The role of cognitive ergonomics in enhancing work efficiency and reducing workload among older employees: An EEG and HRV analysis

Abstract

Background:

The ageing workforce is increasing in today's work environments, bringing unique challenges related to productivity and workload management especially in adapting into modern technology and high cognitive workload demands. The demands of modern work systems often worsen workload levels among ageing office workers, potentially affecting their performance and well-being.

Objectives:

This study explores the potential of Cognitive Ergonomic-Driven Technology (CEDT) as an intervention to mitigate workload and enhance work performance in ageing office workers.

Method:

The experiment involved the application of CEDT during task performance for both managerial and supporting staff. Performance metrics, Heart Rate measures, electroencephalogram (EEG) based Beta-Alpha Ratio (BAR)-measuring cognitive workload, and NASA Task Load Index (TLX)-measuring overall workload, all workload metrics were measured before and after the intervention. BAR is measuring the thirty participants involved in this study, evenly split between managerial and supporting staff.

Result:

The results demonstrate significant improvements in performance scores (PS) and reductions in strain indicators such as heart rate and BAR following the CEDT intervention. Correlation analysis revealed that effort demand (r = −0.542, p < 0.05) was a key factor influencing arithmetic task (task II) outcomes for managerial staff, while performance demand (r = −0.718, p < 0.01) was more critical for supporting staff in typing task (task I).

Conclusion:

These findings indicate that CEDT can enhance work performance and reduce workload among ageing workers, with varying impacts depending on the job role. Practical implementation may face challenges, including potential resistance to technology and cost implications, especially among ageing workers. Future research should explore long-term effects, particularly regarding cognitive fatigue and adaption over time, as well as the customization of CEDT for different job roles.

Keywords

cognitive ergonomics work efficiency workload aged electroencephalography heart rate variability

Introduction

Ageing workforce is defined as an employee aged 50 years and above who are actively working. This group of workforces is posed a few characteristics such as decline in cognitive and physical abilities.^1–3 The global workforce is undergoing a significant demographic shift as a larger proportion of employees are now ageing. This transition towards an older workforce has profound implications for productivity, as well as for the design and operation of work systems.^1,2 Ageing workers face unique challenges, including declines in cognitive and physical abilities, which can hinder their ability to adapt to modern technology, and fast-paced work environments.^3–5 These challenges necessitate the development of strategies to maintain productivity while also addressing the specific needs of older employees.

Cognitive ergonomics, a field focused on optimizing the fit between human cognitive abilities and the demands of work systems, offers a promising avenue for improving the performance and well-being of ageing workers.^6–8 By understanding how cognitive processes such as perception, memory, and decision-making interact with job tasks, cognitive ergonomics aims to design work environments that reduce cognitive load and enhance performance.^9–11 This is particularly important for older workers, who may experience a natural decline in cognitive capabilities over time.^12–14 Leveraging cognitive ergonomics can help mitigate these effects, enabling older workers to maintain high levels of performance.^15,16

One of the most promising advancements in this field is the development of Cognitive Ergonomic-Driven Technologies (CEDT). These technologies are designed to support cognitive functions and reduce cognitive load, thereby improving work performance. CEDT encompasses a range of tools and systems, such as adaptive interfaces, decision support systems, and brain-computer interfaces, which are tailored to meet the cognitive needs of workers. By integrating CEDT into work systems, organizations can create environments that are more conducive to the capabilities of ageing workers, ultimately enhancing productivity and reducing workload.

As the workforce ages, the impact of ageing on work performance becomes a critical issue. Ageing is associated with declines in cognitive and physical capabilities, which can lead to decreased efficiency, accuracy, and overall productivity.^17,18 These changes pose significant challenges for both workers and employers, as maintaining high levels of performance becomes increasingly difficult with age. Moreover, the traditional work systems and environments may not be well-suited to accommodate the needs of older workers, further exacerbating the decline in performance.^19–22

The implementation of CEDT presents a potential solution to these challenges.²³ However, the integration of such technologies into existing work systems is not without its difficulties. Barriers to the adoption of CEDT include technological complexity, cost, and resistance to change from both workers and management.²⁴ Additionally, the effectiveness of CEDT in enhancing work performance and reducing workload among ageing workers has not been extensively studied, leading to uncertainty about its potential benefits.

Given these challenges, there is a pressing need for research that systematically examines the impact of CEDT on work performance and workload levels in ageing workers. Despite the theoretical promise of CEDT, there is a lack of comprehensive studies that evaluate its practical effectiveness in real-world settings.²⁵ This research aims to fill this gap by providing empirical evidence on the outcomes of CEDT interventions for older workers, thereby contributing to the broader understanding of how to best support this growing segment of the workforce.

This study will make a significant contribution to the fields of ergonomics, cognitive psychology, and work system design by providing new insights into how CEDT can enhance the work performance and well-being of ageing workers. By investigating the effects of CEDT on both cognitive workload and physiological, this research will help to clarify the mechanisms through which these technologies operate, offering valuable guidance for the design of more effective work environments.

The findings of this study have important practical applications for organizations looking to optimize their work systems for an ageing workforce. The results will inform the development of interventions that are specifically tailored to the cognitive and physical needs of older workers, thereby improving their productivity and job satisfaction. Moreover, the study will provide evidence-based recommendations for the implementation of CEDT, helping organizations to overcome the barriers in adoption and maximize the benefits of these technologies.

Ageing workforce: challenges and adaptation

Aged workers experience a decline in cognitive functions such as memory, attention, and processing speed, which can negatively impact their work performance. The literature highlights that these cognitive declines can lead to slower decision-making, increased errors, and difficulty in adapting to new tasks or technologies.^26–28 In addition to cognitive decline, ageing workers are also more susceptible to high workload, which can further impair their performance. High workload in older workers is often exacerbated by the demands of maintaining productivity in environments that may not be designed to accommodate their needs.^29–31

Cognitive ergonomics

Cognitive ergonomics is a sub-discipline of ergonomics that focuses on the optimization of cognitive work, including processes such as perception, memory, reasoning, and motor response.⁶ CEDT have been designed to support cognitive functions, reduce mental workload, and improve decision-making processes.²⁴

Despite the advancements in ergonomic interventions and technologies, there remain significant gaps in the literature, particularly regarding the effectiveness of CEDT interventions for ageing workers. This section identifies these gaps, highlighting the need for more empirical studies that evaluate the long-term impact of CEDT on work performance and workload. The discussion addresses the methodological challenges in studying this population and suggest areas for future research.

Method

The experiment aimed to assess the impact of cognitive ergonomic-driven technology on mental workload (MWL) and work performance. Participants completed two task document preparation and digitization (task I) and arithmetic task (task II) under segment A: without the intervention of CEDT and segment B: with the intervention of CEDT. Key measures included heart rate variability (HRV), average beats per minute (BPM), and cognitive engagement ratios, with tasks varying in complexity and frequency to observe their effects on MWL and performance.

Experiment framework

Accomplishing the experiment's objective, a series of methodologies were employed. Initially, subjects were prepared following a data acquisition protocol involving electroencephalogram (EEG) device from Neurosoft, NASA Task Load Index (TLX), Actiheart and a video recorder. EEG signal processing utilized both onboard and digital devices to clean the data, while Actiheart was used to monitor physiological strain and cardiovascular health. Subsequently, feature extraction was performed, and power ratios were calculated from the processed EEG signals. The perceived workload was assessed using the NASA TLX. The experiment incorporated between two segments with and without the intervention CEDT, and consist of two tasks in each segment. Figure 1 presents the block diagram outlining the experimental procedure.

Figure 1.

Block diagram outlining the experimental procedure.

Participants preparation

To qualify as participants, individuals must meet several criteria. Participants should have at least five years of working experience, normal to corrected-to-normal vision, and computer literacy. Participants were selected based on specific criteria, including computer literacy skills, as these are necessary for engaging with the study task. Participants with less than five years of work experience, a body mass index (BMI) outside the normal range, have heart-related diseases/injuries or have musculoskeletal injuries are excluded. Additionally, individuals with cognitive mental health conditions such as depression or anxiety are excluded, as these factors could affect their ability to perform the task and introduce confounding variables that may impact the study results.

The targeted age for participants is 50 to 65 years, as this demographic is of the particular interest. All subjects are between the age of 50 to 65 years (56.43 on average) and office workers. The study involved 30 participants, with half being managerial staff and the other half being supporting staff (Figure 2). This study was approved by this Ethics Committee for Research Involving Human Subjects (JKEUPM), Ref.no UPM/TNCPI/RMC/JKEUPM/1.4.18.2 (JKEUPM-2022-911). Figure 3 show the top view on placement of participant during experiment be carry out. Placement of the device called Actiheart is as illustrated in Figure 4.

Figure 2.

Participants demographic distribution.

Figure 3.

Top view of experimental design.

Figure 4.

Actiheart sensor placement on body.

Figure 5.

10–20 system of electrode placement. Red circle denotes the electrode position used in this experiment. A1 and A2 used as the reference points.

Time-based work task segmentation

Time based work task is used as stimuli in this study. Based on the study design, time-based work task was divided into two segments. Segment A: without the intervention of CEDT and Segment B: with the intervention of CEDT. Time based work task consists of two task document preparation and digitization (task I) and arithmetic task (task II) as illustrated in Table 1.

Task I: Document Preparation and Digitization was chosen to assess participants’ efficiency in document-related tasks, which are essential skills in office environments. This task includes typing, as well as additional processes such as scanning, and editing when using CEDT. The task evaluates the ability to rapidly and accurately prepare digital documents, integrating skills such as fine motor coordination, cognitive processing, and adaptability to technology. Participants are required to perform tasks under time constraints, measuring their proficiency in both traditional typing and advanced document handling. This task reflects common office activities, making it a suitable measure of functional performance in modern workplace settings.

Task execution: Participants typed a 1000-word article in Microsoft Word.³² The Task I will involve several cognitive processes, including phonological long-term memory, orthographic long-term memory, the semantic system, orthographic working memory, and graphic-motor planning processes.³³ The Task l will be timed for 10 min and the participants’ typing speed and accuracy will be measured.

Task II: The arithmetic task evaluates the participants’ basic mathematical skills and mental computation abilities. It involves solving a series of numerical problems, such as addition, subtraction, multiplication, and division, within a limited time frame. This task was chosen because it requires cognitive processes like numerical reasoning, memory recall, and attention to detail. These skills are essential for various office tasks that involve financial calculations, data analysis, and decision-making. By including arithmetic tasks, the study aims to assess the cognitive load and accuracy of ageing workers in performing essential quantitative tasks commonly encountered in the workplace.

Task execution: Task II consists of 400 simple math problem (addition, subtraction, multiplication and division).³⁴ Participants need to answer as many as they can under 10 min without using any help from CEDT in segment A.

CEDT intervention: In segment B participant are instructed to use CEDT to ease both their work task. Article given in each segment were completely random and different for each segment. This restriction was added as to overcome the error of participants memorizing the article as typing the memories words is easier than typing random words.²⁴ CEDT consist of Optical Character Recognition (OCR) - Photomath and Natural Linguistic Processing (NLP) – Microsoft lens.

The experiment simulated real work at selected workstation to measure the desired variables. Task and work were determined based on surveys, interview, and observations, and were conducted in two segments: with CEDT and without CEDT. The experiment took place in the Ergonomic Lab at Malaysian Research Institute on Ageing (MyAgeing^TM). The experimental setup was as illustrated below.

Performance metric

Crafted time base work task in this study was assess in accuracy, efficiency and performance. Accuracy in task II was determined by the ratio of correct answers to total answers, providing a measure of precision in responses. For task I, accuracy was calculated using equations (1) and (2). The net word per minute (WPM) considered all typed entries, divided by 5 (average word length), minus uncorrected errors, divided by the time in minutes. Gross WPM was calculated similarly but without subtracting errors. The accuracy percentage was derived from the net WPM divided by gross WPM and then multiplied by 100, offering a quantitative measure of typing precision. Results were converted into percentages for easy comparison, with higher percentages indicating greater accuracy. Accuracy was calculated by using the formula below.

T a s k I A c c u r a c y = (n e t W P M / g r o s s W P M) * 100 = a c c u r a c y %

(1)

\begin{aligned} T a s k I I A c c u r a c y = & (c o r r e c t a n s w e r / t o t a l a n s w e r e d \\ q u e s t i o n s) * 100 \\ = & a c c u r a c y % \end{aligned}

(2)

Note: Net WPM = ((all typed entries/5) – uncorrected error)/Time (min)

Gross wpm = (all typed entries/5/ Time (min)Efficiency is assessed by participants’ productivity in completing tasks. For task I, efficiency score was calculated by considering all typed entries, divided by 5 to estimate words, minus uncorrected errors, divided by time in minutes, yielding the number of correct answers per minute. This approach balanced speed and accuracy, providing a measure of effective typing speed. For task II, efficiency was defined as the number of correct answers per minute, reflecting participants’ ability to solve arithmetic problems quickly and accurately. Efficiency score was calculated by using the formula (3) and (4).

\begin{aligned} T a s k I E f f i c i e n c y = (((a l l t y p e d e n t r i e s / 5) – u n c o r r e c t e d \\ e r r o r)) / T i m e (m i n) \\ = c o r r e c t a n s w e r p e r m i n u t e \end{aligned}

(3)

T a s k I I E f f i c i e n c y = (c o r r e c t a n s w e r) / T i m e (m i n) = c o r r e c t a n s w e r p e r m i n u t e

(4)

Performance Score (PS) was measured by combining accuracy and efficiency score for both tasks. This comprehensive approach offered a well-rounded evaluation of participants’ abilities, considering the correctness and speed of task completion for both task each segment.

P S = (a c c u r a c y / 100) * e f f i c i e n c y

(5)

The Total Performance Score (TPS) of segment A and B was evaluated by calculating both performance Task I and II. The TPS was then calculated using the formula below.

T P S = (T a s k I P S + T a s k I I P S) / 2

(6)

where “Task I PS” is the performance score of tasks I and “Task II PS’ is the performance score of task ll.

The results were analyzed for each task in each segment, providing a detailed understanding of participants’ performance.

Actiheart data acquisition

Actiheart sensor was utilized by attaching to the participants’ chest near the sternum, which allows for optimal ECG signal acquisition and accurate tracking of heart rate (HR) and heart rate variability (HRV).³⁵ The device employs a single-channel electrocardiogram (ECG) and an accelerometer to capture these metrics. Proper electrode placement is critical for accurate readings. The primary electrode was positioned on the left side of the sternum (V1), just below the pectoral muscle, while a reference electrode was placed on the right side near the clavicle (V2). Participants were instructed to ensure clean and dry skin before attachment to enhance electrode contact and signal quality. The device was secured with an adhesive patch, and participants were advised to avoid heavy sweating or water exposure to maintain electrode adherence throughout the monitoring period.

Perceived workload data acquisition using NASA task load index

NASA TLX is a widely used subjective assessment tool developed by the Human Performance Group at NASA's Ames Research Center. It is designed to measure perceived workload and assess the effectiveness of tasks, systems, or teams.³⁶ In this study, NASA TLX measured after each task in segment to record the workload level of the participants after and before interventions. NASA TLX via mobile apps used in this study as it is more suitable comparing to printed NASA TLX. This apps provide an automation calculation algorithm that determine all the perceived workload variables including overall weighted workload (OWWL).^37,38 Data can be extracted in form of excel document from mobile. Apps can be installed from Google plays store or Apple apps store. The OWWL was categorized and colour coded to ease data analyse and presentation as shown in Table 2.

Table 1.

Experimental work segment.

Descriptions	Segment A	Segment B
Tasks	Task I: Document Preparation and Digitization (Document Typing) Task II: Arithmetic (Simple Math Questions)
Duration	10 min (Task l) + 3 min (rest) + 10 min (Task II)
Performance Measure	Efficiency Accuracy
Cognitive Ergonomic Driven Technology (CEDT)	None	CEDT intervention

Table 2.

Overall weighted workload categorization.

Workload	Value	Colour code
Low	0–9
Medium	10–29
Comparatively High	30–49
High	50–79
Very High	80–100

Electroencephalograph data acquisition

The Neuron-Spectron-4p equipment (Neurosoft) was used to acquire EEG signals. An elastic EEG cap (Tenecom) wired with 13 Ag/AgCl electrodes, comprising 10 EEG electrodes, two reference point and one grounding, was utilized to acquire EEG data (opposite lateral mastoid).^39–41 Two electrodes were place for EOG to be used as noise elimination in data extraction. An adapted international 10–20 system was used to put the EEG electrodes. All electrodes and the skin had a contact impedance of less than 20 kΩ and polarization less than 300 mV. The EEG recordings were amplified and digitized at 500 Hz using Neuron-Spectrum.NET. Figure 5 show the placement of electrode used in recording EEG signal during the experiment.

The data acquisition process was divided in to four parts corresponding to each task in each segment – Task I and Task II in both Segment A and Segment B. Baseline reading were recorded three minutes before the first task began. Before each task, participants were required to rest for three minutes, during which data was recorded. During this resting state, participants were asked to close their eyes and sit comfortably to induce a relaxed brain state. The Neuron-Spectrum.Net software was utilized for EEG processing and artifact removal. The workflow for data acquisition in this experiment is illustrated in Figure 6. Figure 7 show the brain signal of ageing workers during task without CEDT intervention based on EEG point, observed with BAR. This graph be segmented by electrode point.

Figure 6.

Electroencephalograph data acquisition workflow.

Figure 7.

Brain signal of ageing workers during task without CEDT intervention based on EEG point, observed with BAR.

In this study spectral EEG analysis utilizes the fast Fourier transform (FFT) to convert the recorded signal from the time domain to the frequency domain. This frequency-based representation is a fundamental aspect of modern mathematical EEG analysis. Neuron-Spectrum. NET provides an automatic calculation algorithm that determines the appropriate epoch length for FFT based on the duration of the analysis epoch. This software also automates the calculation of the power spectral density (PSD), facilitating efficient and accurate spectral analysis of the EEG.

For rhythm power ratio calculations, the ratio is determined by dividing the absolute power of one frequency band by that of another, using the averaged PSD for each frequency band. The Delta frequency band, typically indicative of deep rest, was not analysed under strain conditions due to its expected low activity. The Beta/Alpha (BAR) power ratio was calculated by dividing the PSD of Beta waves by the PSD of Alpha waves. BAR was used to measure the cognitive workload of the participants before and after the intervention. This ratio was then compared between baseline and workload sessions. The workflow for data collection, signal pre-processing, feature extraction, and power ratio computation is illustrated in Figure 1.

Data analysis

Analysis was performed using IBM Statistical Package for the Social Sciences (SPSS) Statistic 27 (IBM, Armonk, NY, USA). The distribution data were examined for normality. Changes of CEDT intervention are describe by using percentage different, in which positive and negative value show the direction of the changes, (+) increment and (-) decrement. Differences in mean and ratios between groups were tested with Wilcoxon signed rank test respectively as appropriate. Perceived workload (NASA TLX - OWWL) was categories into low until very high and color coded as tabulate on Figure 8 to help in data presentation. Pearson's correlation between workload metrics and work performance level were tested to look on which metrics that significantly effecting the performance level, and the OWWL and performance level also tested with Pearson's correlation to know the relationship between these parameters.

Figure 8.

Brain signal of ageing workers during task with CEDT intervention based on EEG point, observed with BAR.

Result and discussion

This section presents the findings from the study examining the impact of CEDT ON the work performance and workload levels of ageing workers. The results are categorized into performance metrics, followed by indicators related to workload, and physiological indicators. The findings provide insight into the effectiveness of CEDT in enhancing productivity and reducing workload among the ageing workforce.

Performance metric

Tables 3 summarize the performance metrics during the tasks. In Segment A, the efficiency, accuracy, and PS values for managerial staff were lower than those for supporting staff. In Segment B, both groups demonstrated higher performance indicate an increase in performance with CEDT intervention in segment B.

Table 3.

Performance metrics during task I and task II of segment A and B.

Task	Segment	Type of ageing staff	Efficiency score	Accuracy (%)	Performance score (PS)
1	A	Managerial	27.43	95.60	26.62
	A	Supporting	28.78	97.35	27.97
	B	Managerial	141.54	96.63	137.03
	B	Supporting	140.49	96.67	135.94
2	A	Managerial	12.96	96.27	12.58
	A	Supporting	10.19	82.61	9.45
	B	Managerial	13.72	97.97	13.45
	B	Supporting	11.76	97.50	11.53

Table 4 highlights the change in performance with and without CEDT intervention. The data shows a significant increase in PS across both segments and tasks for both managerial and supporting staff. Managerial staff in Segment A shown a 134.93% increase in PS during Task I and a 6.75% increase during Task II. Similarly, supporting staff in Segment A shown a 131.75% increase in Task I and a 19.91% increase in Task II. These findings demonstrate the substantial positive impact of CEDT on performance, particularly during the more complex tasks.

Table 4.

Change of performance with and without intervention of CEDT.

Type of ageing staff	Segment	Performance score
Type of ageing staff	Segment	Task I	Task II	TPS
Managerial	A	26.62	12.58	19.60
Managerial	B	137.03	13.45	75.24
Supporting	A	27.97	9.45	18.71
Supporting	B	135.94	11.53	73.74

The PS and TPS metrics clearly indicate work performance improves with CEDT. Both managerial and supporting staff showed higher PS and TPS values post-intervention, with Segment B demonstrating the most substantial improvement. For managerial staff, the significant increase in PS and TPS after CEDT implementation suggests that the technology effectively enhances their ability to complete tasks more efficiently and accurately.^42,43 This could be attributed to CEDT's ability to reduce cognitive load and automate routine tasks, allowing managerial staff to focus more on decision-making and oversight, which are critical aspects of their roles.^17,44

Supporting staff also showed a marked improvement in performance, though the increase was more pronounced in Segment B. This suggests that the tasks in Segment B were better suited to the capabilities of CEDT, or that the supporting staff were able to utilize the technology more effectively.^45,46 The notable improvement across both groups implies that CEDT can enhance performance universally, although the degree of benefit may vary depending on the nature of the task and the user's role.^16,47,48

Physiological measures actiheart

Heart rate (HR) and heart rate variability (HRV) were used as indicators of physiological strain. Table 5 shows the percentage change in heart rate during tasks, observed in beats per minute (BPM). In Segment A, managerial staff saw a 3.05% reduction in HR, while supporting staff saw a 5.27% reduction. Segment B showed similar reductions, with managerial staff experiencing a 4.62% reduction and supporting staff a 3.90% reduction.

Table 5.

Change of heart rate during task, observed with beat per minute (BPM) and heart rate variability (HRV).

Task	Type of ageing staff	Beat per minute (BPM)			Heart rate variability (HRV)
		Segment		Percentage different %	Segment		Percentage different %
		A	B	Percentage different %	A	B	Percentage different %
1	Managerial	71.29	69.14	3.05	38.70	38.70	−3.54
1	Supporting	74.27	70.45	5.27	58.28	58.28	49.43
2	Managerial	72.50	69.23	4.62	35.00	35.00	−1.53
2	Supporting	73.10	70.30	3.90	48.21	48.21	33.58

The changes in HRV, is indicate the autonomic nervous system activity. Interestingly, HRV showed varying trends. For example, during Task I of Segment A, managerial staff experienced a 3.54% decrease in HRV, while supporting staff saw a 49.43% increase. This trend suggests that while HR decreased with CEDT intervention, the impact on HRV was more variable, potentially indicating individual differences in response.

Heart rate variability (HRV) and heart rate (HR) were used as indicators of physiological strain. The results showed that HR decreased after CEDT intervention, while HRV exhibited more variable responses. A decrease in HR coupled with an increase in HRV typically indicates reduced workload and improved autonomic balance.^38,49,50 However, the mixed HRV results, particularly the significant increase for supporting staff, suggest that while CEDT reduced immediate physical strain, and have induced a different kind of strain, potentially cognitive or emotional, as participants adjusted to the recent technology.^51,52 The higher HR reduction in supporting staff could be due to the more physical nature of their tasks. CEDT might have alleviated some of the physical strain, hence the reduction in HR.^53,54 On the other hand, managerial staff, who might face more cognitive demands, showed less consistent HRV changes, potentially reflecting the cognitive challenges posed by integrating CEDT into their work processes.^55,56

Physiological measures electroencephalograph

EEG was used to measure brain signal through the Beta Alpha ratio (BAR). Table 6 details the changes in brain signal during tasks, observed with BAR. For managerial staff, the BAR increased by 11.84% during Task I and 8.84% during Task II in Segment A. Supporting staff showed an even more pronounced increase, with a 20.46% rise during Task I and a slight 0.59% increase during Task II. These increases suggest a heightened mental workload or cognitive engagement during tasks, likely due to the cognitive demands imposed by the tasks.

Table 6.

Changes in mental state during task observed using BAR.

Task	Type of ageing staff	Segment
Task	Type of ageing staff	A	B
1	Managerial	0.498	0.561
1	Supporting	0.536	0.658
2	Managerial	0.617	0.674
2	Supporting	0.608	0.612

Figures 8 and 9 compare the brain signal of ageing workers during tasks with and without CEDT intervention, based on EEG data (BAR). Without CEDT intervention, the BAR values were lower across all electrode sites, suggesting lower cognitive workload. With CEDT, there was a noticeable increase in BAR, indicating a higher level of cognitive engagement, particularly in the frontal (FP1-A1, FP2-A2) and parietal (P3-A1, P4-A2) regions, which are associated with attention and problem-solving.

Figure 9.

Workload of ageing workers during task without CEDT intervention, based NASA TLX metrics.

The EEG results, particularly the Beta/Alpha Ratio (BAR), provided insights into the mental workload of participants. Higher BAR values indicate higher cognitive workload.⁵⁷ The increase in BAR for both managerial and supporting staff after CEDT intervention suggests that while CEDT reduces physical workload, it might increase cognitive workload, particularly during the initial stages of technology adoption.^58–60 This increase be due to the mental effort required to learn and adapt to the new system.⁴² However, once users become accustomed to CEDT, this cognitive load might decrease, potentially leading to even better performance and lower workload levels. The more pronounced increase in BAR for supporting staff could indicate that while CEDT made their tasks more efficient, it also required them to engage more mentally, due to the need to oversee automated processes or manage new interfaces.^42,61

Subjective mental workload

Mental workload was assessed using the Overall Workload Level (OWWL) metric and NASA TLX scores. Table 7 shows the percentage change in OWWL during tasks. In Segment A, managerial staff experienced a 12.35% increase in OWWL during Task I, but a 24.37% decrease during Task II, leading to an overall decrease of 5.79%. Supporting staff in Segment A also showed a decrease of 2.40% in overall OWWL. Segment B, however, showed a more stable workload, with minor variations across tasks.

Table 7.

Change of workload during task, observed with OWWL.

Type of ageing staff	Segment	OWWL			Percentage different of OWWL %
Type of ageing staff	Segment	Task I	Task II	TPS	Task I	Task II	TPS
Managerial	A	47.42	55.33	51.38	12.35	−24.37	−5.79
Managerial	B	53.67	43.31	48.49	12.35	−24.37	−5.79
Supporting	A	41.96	52.76	47.36	9.10	−12.58	−2.40
Supporting	B	45.96	46.51	46.23	9.10	−12.58	−2.40

Figure 10 present NASA TLX metrics for managerial and supporting staff during tasks without and with CEDT intervention. Without CEDT, the workload was higher across all dimensions (Mental Demand, Physical Demand, Temporal Demand, Performance, Effort, and Frustration). With CEDT, there was a notable reduction in workload, particularly in Mental Demand and Frustration, which are critical factors affecting cognitive strain. For instance, managerial staff showed a Mental Demand score of 46.33 without CEDT, which increased to 50.00 with CEDT during Task I, indicating a shift in perceived workload.

Figure 10.

Workload of ageing workers during task with CEDT intervention, based NASA TLX metrics.

Workload, as measured by the Overall Workload Level (OWWL) and NASA TLX metrics, decreased with the introduction of CEDT, although the results were more refine when broken down by task and segment. For managerial staff, the decrease in workload was more pronounced in Task II, particularly in Segment B. This might suggest that CEDT is more effective in managing complex or time-sensitive tasks, where the cognitive demands are higher.^11,62,63 In these scenarios, CEDT could automate routine processes, allowing managers to allocate more mental resources to strategic decision-making.^12,64

Supporting staff also experienced a decrease in workload, but the effect was more uniform across tasks. This suggests that CEDT is effective in reducing both physical and mental workload for this group, making it a valuable tool for improving work efficiency and reducing fatigue.^65–68 The NASA TLX metrics further support these findings, with significant reductions in Mental Demand and Frustration for both groups. This indicates that CEDT not only reduces the perceived effort required to complete tasks but also mitigates the frustration associated with task complexity and workload.

Comparing means segment before and after by using Wilcoxon test

The Wilcoxon Signed-Rank Test was used to compare performance, workload, HRV, heart rate, and BAR before and after CEDT intervention. Table 8 summarizes the Z-values and p-values for these comparisons. For managerial staff in Task I, performance improved significantly with a Z-value of −3.508 and a p-value of 0.001. Heart rate and BAR also showed significant changes with Z-values of −2.246 (p = 0.025) and −2.333 (p = 0.020), respectively. However, workload and HRV did not show significant changes. For supporting staff, significant improvements were observed in performance, heart rate, and BAR during both Task I and Task II. The Z-value for performance during Task I was −3.408 (p = 0.001), indicating a robust improvement in task performance. Heart rate and BAR also showed significant changes with Z-values of −2.921 (p = 0.003) and −2.357 (p = 0.018), respectively

Table 8.

Wilcoxon test analysis on the intervention of CEDT.

Type of ageing staff	Metric	Z-value	p-value
Managerial	Performance	−3.508*	0.001
	Workload	−0.265	0.791
	HRV	−0.682	0.495
	Heart Rate	−2.246*	0.025
	BAR	−2.333*	0.020
Supporting	Performance	−3.408*	0.001
	Workload	−0.905	0.366
	HRV	−1.874	0.061
	Heart Rate	−2.921*	0.003
	BAR	−2.357*	0.018

*. Correlation is significant at the 0.05 level.

The Wilcoxon Signed-Rank Test results indicated significant improvements in performance, heart rate, and BAR after the CEDT intervention for both managerial and supporting staff. However, the changes in workload and HRV were less consistent. The significant performance improvement, as demonstrated by the Z-values and p-values, confirms that CEDT positively impacts work outcomes.^42,69 The significant reduction in heart rate suggests that CEDT helps alleviate physical strain, which is crucial for sustaining productivity, especially among ageing workers.^55,56

However, the difference in workload and HRV responses between managerial and supporting staff highlight distinct cognitive demands. Managerial roles often involve complex decision making and multitasking, leading to higher cognitive load when integrating CEDT. In contrast, supporting staff roles require more repetitive or task specific functions such as financial and clerk, making the transition to CEDT easier but still posing challenges in procedural adjustments.^6,50,52

These findings suggest that while CEDT is effective in reducing physical strain across the roles, but still the cognitive impact may vary. Customizing the CEDT interface and training programs based in role specific needs could optimize the effectiveness. For managerial staff, this involves advanced decision support features and adaptive interfaces that reduce cognitive overload, and for supporting staff, simplified workflows and step-by-step guidance can enhance usability. With adequate training and role specific support during the transition phase are essential to help both group mange the cognitive demands associated with adopting new technology.

Correlation of NASA task load index and task performance

The correlation between workload metrics (NASA TLX components) and task performance was analyzed for both managerial and supporting staff. Table 9 presents the correlation coefficients.

Table 9.

Correlation analysis of workload metric and work performance.

Type of ageing staff	Task	MD	PD	TD	PR	EF	FR
Managerial	1	0.227	0.176	0.073	0.198	−0.121	0.111
Managerial	2	−0.316	−0.029	0.377	0.309	−.542*	−0.197
Supporting	1	−0.304	−0.219	−0.043	0.290	−0.436	−0.199
Supporting	2	0.097	−0.416	−0.145	−.718**	−0.281	−0.373

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

For managerial staff, a significant negative correlation was observed between Effort Demand (EF) and task performance during Task II (r = −0.542, p < 0.05). This suggests that as the perceived effort required for the task increases, the performance of managerial staff tends to decrease. The negative correlation indicates that higher effort demands may contribute to a decline in performance, likely due to increased fatigue or cognitive overload, underscoring the importance of managing effort levels to optimize task outcomes for managerial staff under Cognitive Ergonomic-Driven Technology (CEDT).^70–72

Supporting staff exhibited a significant negative correlation between Performance Demand (PR) and task performance during Task I (r = −0.718, p < 0.01). This strong negative correlation indicates that as the demands on performance increase, the task performance of supporting staff significantly decreases.^73–75 This may reflect the difficulty supporting staff experience when faced with high-performance expectations, suggesting that CEDT interventions should focus on alleviating performance pressure to improve outcomes for this group.

The significant correlations highlight distinct challenges faced by managerial and supporting staff in relation to specific workload demands. For managerial staff, managing effort is critical to maintaining performance, particularly in complex tasks. For supporting staff, reducing performance pressure is key to enhancing task outcomes. These findings suggest that targeted interventions in CEDT should be customized to address these specific workload demands to improve overall performance and well-being.^76–78

Conclusion

CEDT offers significant benefits for improving the performance and well-being of ageing workers, but its effectiveness can vary depending on the user's role and the specific tasks involved. This study examined the effects of Cognitive Ergonomic-Driven Technology (CEDT) on the work performance, workload levels, and workload of ageing workers, focusing on both managerial and supporting staff. The findings indicate that CEDT significantly enhances work performance, particularly in more complex tasks, and helps reduce physical strain, as evidenced by improvements in heart rate and HRV. However, the technology also increases cognitive demands, as shown by the rise in EEG (BAR) and mixed HRV results, highlighting the need for adequate user training and support.

Workload assessments revealed that CEDT effectively reduces perceived workload, particularly for managerial staff engaged in time-sensitive tasks. The Wilcoxon test confirmed significant improvements in performance and workload level indicators post-intervention, although the adaptation to CEDT may initially introduce new cognitive challenges. CEDT proves beneficial for ageing workers by enhancing performance and reducing cognitive demand, though careful consideration is needed to manage the cognitive challenges it introduces.

By integrating CEDT into workplace strategies, companies can promote a healthier, more productive ageing workforce, potentially reducing healthcare cost and increasing employee preservation. While the result is promising, the study acknowledged limitations such as the small sample size and the short-term nature of the assessment. Practical implementation may face challenges, including potential resistance to technology and cost implications, especially among ageing workers. Future research should explore long-term effects, particularly regarding cognitive fatigue and adaption over time, as well as the customization of CEDT for different job roles.

Footnotes

Acknowledgements

The author(s) would like to express sincere gratitude to National Institute of Occupational Safety and Health (NIOSH) Malaysia and Malaysian Research Institute on Ageing (MyAgeing) for providing access to their facilities and assisting with data collection.

ORCID iD

Muhammad Nazirul Iszat Ismail

Ethical considerations

This study was approved by the University's Ethics Committee [Ethics Code: UPM/TNCPI/RMC/JKEUPM/1.4.18.2 (JKEUPM-2022-911)] on March 23, 2023. All participants provided written informed consent prior to enrolment in the study. This research was conducted ethically in accordance with Research Involving Human Subjects (JKEUPM) guideline.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Malaysia Ministry of Higher Education, (grant number FRGS/1/2022/TK10/UPM/02/7).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Informed consent

The study was approved by the Ethics Committee for Research Involving Human Subjects (JKEUPM) (Ethical Clearance Reference Number: UPM/TNCPI/RMC/JKEUPM/1.4.18.2) on March 23, 2023. All participants provided written informed consent prior to participating.

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