Investigating the relationship between physical,cognitive,and environmental factors of ergonomics with the prevalence of musculoskeletal disorders: A case study in a car-parts manufacturing industry

Abstract

BACKGROUND:

Work-related musculoskeletal disorders (WRMSDs) is a multi-factorial disorder in most occupational setting and it has increased significantly in recent years.

OBJECTIVE:

This study aimed to investigate the relationship between physical, cognitive, and environmental factors of ergonomics with the prevalence of WRMSDs in a car-parts manufacturing industry

METHODS:

This cross-sectional study was performed among 220 workers in a milling unit of a car parts manufacturing company in 2021-2022. The prevalence of WRMSDs was assessed using the Extended Version of the Nordic Musculoskeletal Questionnaire. Noise exposure was evaluated using dosimetry method. Mental and physical workload were evaluated by the NASA-TLX and key index methods (KIM-MHO and KIM-LHC), respectively. Data analysis was performed using SPSS version 25.0.

RESULTS:

The subjects’ mean age and work experience were 36.3±6.5 and 8.35±6.41 years, respectively. Eighty-five percent of the subjects reported WRMSDs in at least one area of the body. The results of mental workload assessment revealed a high workload mean range (73.23±14.89) in all of the subjects. Mean score of KIM-LHC and KIM-MHO were 738.18±336.42 and 201.86±36.41, respectively with odds ratio of 1.32 for KIM-LHC in creating the WRMSDs. There was a significant relationship between the noise exposure, mental and physical workload and the prevalence of WRMSDs (p-value < 0.05).

CONCLUSION:

The results of the present study revealed that environmental, physical and cognitive factors can simultaneously be effective in the prevalence of WRMSDs. Therefore, performing effective control measures requires comprehensive attention to physical, environmental, and cognitive ergonomics in the algorithm of ergonomics management in the workplace.

Keywords

Musculoskeletal disorders noise exposure ergonomic risk factor mental workload physical workload

1 Introduction

Work-related musculoskeletal disorders (WRMSDs) is a multi-factorial disorder which makes it difficult to prevent in workplace [1]. WRMSDs may be caused by cumulative exposure to their contributing factors during a long-term process or suddenly caused by a severe trauma to a part of the musculoskeletal system [2]. Some of the WRMSDs’ symptoms include discomfort, pain, fatigue, dryness, swelling, restriction on range of motion (ROM), muscle cramps, numbness, and tingling [3, 4]. The risk factors for WRMSDs can be divided into four categories: work-related physical or biomechanical factors, work-related organizational or psychological factors, individual factors, and social related factors [5 –7]. The main physical risk factors are manual material handling (MMH), applying force, contact pressure, repetitive movements, vibration, undesirable static postures, and improper work organization [6, 8]. In Iran, musculoskeletal disorders are the main source of disability and related costs. According to available statistics, nearly 48 percent of work-related disorders are cumulative injuries caused by physical factors [4].

The mismatch between the human capability and job demands render the workers to various adverse consequences such as fatigue, decreased job satisfaction, absence from work, decreased physical ability, and reduced job productivity. WRMSDs are one of the most important consequences of this mismatch and are related to the environmental, organizational, psychosocial, physical, individual, and sociocultural risk factors [4 , 10]. Previous studies reported that environmental factors such as excessive noise exposure, poor and non-standard lighting, vibration, improper temperature, and heat strain can increase the risk of WRMSDs [11]. Noise pollution as a important risk factor shows its physiological and psychological adverse effects on human gradually [12]. In addition to noise, previous studies have shown that vibration is also an important risk factor in the physical and mental health of employees, and in addition to accelerating the process of creating WRMSDs, it can also lead to diseases such as cardiovascular diseases. Therefore, evaluation of harmful physical factors such as noise and vibration in the ergonomic assessment process of workstations can be useful [13, 14].

In addition to the main effects of noise on health, such as the effect of occupational exposure to noise on the cardiovascular system [15], aggressive behaviors, physical and mental fatigue, stress, distraction, and reduced long-term productivity are common negative consequences of noise exposure [16]. Also noise exposure above the standard limits can interfere with verbal communication and the perception of warning signs, which can affect the safety and performance of workers, and makes them prone to WRMSDs [17, 18].

Physical factors such as awkward working postures, poor workstation design, repetitive and static work [19, 20], improper manual material handling (MMH), and inadequate recovery time can also increase the risk of WRMSDs [20 –22]. Therefore, to achieve health, safety, and comfort in the workplace and to increase the productivity and efficiency of people in the long run, the reasonable strategy is to balance the job demands to individual’s capabilities [23].

Another important occupational risk factors are psycho-social factors such as job stress, psychological demands, low decision-making ability, low social support, job dissatisfaction, mental workload and effort-reward imbalance [19, 22]. Workload has a multidimensional definition. The amount of mental work is the amount of effort that a person’s mind endures while doing work and is related to the person’s mental abilities and capacities and how to receive, understand and process information. All these processes can finally lead to the decision to do the work [24]. Mental workload is directly related to individual performance and affects worker’s health. Increased workload can reduce the operator awareness, increase the human error and consequently reduce the performance level [4]. Along with high mental workload, physiological changes such as increased heart rate, psychosocial effects such as excitement, and behavioral effects such as increased human error rates are common [25].

Previous researches have shown that one-dimensional evaluations in ergonomics assessment (mainly physical ergonomics) are not sufficient and effective interventions in workplace ergonomics require comprehensive attention to all components of ergonomics (physical, environmental, cognitive, and organizational) [26].

Ergonomics is a multidisciplinary field that focuses on optimizing the interaction between humans and their work environment. Simultaneous evaluation of the effect of the most important ergonomic risks in three physical, cognitive and environmental components can lead to better planning for prioritizing the implementation of control measures with a proactive and preventive approach.

So far, many studies have been conducted on the effective risk factors in the prevalence of WRMSDs in various industries, but the simultaneous and interactive effect of different physical, environmental and cognitive risk factors has been less studied [27]. Therefore, it is very important to identify the most important parameters and risk factors in each of the physical, environmental and cognitive ergonomics components and to investigate their simultaneous relationship with the prevalence of WRMSDs in different industries. In Iran, the automotive industry is one of the biggest and most important industries, in which a high percentage of the workforce is employed.

WRMSDs are a significant concern in the workplace, particularly in physically demanding industries such as car-part manufacturing. In the car-part manufacturing industry, where workers are exposed to physically demanding tasks and noisy environments, the risk of developing WRMSDs is a major concern. However, recent research has also highlighted the potential impact of physical and mental workload and noise exposure on the development of WRMSDs. In this paper, we present a case study investigating the relationship between mental and physical workload, noise exposure, and the prevalence of WRMSDs in a car-parts manufacturing industry. By examining these factors and their potential interactions, we aim to gain a better understanding of the underlying causes of WRMSDs in this specific setting and provide insights for potential interventions to reduce their occurrence. This study contributes to the growing body of research on the multifaceted nature of WRMSDs and highlights the need for a comprehensive approach to address this complex issue in the workplace.

Since in the automotive industry and among the various units of the car-parts manufacturing companies, the milling unit is at a high-risk level of WRMSDs according to the work processes and the nature of the work, this study aimed to investigate the relationship between physical, environmental, and cognitive components of ergonomics with the prevalence of WRMSDs among workers of a car-parts manufacturing company.

2 Methods

2.1 Study design

In this cross-sectional study 220 workers of the CNC milling unit of a car-parts manufacturing company were studied during 2021-2022. The sample size was 220 workers and determined by applying the Cochran formula with a 0.05 error level (5% error), and test power of 80% (1-β= 0.80). The sample size was selected from all personnel by using simple random sampling method. All participants in the present study were male.

Inclusion criteria included at least one year of work experience and age range of 20–50 years (appropriate age for occupational activity). Exclusion criteria was defined as history of systemic diseases of the musculoskeletal system such as history of upper, lower limb and spine surgery, history of spine or hip fractures, osteoporosis, pregnancy, psychosomatic disease, history of the prolonged systemic use of corticosteroids, change of job activity during the study, and lack of enough consent to participate in the study. Before conducting the study and completing the questionnaires, a training class was designed to justify the participants about the purpose of conducting research to maximize their participation and all the information needed for completing the questionnaires was provided to the individuals. Participants were able to withdraw from the study if they were not satisfied at any stage. All the selected people remained in the study until the end of the research and the response rate in the present study was 100%. The ethics committee of the University of Social Welfare and Rehabilitation Sciences approved the methodology of the study.

Demographic data were obtained using a self-administrated questionnaire and to extract main variables, the following questionnaires were used.

2.2 Data collection

2.2.1 Preliminary study

To control the inclusion criteria, the anxiety, depression, sleep and social interaction disorders were evaluated through the General Health Questionnaire (GHQ) that was developed by Goldberg et al. [28]. GHQ have 28 questions and four subscales including physical symptoms, anxiety, social dysfunction, and depression [29, 30]. The final score of each person’s health level is obtained from the sum of the four subscales score.

The question options are scored using the simple Likert method (0, 1, 2, and 3). A final score of 23 or higher indicates a lack of health, and a score below 23 indicates general health. The Persian version of the GHQ has shown good reliability with Cronbach’s alpha coefficient of 0.94 and its structural validity was approved by previous study [31].

2.2.2 Investigating the prevalence of WRMSDs

The Extended Nordic Musculoskeletal Questionnaire (ENMQ) was used to evalute the prevalence of WRMSDs. Nine body regions including neck, shoulders, upper back, elbows, wrists, waist, hips or thighs, knees, and ankles are examined in this questionnaire. Moreover, severity and duration of the pain are investigated in this tool. The reliability and validity of this questionnaire (ICC > 0.70) has been confirmed by Mokhtarinia et al. [32].

2.2.3 Noise measurement

Occupational exposure of workers to noise was measured through dosimetry method. This method is the simplest and most reliable method of measuring the occupational exposure to noise, because the receiver is connected to the nearest area of person’s hearing system, and records the amount of dose received by the person during the work shift. Dosimetry allows recording the noise intensity level with all changes in real time [33]. It is noteworthy that a dosimeter gives results in terms of percentage [34].

The dose received by the individual was calculated using the following equation: $Dose (%) = \frac{t_{i}}{t_{a}} \times 100$ (1)

t_i : Sound exposure time (hour)

t_a: Permissible noise exposure time according to ambient sound pressure level (hour)

Since we have different noise pressure levels over time during noise measurement in work environments, the equivalent noise exposure level (Leq) was first calculated. Then the dose received was extracted [34].

The equivalent noise exposure level (Leq) was calculated using the following equation: $Leq (8) = 10 Log [\frac{1}{8} \sum_{i = 1}^{n} 10^{{L_{P}}_{i} / 10} \times t_{i}]$ (2)

Leq: equivalent exposure level (dB)

t_i: exposure time (hour)

T: work shift time (8 hours)

LP_i: noise pressure level (dB)

The relationship between the received dose (in percentage) and the sound pressure level (SPL) is also given below: $D (%) = 12.5 \sum t_{i} antilog [({SPL}_{i} - 85) / 10]$ (3)

t_i: exposure time (hour)

SPL: Sound pressure level at a specific time (dB)

It should be noted that the standard occupational exposure level for 8-hours of daily work is 85 dB in Iran [35]. In this study, the level of occupational exposure to noise followed a specific pattern (exposure to a certain sound pressure level at regular times). During the present study, the short-term dosimetry method (30 minutes) and the ST-130 dosimeter made by Tenmars company, TAIWAN, calibrated using the TES-1356 calibrator was applied to measure each person’s exposure to ambient noise. For this purpose, the microphone was installed on the worker’s collar (maximum distance of 30 cm from the ear), and the dosimeter device was attached to his belt. At the end of the measurement period, the dose percentage displayed on the device screen was read, and the dose percentage equal to 8-hours was recorded for each person [36].

2.2.4 Mental workload assessment

This tool was applied to assess mental workload. The NASA-TLX is a multidimensional tool that provides an overall score of mental workload based on a weighted average of six scales of intellectual and mental demand, physical demand, temporal demand, effort, overall performance, and frustration level. Except for the performance item, which is evaluated between “very good” and “unsatisfactory” levels, the other items are evaluated in the “very low” to “very high” range [37]. Each individual evaluated each dimension on an axis with 1-point steps from 0 to 21. In the second part, the load rate of each dimension is calculated. The final mental workload score was calculated using the following equation [38]. $final score = \frac{\sum load rate \times load weight}{15}$ (4)

According to the questionnaire approach, if the overall workload score is less than 50, the risk level is low and if it is above 50 the risk level is high. The face validity and reliability of the scales (Cronbach’s alpha coefficient = 0.86) has been approved in Persian population [4].

2.2.5 Physical workload assessment

In the present study, the key index method (KIM) was used to evaluate the physical workload. KIM was developed by the German Federal Institute for Occupational Safety and Health (BAuA) [39, 40]. Different versions of the KIM for different tasks are provided. We used KIM-MHO and KIM-LHC which are used for manual handling operation and lifting, holding, and carrying tasks, respectively [40].

Since the process of miling unit was manual, workers manually moved the load and the manufactured parts. To assess the physical workload and determine the workers’ risk level in this unit, KIM-MHO and KIM-LHC assessment methods were used.

The components of physical workload that have been evaluated in the KIM-MHO method include the following items:

The force applied by the hands and fingers based on the force level in this area, the average time of holding the load (seconds and minutes) and the average number of lifting times (number of liftings per minute)

The miling situation

Hand and arm position

Working conditions

Body posture

Work organization conditions

The components of physical workload evaluated in the KIM-LHC method also include the following items:

The number of manual material handling during each work shift

The load weight

Manual material handling condition

Body posture condition

Unfavorable working conditions

Work organization conditions

The final calculated score, define the risk level of physical workload based on the risk table. Accordingly, the score below 20 indicate the low physical load, and more than 100 indicate high physical load [40, 41].

2.3 Data analysis

Deascriptive analysis were performed to present mean, standard deviation, and frequency percentage. To test the normality of data the Kolmogorov-Smirnov test was used. The comparison between the qualitative and quantitative variables were performed by Chi-square and Mann-Whitney U tests, respectively. To evaluate the correlation between variables the Spearman correlation coefficient was conducted and to evaluate the effect of studied parameters on the prevalence of WRMSDs the logistic regression method was applied. Data anylysis was done using SPSS software version 25.0, and all significant levels were set at 0.05 level.

3 Results

3.1 Demographic information of the subjects

Descriptive results showed that the subjects’ mean age, work experience, and body mass index (BMI) were 36.3±6.51 years, 8.35±6.41 years, and 25.59±4.08 kg/m², respectively. The results of the assessment of WRMSDs showed that 120 workers had musculoskeletal disorders in at least one of their organs, and 100 workers did not have any of these disorders. A comparison of demographic characteristics of participants showed that the mean age, work experience, body mass index, and general health level in workers with WRMSDs were more than those without these disorders, and there was a significant difference between the values of the mentioned parameters and the prevalence of WRMSDs (p-value < 0.05). In addition, workers without WRMSDs had more history of sports activity than participants with these disorders. It should be noted that the rate of smoking among workers with WRMSDs was significantly higher (p-value < 0.05). Other demographic characteristics according to the presence or absence of WRMSDs among the subjects are presented in Table 1.

Table 1
Demographic characteristics of the participants (n = 220)

Variable (Unit) Prevalence of WRMSDs p-value

M±SD / Percentage

With WRMSDs Without

(n = 120) WRMSDs (n = 100)

Age (year) 36.87±6.6 33.05±5.03 0.028*

Work experience (year) 8.72±6.59 6.27±4.95 0.035*

Body mass index (BMI) (kg/m²) 25.83±4.08 24.2±3.95 0.041*

Smoking (%) Yes 35.3 27.8 0.301

No 64.7 72.2

Marital status (%) Single 13.7 22.2 0.863

Married 86.3 77.8

General health score 13.72±5.11 12.41±4.32 0.039*

Sport activity (%) Yes 55.9 63 0.033

No 44.1 37

Education level (%) Middle School 41.4 43.1 0.437

High School 55.6 55.9

Associate Degree 3 0

Bachelor’s Degree 0 0

Master’s Degree 0 1

Variable (Unit)		Prevalence of WRMSDs	p-value
Age (year)		36.87±6.6	33.05±5.03	0.028*
Work experience (year)		8.72±6.59	6.27±4.95	0.035*
Body mass index (BMI) (kg/m²)		25.83±4.08	24.2±3.95	0.041*
Smoking (%)	Yes	35.3	27.8	0.301**
	No	64.7	72.2
Marital status (%)	Single	13.7	22.2	0.863**
	Married	86.3	77.8
General health score		13.72±5.11	12.41±4.32	0.039*
Sport activity (%)	Yes	55.9	63	0.033**
	No	44.1	37
Education level (%)	Middle School	41.4	43.1	0.437**
	High School	55.6	55.9
	Associate Degree	3	0
	Bachelor’s Degree	0	0
	Master’s Degree	0	1

*Mann-Whitney U test; **Chi-square test.

3.2 Prevalence of WRMSDs

Based on the results obtained from the evaluation of the prevalence of WRMSDs using the Nordic questionnaire, it was determined that the prevalence of WRMSDs in the neck, shoulder, upper back, elbow, wrist/hand, waist, hip/thigh, knee and ankle/foot areas were 65.3, 63.9, 50.4, 54.2, 58.3, 68.6, 35.4, 51.8, and 30.8 percent, respectively. The highest lifetime prevalence of WRMSDs were in the lumbar, neck, and shoulder organs, and the lowest prevalence were obtained in the ankle and pelvis/thigh.

3.3 Noise exposure level

Evaluation of the amount of noise dose received by the studied workers using the dosimetry method for 8-hour shift work showed a very high noise exposure (789.17±447.95%). The obtained value was higher than the acceptable standard value in Iran (100 % dose equivalent to 85 dB). The calculated noise exposure dose among two groups of subjects with and without WRMSDs were 777.8±454.06 and 769.63±492.75%, respectively. It seems that the subjects with the history of WRMSDs were significantly exposed to noise higher than workers without WRMSDs (p-value < 0.05). A significant correlation was found between noise exposure and the prevalence of WRMSDs in the neck, shoulders, and wrists regions (Table 2).

Table 2
Correlation values between noise exposure and the prevalence of WRMSDs

8-hour equivalent dose percent Prevalence of WRMSDs (body region)

Neck Shoulder Upper back Elbow Wrist Waist Pelvis/thigh Knee Ankle

p-value

0.027^* 0.034^* 0.352 0.375 0.045^* 0.575 0.806 0.635 0.930

Spearman correlation coefficient

0.487 0.362 0.196 0.172 0.474 0.238 0.165 0.217 0.194

*Significant correlation (p-value < 0.05).

3.4 Mental workload level

The results of mental workload assessment revealed a high workload mean range (73.23±14.89) in all of the subjects. Among the six subscales of NASA TLX, the highest and lowest scores were related to the physical demand (88.98±17.35) and the performance (30.1±34.97), respectively. The mental workload in subjects with and without WRMSDs were 77.84±12.9 and 72.27±15.11, respectively. Also, the findings showed that all subscales of mental workload were higher in the workers with WRMSDs, except for the performance subscale, which was higher in workers without WRMSD (Table 3).

Table 3
Evaluation of mental workload among the participants

Mental workload component Prevalence of WRMSDs p-value*

M±SD

Without WRMSDs With WRMSDs

(n = 100) (n = 120)

Mental demand 70.37±32.03 75.29±34.66 0.034^

Physical demand 88.09±18.61 93.82±28.08 0.048^

Temporal demand 81.67±24.36 84.71±23.41 0.026^

Effort 87.16±19.84 91.24±27.6 0.039^

Performance 32.35±34.37 29.63±35.29 0.023^

Frustration 49.63±36.27 52.65±40.62 0.216

Total mental workload score 72.27±15.11 77.84±12.9 0.041^

Mental workload component	Prevalence of WRMSDs	p-value*
Mental demand	70.37±32.03	75.29±34.66	0.034^**
Physical demand	88.09±18.61	93.82±28.08	0.048^**
Temporal demand	81.67±24.36	84.71±23.41	0.026^**
Effort	87.16±19.84	91.24±27.6	0.039^**
Performance	32.35±34.37	29.63±35.29	0.023^**
Frustration	49.63±36.27	52.65±40.62	0.216
Total mental workload score	72.27±15.11	77.84±12.9	0.041^**

*Mann-Whitney U test; **Significant difference (p-value < 0.05).

The Spearman test analysis showed a positive significant association between the total mental workload, its subscales except the frustration (r = 0.37, p-value = 0.437) and the prevalence of WRMSDs. Spearman correlation coefficients and related p-values between prevalence of WRMSDs and dimensions of mental workload, including mental demand, physical demand, temporal demand, effort, performance, frustration and total score of mental workload were (r = 0.76, p-value = 0.047), (r = 0.81, p-value = 0.43), (r = 0.79, p-value = 0.039), (r = 0.84, p-value = 0.026), (r = –0.72, p-value = 0.035), (r = 0.37, p-value = 0.473) and (r = 0.78, p-value = 0.041), respectively.

3.5 Physical workload level

The average final score of physical workload using the KIM - MHO and KIM - LHC were 201.86±36.41 and 738.18±336.42, respectively, which indicating a high-risk level. To compare the physical workload among the participants according to prevalence of WRMSDs, the Mann-Whitney test was applied. The findings demonstrated that the final scores of physical workload in the KIM-MHO and KIM-LHC methods, were significantly different between two groups (Table 4 and 5).

Table 4
Evaluation of physical workload based on the KIM-MHO method among the participants

KIM-MHO physical workload components Prevalence of WRMSDs p-value

M±SD / Percentage

Without WRMSDs With WRMSDs

(n = 100) (n = 120)

Level of force applied to the Moderate 85.7 42.6 0.039**

hand/ arm area (%) High 14.3 47.9

Very high 0 9.5

The score of force applied to the hand/ arm area 36.87±6.6 20.46±5.73 0.021*

The gripping situation (%) Limited gripping 76.2 85.7 0.046

Difficult gripping 23.8 14.3

Hand and arm position (%) Limited 40.5 26.8 0.032

Undesirable 59.5 71.4

Working conditions (%) Desirable 0 0 0.251

Undesirable 100 100

Body posture (%) Limited 45.2 28.6 0.041**

Undesirable 54.8 71.4

Work organization conditions (%) Desirable 0 0 0.126

Undesirable 100 100

The final score of the physical workload 201.79±35.93 202.29±42.28 0.013*

Risk level (%) Low 0 0 0.223

High 100 100

KIM-MHO physical workload components	Prevalence of WRMSDs	p-value
Level of force applied to the	Moderate	85.7	42.6	0.039**
hand/ arm area (%)	High	14.3	47.9
	Very high	0	9.5
The score of force applied to the hand/ arm area		36.87±6.6	20.46±5.73	0.021*
The gripping situation (%)	Limited gripping	76.2	85.7	0.046**
	Difficult gripping	23.8	14.3
Hand and arm position (%)	Limited	40.5	26.8	0.032**
	Undesirable	59.5	71.4
Working conditions (%)	Desirable	0	0	0.251
	Undesirable	100	100
Body posture (%)	Limited	45.2	28.6	0.041**
	Undesirable	54.8	71.4
Work organization conditions (%)	Desirable	0	0	0.126
	Undesirable	100	100
The final score of the physical workload		201.79±35.93	202.29±42.28	0.013*
Risk level (%)	Low	0	0	0.223
	High	100	100

*Mann-Whitney U test; **Chi-Square test.

Table 5

Evaluation of physical workload based on the KIM-LHC method among the participants

KIM-LHC physical workload components		Prevalence of WRMSDs		p-value
		M±SD / Percentage
		Without WRMSDs	With WRMSDs
		(n = 100)	(n = 120)
The frequency of load	750–1000	10	2.6	0.035**
handling during each work	1000–1500	30	23.1
shift (%)	1500–2000	60	69.2
	>2500	0	5.1
The load weight (%)	5–10 Kg	10	2.6	0.041**
	10–15 Kg	20	23.1
	15–20 Kg	0	7.7
	20–40 Kg	40	30.8
	>40 Kg	30	35.9
Load handling condition (%)	With one hand/ asymmetric	30	10.3	0.016**
	With one hand/ unstable load center	70	89.7
Body posture condition (%)	Straight body/ slightly bent forward	0	15.4	0.024**
	The body is slightly bent forward/ lifting or placing a load	40	20.5
	Straight body/ full body flexion	10	7.7
	Body slightly bent forward/ full body flexion	40	7.7
	Body slightly bent forward/ sitting	0	5.1
	Full body bending/lifting or placing a load	10	43.6
The final score of the physical workload		701.1±358.32	747.69±334.82	0.033*
Unfavorable working conditions		4.9±1.19	5.74±1.86	0.047*
Work organization	Desirable	0	0	0.211
conditions (%)	Undesirable	100	100
Risk level (%)	Low	0	0	0.220
	High	100	100

*Mann-Whitney U test; **Chi-Square test.

The evaluation of physical workload and its components showed a significant relationship between the final score of physical workload evaluated using KIM-MHO and KIM-LHC methods and the prevalence of WRMSDs (p-value < 0.05). Spearman correlation coefficients between the above two factors were 0.791 and 0.836, respectively (Tables 6 and 7).

Table 6

Relationships between physical workload with the prevalence of WRMSDs

KIM Methods	Components	Prevalence of WRMSDs										r **
		Neck	Shoulder	Upper back	Elbow	Wrist	Waist	Pelvis /thigh	Knee	Ankle	WRMSDs *
		p-value
KIM-MHO	The force applied to the hand/ arm area	0.041	0.035	0.732	0.026	0.024	0.037	0.894	0.582	0.760	0.021	0.721
	The gripping situation	0.027	0.035	0.604	0.986	0.046	0.273	0.084	0.066	0.850	0.046	0.642
	Hand and arm position	0.036	0.382	0.258	0.029	0.034	0.260	0.770	0.813	0.476	0.032	0.691
	Working conditions	0.804	0.635	0.381	0.693	0.257	0.349	0.834	0.472	0.641	0.001	0.683
	Body posture	0.037	0.045	0.063	0.048	0.021	0.037	0.089	0.096	0.186	0.041	0.746
	Work organization conditions	0.025	0.259	0.041	0.423	0.029	0.038	0.281	0.191	0.418	0.021	0.574
	Final score of physical workload	0.048	0.035	0.209	0.031	0.043	0.025	0.308	0.866	0.558	0.013	0.791
KIM-LHC	The frequency of load handling	0.027	0.034	0.065	0.271	0.038	0.042	0.621	0.033	0.412	0.035	0.843
	The effective load weight	0.042	0.029	0.872	0.143	0.049	0.031	0.934	0.045	0.024	0.041	0.819
	Load handling condition	0.033	0.035	0.498	0.366	0.037	0.048	0.899	0.394	0.786	0.016	0.731
	Body posture condition	0.038	0.026	0.041	0.561	0.379	0.043	0.385	0.036	0.028	0.024	0.682
	Unfavorable working conditions	0.018	0.027	0.047	0.884	0.028	0.029	0.943	0.032	0.963	0.047	0.614
	Work organization conditions	0.241	0.348	0.814	0.396	0.406	0.372	0.841	0.732	0.635	0.002	0.579
	Final score of physical workload	0.041	0.033	0.027	0.133	0.032	0.046	0.323	0.028	0.162	0.033	0.836

*Existence of musculoskeletal disorders in at least one organ; **Spearman correlation Coefficient.

3.6 Prevalence of WRMSDs based on noise exposure, mental, and physical workload

Multivariate logistic regression was implemented to identify risk factors associated with WRMSDs in different parts of the body. The extracted statistical parameters such as the odds ratio (OR) and confidence interval at 95% (95% CI) were used to interpretate the effect of each risk factors in the prevalence of WRMSDs.

The obtained results from a logistic regression analysis revealed that the prevalence of WRMSDs on some body parts was related to the demographics factors such as age, work experience, BMI, general health level and sport history. It was found that for every year of sports history, the risk of WRMSDs decreases by 26%, and for every year of work experience the risk of WRMSDs increase by 18%. It was also shown that among the noise exposure, mental workload, and physical workload factors, the most influential factor in the prevalence of WRMSDs was the physical workload evaluated by the KIM-LHC method by 32%. Then, physical activity assessed by the KIM-MHO method, mental workload, and noise exposure increased the chances of developing WRMSDs by 28%, 18%, and 6%, respectively (Table 7).

Table 7
Risk factors affecting the prevalence of WRMSDs using the logistic regression model

Variables (Tools) Parameters Index

OR (CI _95 %) B (SE) p-value

Demographic Age 1.10 (1.008 –01.209) 0.099 (0.046) 0.028

Work experience 1.18 (1.012 –1.207) 0.087 (0.041) 0.035

Body mass index (BMI) 1.12 (1.016 –1.30) 0.108 (0.05) 0.041

General health level 1.15 (1.007 –1.203) 0.17 (0.053) 0.011

Sports history 1.26 (1.21 –1.59) –0.34 (0.09) 0.003

Noise exposure Noise exposure 1.06 (1.025 –1.172) 0.094 (0.035) 0.008

Mental workload (NASA-TLX) Physical demand 1.34 (0.996 –1.06) 0.028 (0.016) 0.035

Effort 1.29 (0.944 –1.139) 0.037 (0.048) 0.045

Temporal demand 1.23 (1.03 –1.12) 0.14 (0.04) 0.023

Mental demand 1.16 (1.07 –1.34) 0.43 (0.04) 0.036

Performance 1.09 (0.991–1.22) –0.49 (0.087) 0.020

Final score of mental workload 1.18 (1.024 –1.36) 0.028 (0.06) 0.032

Physical workload (KIM-MHO) The force applied to the hand/ arm area 1.34 (1.11 –1.47) 0.39 (0.023) 0.018

The gripping situation 1.26 (1.032 –1.164) 0.179 (0.07) 0.001

Hand and arm position 1.13 (1.09 –1.43) 0.164 (0.026) 0.031

Body posture 1.23 (1.022 –1.234) 0.046 (0.023) 0.048

Working conditions 1.14 (1.006 –1.193) 0.106 (0.09) 0.002

Work organization conditions 1.11 (1.014 –1.185) 0.871 (0.037) 0.013

Final score of the physical workload 1.28 (1.08 –1.63) 0.076 (0.02) 0.001

Physical workload (KIM-LHC) The frequency of load handling 1.37 (1.023 –1.194) 0.015 (0.05) 0.037

The load weight 1.31 (1.11 –1.47) 0.039 (0.024) 0.026

Load handling condition 1.24 (1.041 –2.18) 0.097 (0.04) 0.016

Body posture condition 1.29 (1.21 –1.189) 0.013 (0.006) 0.030

working conditions 1.16 (1.19 –1.63) 0.07 (0.036) 0.002

Work organization conditions 1.087 (1.016 –1.204) 0.022 (0.017) 0.001

Final score of the physical workload 1.32 (1.12 –1.86) 0.063(0.08) 0.004

Variables (Tools)	Parameters	Index
Demographic	Age	1.10 (1.008 –01.209)	0.099 (0.046)	0.028
	Work experience	1.18 (1.012 –1.207)	0.087 (0.041)	0.035
	Body mass index (BMI)	1.12 (1.016 –1.30)	0.108 (0.05)	0.041
	General health level	1.15 (1.007 –1.203)	0.17 (0.053)	0.011
	Sports history	1.26 (1.21 –1.59)	–0.34 (0.09)	0.003
Noise exposure	Noise exposure	1.06 (1.025 –1.172)	0.094 (0.035)	0.008
Mental workload (NASA-TLX)	Physical demand	1.34 (0.996 –1.06)	0.028 (0.016)	0.035
	Effort	1.29 (0.944 –1.139)	0.037 (0.048)	0.045
	Temporal demand	1.23 (1.03 –1.12)	0.14 (0.04)	0.023
	Mental demand	1.16 (1.07 –1.34)	0.43 (0.04)	0.036
	Performance	1.09 (0.991–1.22)	–0.49 (0.087)	0.020
	Final score of mental workload	1.18 (1.024 –1.36)	0.028 (0.06)	0.032
Physical workload (KIM-MHO)	The force applied to the hand/ arm area	1.34 (1.11 –1.47)	0.39 (0.023)	0.018
	The gripping situation	1.26 (1.032 –1.164)	0.179 (0.07)	0.001
	Hand and arm position	1.13 (1.09 –1.43)	0.164 (0.026)	0.031
	Body posture	1.23 (1.022 –1.234)	0.046 (0.023)	0.048
	Working conditions	1.14 (1.006 –1.193)	0.106 (0.09)	0.002
	Work organization conditions	1.11 (1.014 –1.185)	0.871 (0.037)	0.013
	Final score of the physical workload	1.28 (1.08 –1.63)	0.076 (0.02)	0.001
Physical workload (KIM-LHC)	The frequency of load handling	1.37 (1.023 –1.194)	0.015 (0.05)	0.037
	The load weight	1.31 (1.11 –1.47)	0.039 (0.024)	0.026
	Load handling condition	1.24 (1.041 –2.18)	0.097 (0.04)	0.016
	Body posture condition	1.29 (1.21 –1.189)	0.013 (0.006)	0.030
	working conditions	1.16 (1.19 –1.63)	0.07 (0.036)	0.002
	Work organization conditions	1.087 (1.016 –1.204)	0.022 (0.017)	0.001
	Final score of the physical workload	1.32 (1.12 –1.86)	0.063(0.08)	0.004

4 Discussion

The relationship between ergonomics and WRMSDs has been extensively studied in various industries, with a strong focus on physical factors such as repetitive movements, awkward postures, and high force exertion. However, the role of cognitive and environmental factors in the development of WRMSDs has received less attention. This case study aims to fill this gap by investigating the complex interplay between physical, cognitive, and environmental factors of ergonomics and their impact on the prevalence of WRMSDs in a car-parts manufacturing industry. In industrial environments, including the car and car parts manufacturing industries, like many other working environments, musculoskeletal disorders are among the major challenges due to the nature of work and the existence of numerous ergonomic risk factors. Analysis of demographic characteristics of the subjects showed that there was a significant relationship between age, work experience, body mass index, general health level, sports history, and the prevalence of WRMSDs. Previous studies have clarified that age, work experience, and body mass index are among the important risk factors in the prevalence of WRMSDs [4, 22]. It was also found that some parameters related to lifestyle, such as sports activity, smoking, as well as the level of general health, are also among the effective parameters in maintaining the health of the musculoskeletal system, which is consistent with the results of previous studies [6, 22].

It was found that 85% of the subjects had musculoskeletal disorders in at least one of their organs, which indicates the high prevalence of this complication among workers working in the studied industry. It should be noted that the highest prevalence of WRMSDs were in the lumbar, neck, shoulder, and wrist organs with 68.6, 65.3, 63.9, and 58.3%, respectively. The lowest WRMSDs prevalence were in the hip/thigh and ankle with 35.4 and 30.8%, respectively. Among those who have these disorders, 29.4% have changed their duties due to neck discomfort, 34.6% of workers have been prevented from doing their daily work due to back pain, 39.8% of them have been referred to a therapist, and 30.8% have been forced to leave their job.

Similar to the present study, the other studies also show a high prevalence of musculoskeletal disorders among workers working in various industries, including car-parts industries. Seung Tae Yang et al. studied a motor vehicle parts manufacturing industry. They reported that 63.2% of workers have musculoskeletal disorders caused by manual material handling (MMH), exerting excessive physical pressure, and having repetitive movements of the upper limbs. These results are consistent with the findings of the present study [42].

A study by Widanarko et al. concerning the effect of physical, psycho-social, and environmental risk factors on the prevalence of WRMSDs in 3003 New Zealand workers clarified that among workers in various industries, musculoskeletal disorders are prevalent in the neck, shoulders, hands/ arms, wrists, and waists. Undesirable working posture, unfavorable gripping and hand movements, work with vibrating tools, and working in a hot or cold environment (heat strain) are among the most important causes of WRMSDs [19]. These findings are consistent with the results of the present study.

It was found that increasing the noise exposure leads to increased musculoskeletal discomfort in the neck, shoulders, and wrists. The Spearman correlation coefficient between the noise exposure and the prevalence of WRMSDs in neck, shoulder, and wrist were 0.478, 0.362, and 0.474, respectively, which indicating a moderate correlation between them. It should be noted that the results of this study are similar to the results of the study conducted by MariusCheta et al., which found that the distance between the noise source and the ears can affect the body posture, and as a result of exposure to excessive noise (above 85 dB), the possibility of WRMSDs prevalence increases [11]. Other studies have also shown that exposure to noise and inappropriate posture can act as important risk factors on the prevalence of occupational stress and cognitive problems [43]. In this study, due to the high noise level produced by machines and equipment, performing some tasks at the same time, such as hammering and polishing the produced parts, the nature of grinding and miling work, the short distance between noise sources and working stations, high speed of hand movements and rotation of the trunk, and neglecting the ergonomic principles during work, the prevalence of musculoskeletal disorders in the upper limbs was high. Prediction of the logistic regression model showed that noise exposure increases the chance of WRMSDs by 6%.

It was found that workers with WRMSDs have a higher average mental workload score compared to those without WRMSDs (77.84±12.9 vs. 72.27±15.11). Examination of the six dimensions of mental workload also revealed that mental demand, physical demand, temporal demand, effort, and performance in workers with WRMSDs were at higher levels. The comparison of mental workload of workers with and without WRMSDs showed a significant difference between the average final score of mental workload and all related components except the frustration with the prevalence of WRMSDs. Analysis of the relationship between mental workload components and the prevalence of WRMSDs using the Spearman correlation test revealed that the correlation coefficient between the effort, physical demand, temporal demand, mental demand, performance, and final score of mental workload with the prevalence of WRMSDs were 0.84, 0.81, 0.79, 0.76, –0.72, and 0.78, respectively. It was determined that there was a significant relationship between these parameters.

Therefore, according to the results of this study, it can be said that mental workload is one of the critical risk factors in the prevalence of WRMSDs. A study by Koohpaei et al. conducted in 2016 in the automotive manufacturing industry also showed a significant relationship between WRMSDs and mental workload. It was also found that the score of effort and performance among workers in the automotive industry is high, which can be one of the most important reasons for the increased physical and mental demands in these industries [44]. A study conducted by Sadeghi-Yarandi et al. in 2020 also showed a significant relationship between the prevalence of musculoskeletal disorders with the parameters of age, work experience, body mass index, gender, and education level from demographic characteristics, the physical demand on the topic of job stress and components of physical load, time pressure and effort on the topic of mental workload [4]. Consistent with the previous studies, in the present study, due to the nature of work and doing occuational tasks and sub-tasks in high speed situations in the studied unit, workers should perform their duties under high physical and mental demand with high effort in a short and definite timing period and simultaneously maintain their performance level at an optimal level. This increased workload increases the prevalence of WRMSDs in the neck, shoulders, wrists, and back. The demanding nature of production tasks, combined with tight deadlines and high levels of stress, can lead to mental fatigue and decreased concentration, increasing the risk of musculoskeletal injuries. This highlights the need for a holistic approach to ergonomics, which takes into account not only physical but also cognitive demands in the workplace.

The logistic regression model results showed that the chance of developing WRMSDs increased by 18% for one score of increased mental workload. Also, among the six dimensions of mental workload, physical demand and effort increase the chance of WRMSDs by 34 and 29%, respectively, more than the other components. The component of performance and efficiency reduces the chance of WRMSDs by about 9%.

According to KIM- MHO index, the mean final score of physical workload among the subjects was equal to 201.86±36.41 and according to KIM –LHC index the mean final score of physical workload was 738.18±336.42, which located in the high-risk level. The high physical workload level in this unit can be due to high force insertion for carrying the manufactured parts, awkward physical posture during shift work, repetitive hand/arm movements, manual material handling, and performing tasks for long periods without suitable rest time. It should be noted that the results of the study by Klussmann et al. in 2017 also indicated a significant relationship between the prevalence of musculoskeletal disorders and the key index score (KIM) [45], which is consistent with the results of the present study. Previous studies have shown that not using the work-rest cycle, according to the type of employees’ activity, can ultimately lead to the accumulation of lactic acid, static and dynamic load caused by physical activity in the body, and ultimately accelerate the prevalence of WRMSDs [46].

According to the KIM-MHO index, in more than half of the cases (53.1%), the force applied to the hand/ arm area was high. The gripping situation was mostly (77.6%) limited, and in 6.2% of cases, the hand/arm position was unfavorable. Participants were worked in awkward postures, often with slight rotation or complete body flexion or extension.In addition, the working situations and work organization conditions are entirely (100%) in an unfavorable situation. Based on the results, it was found that the mean final score of KIM-MHO in workers with WRMSDs was 202.29±42.28 and among workers without WRMSDs was 201.79±93.35. The mean final score of physical workload based on the KIM-LHC index in workers with WRMSDs was 747.69±334.82 and in workers without WRMSDs was 701.1±358.32.

It was found that the load-handling more than 1000 times during a work shift, carrying a load weighing more than 40 kg, load-handling with one hand or asymmetrically, awkward posture, and unfavorable working conditions were higher in workers with WRMSDs. Analysis of the relationship between physical workload and the prevalence of WRMSDs using the Spearman correlation test revealed that the correlation coefficient between KIM –MHO and KIM –LHC indexes with the prevalence of WRMSDs were 0.79 and 0.83, respectively, and there was a significant relationship between the mentioned parameters.

According to the logistic regression findings, based on the KIM-MHO index, the force applied to the hand/ arm area, gripping situation, body posture, working conditions, hand/arm position, and work organization conditions, and based on the KIM-LHC index, increasing the number of load handling times, the load weight, awkward posture of the body, load-handling with one hand or asymmetrically, unfavorable working conditions and work organization can increase the chances of WRMSDs prevalence.

Ultimately, the findings of this case study provide valuable insights into the potential relationship between mental and physical workload, noise exposure, and the prevalence of WRMSDs in a car-parts manufacturing industry. The results suggest that there is a complex interplay between these factors, with each one contributing to the development of WRMSDs in workers. One of the key findings of this study is the significant impact of mental workload on the occurrence of WRMSDs. This is consistent with previous research that has highlighted the role of psychosocial factors in the development of WRMSDs. The high levels of mental workload reported by workers in this industry, combined with the physically demanding nature of their work, may have contributed to the increased risk of WRMSDs.

This highlights the need for interventions that address both physical and mental workload in order to effectively prevent and manage WRMSDs in this setting. Another important factor identified in this study is noise exposure. The results showed a significant association between high levels of noise exposure and the prevalence of WRMSDs. This is consistent with previous research that has linked noise exposure to various health issues, including WRMSDs. The noisy environment in a car-parts manufacturing industry may contribute to increased stress levels and fatigue, which can further exacerbate the impact of physical and mental workload on WRMSDs. Furthermore, this study also highlights the potential interaction between these factors. For example, workers who reported high levels of mental workload and noise exposure were found to have a significantly higher prevalence of WRMSDs compared to those who reported low levels of both factors. This suggests that interventions targeting one factor alone may not be sufficient in reducing the risk of WRMSDs, and a comprehensive approach is needed to address all contributing factors.

Overall, this case study provides important implications for the prevention and management of WRMSDs in the workplace. It highlights the need for a holistic approach that takes into account both physical and psychosocial factors, as well as their potential interactions. This could include interventions such as ergonomic improvements, noise control measures, and stress management programs. By addressing these factors, employers can create a healthier and safer work environment for their employees, ultimately reducing the prevalence of WRMSDs and improving overall worker well-being. This case study has significant implications for employers and policymakers in the car-parts manufacturing industry. This could include implementing job rotation to reduce physical demands, providing breaks to reduce mental fatigue, and implementing noise control measures to reduce the impact of environmental factors. Such interventions not only promote worker well-being but also have a positive impact on productivity and overall business performance.

4.1 Limitations and strengths of the study

One of the strengths of the present study was determining the role of environmental, psychological, social, and physical risk factors in the prevalence of WRMSDs in a car-parts manufacturing company for the first time in Iran. Therefore, considering the importance of different jobs in the automotive industry, the present study results can create a novel scientific insight on the prevalence of WRMSDs and the consequences and adverse effects of these disorders in workers’ health in similar industries with high volume of manual activity. Therefore, efforts to prevent and control these disorders become essential. One of the limitations of this study was the lack of evaluation of musculoskeletal disorders using clinical instruments such as EMG (Electromyography) due to time and facility constraints.

That is because objective tools are much more accurate than questionnaires and self-report tools (subjective). Therefore, it can be suggested that this method should be used in future studies to evaluate these disorders in different areas of the musculoskeletal system, and the results can be compared with the present study. Another limitation was the concurrence of the study with the COVID-19 pandemic period. The COVID-19 pandemic affected the working conditions in the studied industry and made it impossible to carry out intervention measures in accordance with the obtained results. Therefore, it is suggested that in the future, researchers conduct intervention studies and report the effectiveness of the corrective measures taken.

5 Conclusion

The present study results showed that the prevalence of WRMSDs among people working in the studied car-parts company was high, and the parameters of noise exposure, mental, and physical workload in the workplace were among the most influential risk factors affecting the prevalence of WRMSDs. The findings suggest that a comprehensive approach is needed to address all contributing factors in order to effectively prevent and manage WRMSDs in the workplace. This includes interventions that target both physical and psychosocial factors, as well as their potential interactions. By implementing such interventions, employers can create a healthier and safer work environment for their employees, ultimately reducing the prevalence of WRMSDs and promoting overall worker well-being especially in the automotive industries as one of the most important industries in the world.

Conflict of interest

The authors declare that there is no conflict of interest.

Ethical approval

The present study was approved by the ethics committee of University of Social Welfare and Rehabilitation Sciences (Ethics code: IR.USWR.REC.1400.114).

Funding

None to report.

Footnotes

Acknowledgments

The authors express their gratitude to the participants in the study.

Informed consent

Not applicable

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8-hour equivalent dose percent	Prevalence of WRMSDs (body region)
	Neck	Shoulder	Upper back	Elbow	Wrist	Waist	Pelvis/thigh	Knee	Ankle
p-value
	0.027^*	0.034^*	0.352	0.375	0.045^*	0.575	0.806	0.635	0.930
Spearman correlation coefficient
	0.487	0.362	0.196	0.172	0.474	0.238	0.165	0.217	0.194