d) Behavioral Clustering Matrix 

showing four user phenotypes. 



Ruina Guo et al. /Future Technology                                                                                     August 2025| Volume 04 | Issue 03 | Pages 107-118 

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Cluster 1 exhibits balanced moderate preferences across 
all parameters, while Cluster 2 demonstrates high duration 
tolerance (3.8) with low complexity requirements (2.1). 
Cluster 3 shows brief engagement (1.9) coupled with high 
complexity acceptance (4.1), and Cluster 4 displays high 
duration (3.3) and interaction preferences (3.9) with low 
complexity tolerance (1.7). K-means clustering achieved an 
optimal solution (silhouette width = 0.67), with MANOVA 
confirming significant multivariate differences (Wilks' λ = 
0.23, F(12,369) = 23.87, p < 0.001, η² = 0.44). These 
phenotypic variations illuminate the necessity for 
personalized content delivery strategies that accommodate 
diverse information processing capabilities within the elderly 
hypertensive demographic. 

Comparative analysis across health information 
channels, as detailed in Table 3, reveals significant variations 
in trust and usage patterns. ANOVA indicated significant 
differences in trust ratings across sources (F (7,896) = 127.34, 
p < 0.001, η² = 0.50), with healthcare professionals achieving 
the highest trust levels (M = 4.8, SD = 0.4).  Notably, short 
video platforms exhibit the highest sharing behavior (28.6%) 
among digital channels, indicating substantial social 
engagement potential despite lower retention rates (45.7%). 
This paradox suggests that while elderly hypertensive 
patients may not retain short video content as effectively as 
traditional sources, they demonstrate greater willingness to 
share and discuss this content within their social networks. 
The integration of artificial intelligence in content analysis 
enables precise categorization of health information 
complexity and engagement prediction, facilitating evidence-
based content optimization for elderly hypertensive patients. 
The clustering analysis demonstrates that personalized 
approaches acknowledging individual behavioral phenotypes 
can enhance engagement effectiveness, while the broader 
channel analysis positions short video platforms as 
complementary rather than replacement tools within existing 
health information ecosystems. 

3.2 Impact Mechanisms of Cognitive Load on 
Information Selection 
This research establishes a comprehensive framework 

elucidating how cognitive load influences health information 
selection behaviors among elderly patients.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

The investigation employs advanced statistical modeling 
to uncover pathways connecting cognitive resource 
constraints with processing preferences, providing insights 
for artificial intelligence-driven health communication 
systems. Figure 3 demonstrates systematic variations in 
information processing capabilities across cognitive load 
conditions. The polar representation in Figure 3(a) reveals 
balanced allocation patterns of attention, working memory, 
and processing capabilities under optimal low cognitive load 
conditions. Figure 3(b) establishes critical performance 
thresholds across processing stages. Under low cognitive 
load, elderly patients maintain efficiency scores exceeding 
79% from encoding through response generation. Moderate 
load conditions show performance deterioration to 52-78%, 
while high load scenarios prove detrimental, with efficiency 
scores plummeting to 18-45%, indicating that complex health 
information may overwhelm cognitive capabilities. The 
temporal analysis in Figure 3(c) reveals processing 
sustainability patterns. Low cognitive load enables stable 
performance for approximately 10 seconds, moderate load 
demonstrates gradual decay, while high load conditions cause 
rapid deterioration within 4-5 seconds. Figure 3(d) quantifies 
practical implications through load level comparisons. 
Processing speed increases fourfold from 3.2 seconds under 
low load to 12.4 seconds under high load. Accuracy 
deteriorates from 89.4% to 41.2%, while engagement 
decreases from 85% to 28%, establishing clear performance 
benchmarks for AI algorithm development. The structural 
equation modeling in Figure 4 elucidates the underlying 
mechanisms governing these patterns. Figure 4(a) identifies 
hierarchical cognitive load factors, with working memory as 
the dominant component (loading = 0.86), followed by 
attention allocation (0.78) and processing speed (0.72). 
Interference resistance (0.68) and cognitive flexibility (0.63) 
represent additional constraining factors determining 
processing capacity limitations. Figure 4(b) reveals selection 
pathways with decreasing coefficients from complexity 
perception (0.68) through social influence (0.29), indicating 
hierarchical information processing preferences. This 
cascading pattern suggests elderly patients prioritize 
complexity assessment during selection, with social 
influences playing secondary roles. These pathways provide 
essential guidance for AI system design priorities. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Table 3. Health information channel preferences: trust, usage, and engagement metrics among elderly hypertensive patients (N=128) 

Information 
Source 

Trust 
Level 
(1-5) 

Usage 
Frequency 

(%) 
Content Preference 

Engagement 
Duration 

(min) 

Retention 
Rate (%) 

Sharing 
Behavior 

Preferred 
Format 

Healthcare 
Professionals 

4.8 78.2 
Medical advice, 
prescriptions 

15.3 92.4 
Low 

(8.2%) 
Face-to-face 
consultation 

Medical 
Websites 

4.2 45.6 
Symptom checking, 

drug information 
8.7 67.3 

Very Low 
(3.1%) 

Text-based 
articles 

Health Apps 3.9 52.1 
BP monitoring, 

medication reminders 
12.4 71.8 

Low 
(12.4%) 

Interactive 
interfaces 

Short Video 
Platforms 

3.6 68.9 
Lifestyle tips, exercise 

demos 
3.2 45.7 

Moderate 
(28.6%) 

Visual 
demonstrations 

Family/Friends 3.4 72.4 
Personal experiences, 

recommendations 
25.6 83.2 

High 
(65.3%) 

Verbal 
communication 

Television 
Programs 

3.7 41.3 
Health 

documentaries, 
expert interviews 

35.2 78.9 
Very Low 

(2.4%) 
Traditional 
broadcast 

Print Materials 4.1 23.7 
Brochures, 
educational 
pamphlets 

18.9 85.6 
Very Low 

(1.8%) 
Text and 

illustrations 

Social Media 
Groups 

2.9 34.8 
Patient discussions, 

support groups 
22.1 58.4 

Moderate 
(34.7%) 

Text and image 
posts 

 

      
 

 



Ruina Guo et al. /Future Technology                                                                                     August 2025| Volume 04 | Issue 03 | Pages 107-118 

113 

 

 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

Figure 3. Cognitive Load Effects on Information Processing in Elderly Patients (a) Cognitive resource allocation under low load. (b) Processing 

efficiency across load levels. (c) Temporal performance decay patterns. (d) Speed, accuracy, and engagement comparisons 

 

 

Figure 4. Path Analysis of Cognitive Load and Information Selection (a) Cognitive load factor loadings. (b) Selection pathway coefficients. (c) 
Mediation effects analysis. (d) Path coefficient matrix with model fit indices. (Note: CL=Cognitive Load, CP=Complexity Perception, PC=Processing 
Confidence, EM=Engagement Motivation, IS=Information Selection, BO=Behavioral Outcome; Significance: * p<0.05, ** p<0.01, *** p<0.001) 

 
 



Ruina Guo et al. /Future Technology                                                                                     August 2025| Volume 04 | Issue 03 | Pages 107-118 

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The mediation analysis in Figure 4(c) demonstrates 
sophisticated relationships between cognitive load and 
information selection. Confidence pathways exhibit the 
strongest mediating influence, with indirect effects (0.45) 
substantially exceeding direct effects (0.23). This indicates 
that cognitive load primarily influences selection through 
perceived self-efficacy rather than direct processing 
limitations. Relevance pathways show moderate mediation 
effects (indirect: 0.32, direct: 0.20), suggesting that cognitive 
load affects patients' ability to assess information relevance 
accurately. 

Figure 4(d) presents the path coefficient matrix, 
confirming robust statistical relationships throughout the 
model. Cognitive load demonstrates significant negative 
associations with complexity perception (β = -0.68, p < 0.001) 
and processing confidence (β = -0.52, p < 0.001). Engagement 
motivation serves as a critical mediator (β = 0.48, p < 0.01), 
while information selection behaviors predict behavioral 
outcomes with high precision (β = 0.79, p < 0.001). The 
model's excellent fit indices (CFI = 0.952, RMSEA = 0.048) 
validate the theoretical framework. 

These findings demonstrate that cognitive load 
influences information selection through multiple 
interconnected mechanisms involving perceptual, 
confidence-based, and temporal pathways. The study 
supports the creation of AI algorithms that use dynamic 
strategies for cognitive load assessment, active complexity 
modification, and personal timing optimisation. 
Implementation of these mechanisms makes possible 
automated intelligent health communication systems that 
modify how information is presented based on the real-time 
assessment of the patient's cognitive workload, thereby 
improving health outcomes for elderly patients living with 
chronic illness. Incorporating these findings into AI-based 
health services provides further sophistication to the design 
of technologies intended for older adults, illustrating a 
profound development in gerontechnology. 

 

 

 

3.3 Artificial intelligence-based content preference 
prediction model 
This study develops a more sophisticated artificial 

intelligence system designed to anticipate the health 
information preferences of elderly hypertensive patients, 
utilizing cognitive load evaluation and behavioral pattern 
recognition. The proposed model makes use of algorithms 
built on cognitive, real-time monitoring, and predefined 
processes to streamline communications to the subject’s 
health monitoring systems based on their information intake 
methods and behavioural patterns. The study utilises 
prediction models defined by hierarchical structures of 
simpler models, which rely on differing methodologies for 
computing the target value for better prediction accuracy 
amongst different age groups of elderly people. The ensemble 
model, as shown in Figure 5(a), significantly outperforms 
individual algorithms, achieving a remarkable 94.2% training 
accuracy, 92.8% validation accuracy, and 91.5% test 
accuracy. The neural network approach achieves competitive 
performance as well, with 91.3% training accuracy, 
demonstrating the ability to model complex interactions 
between cognitive load metrics and content selection. 
Random forest algorithms offer strong baseline performance, 
providing reliable and generalised accuracy across subgroups 
of patients. Cross-validation demonstrated robust 
generalizability: ten-fold stratified validation yielded μ = 
91.7% (σ = 1.4%), leave-one-group-out validation across age 
subgroups showed <3.5% degradation (range: 88.2%-
91.5%), and geographic validation achieved 89.3% accuracy. 
Bootstrap resampling (n = 2000) confirmed stability (95% CI: 
90.1-93.2%). Exploration of the content preference algorithm 
revealed several predictive mechanisms that rely heavily on 
feature importance. Cognitive characteristics overwhelm the 
model as highlighted in Figure 5(b), constituting 63 percent 
of the total importance distribution. Behavioural features 
assist in their role to provide accuracy with 23 percent, while 
in combination, a myriad of demographic, clinical, 
andpsychosocial attributes make up a mere 14 percent in 
support of the model.  

 

 
 
 

Figure 5. AI-Based Content Preference Prediction Model Accuracy Comparison. (a) Algorithm performance with 95% CI across training, 

validation, and test phases. (b) Feature category importance distribution in the prediction framework 



Ruina Guo et al. /Future Technology                                                                                     August 2025| Volume 04 | Issue 03 | Pages 107-118 

115 

 

 The allocation displays a disproportionate amount of 
reliance on the underdeveloped cognitive assessments posed 
on the elderly chronic condition bearers within tailored 
communication frameworks, intending to target strategic 
action execution to optimise health. Based on the detailed 
feature ranking on working memory capacity presented in 
Table 4, it was clearly shown that working capacity had the 
greatest predictor value with the highest importance score of 
0.847, which accounted for 18.3% of prediction accuracy or 
prediction value. As the next major variable in importance, 
processing speed provides a secondary level of importance at 
0.792 importance score, hence contributing approximately 
16.8%. Following, the attention allocation abilities emerged 
as the third most important score for these cognitive 
variables (0.731, contribution: 15.2%). These cognitive 
variables are of high clinical importance and significant 
predictive capability, reinforcing clinical data regarding the 
optimal content complexity and the most suitable time for 
presentation for elderly hypertensive patients. Age-stratified 
performance varied: 65-70 years (93.4%), 71-75 years 
(91.8%), 76+ years (88.7%). Working memory importance 
increased with age (0.72, 0.83, 0.91, respectively), indicating 
age-adaptive calibration requirements. 

Behavioral features such as information complexity 
preference and engagement duration have importance scores 
within a moderate range of 0.573 to 0.689, showing that they 
help improve prediction accuracy but do not independently 
drive change. The work demonstrates that temporal 
processing styles greatly affect behavioural patterns related 
to content consumption, while the personalization achieved 
by the AI model was through considering individual cognitive 
rhythm differences across daily activities. The AI model 
dynamically adjusts content complexity, timing, and 
presentation modality according to circadian rhythms and 
real-time cognitive monitoring, ensuring optimal information 
access during periods of peak cognitive capacity. Clinical 
validation demonstrated that AI-guided recommendations 
significantly improved patient engagement metrics, 
satisfaction ratings, and information retention compared to 
traditional methods. This implementation enables care teams 
to provide personalized patient education with reduced 
cognitive load, representing a breakthrough in responsive 
health communication technology for elderly populations 
with chronic conditions. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

The cognitive-behavioral framework shows extension 
potential to other chronic conditions. Pilot testing with 
diabetes patients (n = 32) achieved 87.3% accuracy, while 
cardiovascular patients demonstrated similar cognitive 
patterns. However, systematic validation across conditions 
remains necessary. Real-time deployment faces 
computational constraints: current architecture requires 
247ms latency and 1.2GB memory, while mobile platforms 
need <100ms and <256 MB. The ensemble demands 15.3 
million operations per prediction, necessitating edge 
computing solutions. Three optimization variants address 
deployment constraints: reduced-feature model (89.1% 
accuracy, 67% computational reduction), lightweight neural 
network (90.3% accuracy, 45MB size), and hybrid approach 
(91.8% accuracy, 78ms latency), enabling practical 
implementation. 

4. Discussion  

This study adds important empirical evidence regarding 
the applicability of cognitive load theory in relation to 
understanding information processing behaviours of older 
adults with chronic illnesses. The results show that cognitive 
load theory captures well the information selection strategies 
observed in digital health settings, thus broadening the scope 
of Castro-Alonso et al.’s research on pedagogical 
visualizations to health care communication [26]. The 
analysis shows that cognitive load is a major factor affecting 
information processing efficiency; performance dropped 
from 89.4% accuracy in low load conditions to 41.2% 
accuracy in high load conditions. These findings align with 
Kirschner's cognitive load theory principles [27] while 
extending the theoretical framework to encompass age-
related cognitive changes. The hierarchical factor structure 
identified, with working memory as the dominant 
component, supports Leppink's emphasis on working 
memory limitations in elderly populations [28]. These 
findings establish cognitive load theory as a validated 
framework for digital health communication design in aging 
populations. The artificial intelligence-driven approach 
contributes novel insights into personalized health 
communication strategies. The ensemble model's superior 
performance (94.2% training accuracy) validates multi-
algorithmic approaches in capturing complex cognitive-
behavioral relationships.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 4. Key feature variables' importance ranking for the AI prediction model 

Rank Feature Variable 
Importance 

Score 
Category 

Clinical 
Relevance 

Prediction Contribution 
(%) 

1 Working Memory Capacity 0.847 Cognitive High 18.3 

2 Processing Speed 0.792 Cognitive High 16.8 

3 Attention Allocation 0.731 Cognitive High 15.2 

4 
Information Complexity 

Preference 
0.689 Behavioral High 13.7 

5 Temporal Processing Pattern 0.652 Cognitive Medium 12.4 

6 Engagement Duration 0.618 Behavioral Medium 11.1 

7 Content Modality Preference 0.573 Behavioral Medium 9.8 

8 Health Literacy Level 0.542 Demographic Medium 8.9 

9 Technology Familiarity 0.496 Behavioral Medium 7.6 

10 Age Group 0.451 Demographic Low 6.2 

11 Education Level 0.423 Demographic Low 5.4 

12 Comorbidity Index 0.389 Clinical Low 4.8 

13 Medication Complexity 0.367 Clinical Low 4.1 

14 Social Support Level 0.334 Psychosocial Low 3.7 

15 Gender 0.298 Demographic Low 2.9 

 

      
 

 



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116 

 

This finding extends Sigolo and Casarin's research on 
cognitive load theory applications to information overload by 
demonstrating successful implementation in vulnerable 
elderly populations [29]. The feature importance analysis 
revealing cognitive variables' dominance (63% of predictive 
power) contrasts with traditional health communication 
models, aligning with WHO recommendations for AI 
technologies benefiting older people through cognitive-
centered design approaches. This investigation advances 
health communication theory by establishing working 
memory as the dominant predictor (β = 0.847) and 
demonstrating superior cognitive-behavioral pathways over 
demographic models, extending cognitive load theory into 
digital health domains with temporal optimization insights. 
Gao et al.'s studies on AI-directed social media expression of 
older adults demonstrate the integration of social support 
systems into AI frameworks as a promising [30]. Clinical 
implementation enables immediate optimization through 
evidence-based parameters, while simplified cognitive load 
assessment tools could enhance healthcare adoption [31]. 

While these advances demonstrate clinical readiness, 
several methodological considerations warrant 
acknowledgment. The research acknowledges challenges 
highlighted by Chu et al. regarding digital ageism and the need 
for inclusive AI design for older adults [32]. The study's 
findings contrast with Zhang et al.'s concerns about short 
video impacts on elderly mental health, demonstrating that 
carefully designed AI-guided content can enhance rather than 
impair cognitive engagement [33]. The research establishes 
that elderly hypertensive patients exhibit distinct behavioral 
phenotypes in short video consumption, indicating the 
necessity for personalized content delivery strategies that 
accommodate diverse information processing capabilities. 

Generalizability analysis reveals robust age-stratified 
performance (65-70 years: 93.4%, 76+ years: 88.7%) and 
preliminary chronic disease validation (diabetes: 87.3% 
accuracy), though systematic cross-condition verification 
remains necessary. The attention given to participants from 
Jiangsu Province might be culturally and technologically 
distinctive, and therefore might not align with the elderly 
demographic from other parts of the world. Wu et al.'s work 
on the exposure of Chinese elderly women to short-form 
videos suggests that there might be specific cultural 
boundaries that impact the wide-ranging applicability of such 
research in the global context [34]. Cultural context 
significantly influences applicability, as Chinese elderly 
demonstrate distinct digital literacy and family-centered 
decision-making patterns requiring adaptation for Western 
individualistic healthcare contexts. The two-week duration 
allotted for observation may not account for long-term 
changes in behaviour or seasonal shifts in cognitive functions. 
The AI model's dependence on certain physiological markers 
poses barriers to practical application in healthcare, which 
may not have convenient access to the necessary monitoring 
equipment. 

Future research priorities encompass three critical 
domains for advancing clinical implementation and 
theoretical development. Longitudinal validation studies 
spanning 12-24 months are essential for examining 
behavioral stability and adaptation effects across extended 
timeframes. Cross-cultural validation across Western and 
Eastern healthcare contexts will illuminate universal versus 
culture-specific aspects of cognitive load mechanisms in 
elderly populations. Randomized controlled trials comparing 
AI-guided versus traditional patient education approaches 
will provide definitive efficacy evidence for evidence-based 

clinical implementation. The work lays the groundwork for 
designing health communication systems focused on seniors 
and highlights the role of AI in mitigating the information 
overload crisis facing vulnerable older populations. These 
evidence-based frameworks provide immediate 
implementation pathways for healthcare providers while 
establishing foundational principles for age-inclusive digital 
health technology development, offering constructive 
guidance for improving health outcomes through 
personalized content systems. 

5. Conclusion  

The study makes important theoretical and practical 
advancements through the assessment of cognitive load and 
utilisation of artificial intelligence in the processing of health 
information by elderly hypertensive patients. The 
investigation demonstrates how cognitive load affects 
information processing: performance drops from 89.4% 
accuracy with low cognitive load to 41.2% accuracy with high 
load. The study confirms the applicability of cognitive load 
theory to digital health contexts while also expanding the 
treatment frameworks to include age-related cognitive 
decline and chronic disease management. From the 
hierarchical factor analysis, the strongest predictor in 
working memory (importance score: 0.847, 18.3% of 
prediction) was verified, thus proving the designed principles 
of communication in health focused on the cognitive aspects. 
With an ensemble approach, the AI system provided 
unprecedented results with training accuracy of 94.2% and 
validation and testing scores of 92.8% and 91.5%, 
respectively. Individual algorithms were outperformed 
considerably. In the feature importance analysis from the 
ensemble model, cognitive factors made up 63% of the total 
contribution in comparison to 23% from behavioural features 
and 14% from demographics. These results contested 
existing health communication models that centre on 
demographics, encouraging a new direction that incorporates 
cognitive-driven customisation for older individuals with 
chronic diseases. Practical applications transcend validation 
to encompass real-world implementations in healthcare. This 
study develops border healthcare policies for timing, content, 
presentation modality, and complexity in health 
information—their individual cognition determines the 
reasoning, not demographics’ generalisations. The AI's 
capacity to accommodate cognitive timing enhances patient 
education effectiveness while alleviating the negative effects 
of information overload. Future research includes 
longitudinal verification over longer periods, cross-cultural 
feasibility studies, and the development of blunt but easy-to-
use cognitive tests for wider clinical use. This investigation 
with 128 participants over 2 weeks lays the groundwork for 
designing health communication centred around cognitive 
considerations and offers a foundation to mitigate the 
difficulties presented by digital health devices. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The author adheres to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the authors. 



Ruina Guo et al. /Future Technology                                                                                     August 2025| Volume 04 | Issue 03 | Pages 107-118 

117 

 

Conflict of interest 

The authors declare no potential conflict of interest. 

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