Investigating the relationship between body structure status,work activity type,and the prevalence of musculoskeletal disorders among detergent industry workers

Abstract

BACKGROUND:

The physical condition of workers’ body structure and assigned duties, can contribute to the prevalence of musculoskeletal disorders.

OBJECTIVE:

This study aimed to investigate the relationship between body structure status, type of work activity, and the prevalence of musculoskeletal disorders among workers in the detergent industry.

METHODS:

This cross-sectional study involved 148 industrial workers selected based on inclusion criteria and their medical checkup records. Data collection for the study included a demographic information questionnaire, a body map questionnaire, and an assessment of the workers’ musculoskeletal system conducted by three physiotherapists simultaneously.

RESULTS:

54.1% of the participants had a total body structure score classified as poor or fair. The neck region showed the highest prevalence of musculoskeletal disorders (51.4%), followed by the lower back region (35.1%). Significant associations were found between abnormalities in the upper and middle limbs of the body and the prevalence of pain in the right shoulder region (Fisher/F = 9.29, P≤0.05) as well as the intermediate back region (F = 10.28, P≤0.01). Office workers experienced a higher prevalence of neck pain than workers in the product line and technical roles, with a statistically significant Odds Ratio (OR) ranging between 2.7 and 6.6 times. Conversely, industrial workers who operate powered machinery showed a higher prevalence of pain in the left shoulder (OR = 3.93) and left foot (OR = 4.07). Meanwhile, workers involved in loading and unloading tasks had a higher prevalence of pain in the middle back (OR = 3.61) and right foot (OR = 4.5) compared to office workers.

CONCLUSIONS:

The prevalence of pain in the right shoulder and middle back may be due to abnormalities in the upper and intermediate body structure. Production line workers reported a higher prevalence of pain in the left shoulder, middle back, and foot compared to office workers.

Keywords

Body detergent disorder industry musculoskeletal prevalence worker

1 Introduction

Musculoskeletal impairments encompass more than 150 conditions that cause chronic pain or restricted movement across all age groups, from childhood to old age. These conditions can be categorized as short-term, such as muscle strains or sprains, or long-term, such as low back pain or osteoarthritis [1].

Work-related musculoskeletal disorders (WMSDs) have become a significant contributing factor to a decline in human labor. Research conducted in 2019 on the global burden of disease revealed that disability-adjusted life years (DALYs) due to lower back pain ranked fourth among the 25 to 49-year-old population [2]. Additionally, the frequency of these disorders is significant. Reports from the National Institute of Occupational Safety and Health (NIOSH) indicated that WMSDs account for 48% of all occupational diseases [3].

A case study conducted on workers in the casting industry in Brazil reported that 75.2% of workers experienced symptoms of musculoskeletal disorders (MSDs) within the past 12 months, with 38.5% of them requesting time off due to MSD-related issues [4].

In the automobile parts assembly industry in Iran, research conducted over 12 months reported a high prevalence of MSDs in various body parts. The prevalence rates were as follows: the waist (78.3%), wrist/hand (59.5%), foot/leg (57.7%), shoulder (56.1%), knee (55.1%), upper back (46.2%), neck (39.9%), forearm (17.9%) and thighs (13.8%) [5].

The occurrence of MSDs is influenced by multiple factors, including biomechanical, organizational, psychosocial, and environmental factors. [2]. Work-related factors such as posture, task type, and repetition can contribute to the prevalence of WMSDs. For example, a study in the cotton cloth industry in China found varying prevalence rates of lower back pain among sewing (50%), packing (61.5%), and ironing workers (42.5%), with many workers exhibiting bending, repetitive motion, and awkward posture [6]. Another study conducted on assembly workers in the electronics parts production industry in Iran revealed that the prevalence rates of MSDs were 73.6% in the lower back, 71.1% in the wrist/hand, and 67.9% in the neck region [7].

In the context of the detergent industry being a process industry, it is essential to determine the prevalence of MSDs in these industries. A joint research study conducted between Iran and Poland focused on workers in the meat processing industries. The results revealed varying estimated prevalence rates for MSDs in different body regions, with Poland reporting rates of 38% (back pain), 40% (knee pain), 24% (neck pain), and 24% (upper back pain). In comparison, Iran reported rates of 64% (back pain), 24% (knee pain), 42% (neck pain), and 34.5% (upper back pain) [8].

Based on a study conducted on the working population, it has been determined that body structural status, demographic factors, and occupational information are associated with the prevalence of MSDs. The study, which followed participants for 12 months, concluded that employees with a higher Body Mass Index (BMI) and a lower physical workload have a higher risk of experiencing MSDs compared to those with a lower BMI and a more physically demanding workload [9]. In a cross-sectional study conducted in Spain in 2019, 40 assembly workers were assessed. The study measured parameters such as body height, sitting height, degrees of thoracic kyphosis, back lordosis, and self-reported spine discomfort feelings before and after work. The results revealed that the curvature of the thoracic and lumbar regions of the spine was associated with back discomfort. Additionally, assembly workers who spent long periods standing experienced more back pain, changes in height, and increased thoracic and lumbar curvature [10].

Given the limited number of studies examining the relationship between body structure, task type, and MSDs, it is crucial to investigate the association between body structure, the type of work activities, and the prevalence of MSDs among workers in the detergent industry.

2 Methods

2.1 Participants, inclusion, and exclusion criteria

The cross-sectional study conducted in the detergent industry was approved by the university ethics committee under the code IR.QUMS.REC.1402.052 and was carried out with contract number 28.20.24551. The study considered the entire statistical population of a detergent industry (N = 280 people), with a confidence interval of 95% (CI = 95%) and a 5% margin of error (d = 0.05), along with a Z-score of 1.96. A prevalence of 50% (p and q = 0.5) was used in the Cochran sample size formula (Equation 1). Therefore, the estimated sample size was 162 people. According to the Cochran formula, if the prevalence is unknown, the maximum value of 50% can be utilized. Based on the research conducted and the absence of similar studies in the detergent industry a prevalence rate of 50% was assumed. Equation 1. Cochran sample size formula: $\begin{matrix} n = \frac{\frac{z^{2} pq}{d^{2}}}{1 + \frac{1}{N} [\frac{z^{2} pq}{d^{2}} - 1]} \\ n = \frac{\frac{1 . 96^{2} \times 0.5 \times 0.5}{0 . 051^{2}}}{1 + \frac{1}{280} [\frac{1 . 96^{2} \times 0.5 \times 0.5}{0 . 05^{2}} - 1]} = 162 \end{matrix}$

After meeting the inclusion criteria and reviewing medical checkup records, the sample size decreased to 158. Out of the 158 workers who expressed willingness to participate, 10 samples were excluded due to meeting the exclusion criteria, resulting in a final sample size of 148 participants.

The inclusion criteria for the study consisted of individuals with abnormalities or problems in their musculoskeletal structure based on their recent medical checkup report. This report must be recorded in the individual’s medical file by the occupational medicine doctor. Additionally, participants needed to have a minimum of one year of work experience in the detergent industry and express a willingness to participate in the research.

On the other hand, the exclusion criteria included individuals with recent bone fractures, genetic abnormalities, uncommon diseases, a history of spinal cord surgery or any deep surgery in the musculoskeletal system, individuals who had experienced accidents, those with nervous and vascular problems, individuals with a second job, a history of drug abuse, pregnant women during the musculoskeletal assessment, and individuals using medication specifically related to musculoskeletal discomfort.

2.2 Assessment of body structure status

A team of three physiotherapists assessed the body structure of the workers focusing on musculoskeletal abnormalities in the neck, shoulders, spine, knees, and feet. The research identified skeletal and structural issues, ranging from minor to critical problems diagnosed by the physiotherapist team. To evaluate the participants’ body structure, the physiotherapists instructed them to remove their clothes and lean against a wall. This examination took place in a private room with covered windows and a locked door to ensure the participants ‘privacy and dignity. Only the physiotherapists and the participants being examined were present in the room, allowing the physiotherapists to assess any abnormalities in the shoulder, spine, and neck from both side and front views. During the examination, the physiotherapists asked the participants to raise their arms and move their heads to observe any signs of kyphosis or lordosis. Then, the physiotherapists instructed the participants to turn their faces towards the wall. This allowed the physiotherapists to assess the same body parts from a different angle.

For the examination of the remaining body parts, the physiotherapists instructed the participants to lie down on a bed. They then proceeded to examine the knees and feet for any musculoskeletal abnormalities. This involved raising the legs, bending and stretching the knees, and observing for any abnormal curvature or flatness in the soles of the feet. Finally, the physiotherapists asked the participants to lie on their stomachs for a back-view examination. In the end, all of the physiotherapists determined scores for workers’ body sections (upper, middle, and lower body parts) and whole-body based on their assessment and reaching a consensus with each other.

The assessment of body structure, also known as a physical examination yielded documents that included scores for various body parts: the upper body (neck, right shoulder, left shoulder), middle body (upper back, middle back, lower back), and lower body limbs (right knee, left knee, right foot, left foot). Additionally, a total body structure score was calculated by averaging the scores from the upper, middle, and lower body parts. Each body section (upper, middle, and lower body) was assigned a score ranging from 0 to 100. The scoring categories for the upper middle, lower, and total body scores were defined using visual binning, which involved creating cut points at the mean±1 standard deviation [11]. The mean and standard deviation of the upper, middle, lower, and total body scores are 65.27±8.9, 70.68±7.2, 68.45±11.23, and 67.8±6.1, respectively. For the lower body scores, the categories were defined as≤57.2 (poor), 57.3–68.4 (fair), 68.5–79.7 (good), and≥79.8 (very well). The cut points for upper body scores were≤56.4 (poor), 56.5–65.3 (fair), 65.4–74.2 (good), and≥74.3 (very well). Similarly, the middle body scores were classified as≤63.4 (poor), 63.5–70.7 (fair), 70.8–77.9 (good), and≥80 (very well). The total body scores were categorized as follows: ≤61.6 (poor), 61.7–67.8 (fair), 67.9–73.9 (good), and≥74 (very well). After receiving the musculoskeletal structure assessment files from the physiotherapy team, the researchers requested that participants complete demographic questionnaires, which also included exclusion criteria.

2.3 Demographic and Body Map Questionnaires

Subjects were enrolled in the study after completing the consent form. We collected demographic and occupational data from 158 participants, including age, gender, marital status, education level, weight, height, work experience, working hours, work unit, and task type.

The Body Map Questionnaire measures the prevalence of MSDs pain and identifies the location of pain in various body regions over the past year. Before data collection, participants received an explanation from the researcher and were then asked to complete the self-report questionnaire. The validity and reliability of the questionnaire were confirmed in 1995, with subsequent studies demonstrating high validity and reliability [12 –14]. This questionnaire divides the body into regions and assesses discomfort using a five-level Likert scale: no pain = 0, low pain = 1, medium pain = 2, high pain = 3, and extreme pain = 4 [15].

2.4 Statistical Analysis

The chi-square test was used to examine the relationships between variables, such as classified upper, middle, lower, and total body scores, and the prevalence of MSDs (a binary nominal variable indicating the presence or absence of pain). The variables of the work unit and work task were analyzed for the prevalence of MSDs using the binary logistic regression test.

3 Result

3.1 Participants’ characteristics

The mean and standard deviation of the age among participants was 35.85±7.75 years, while for work experience, it was 8.72±6.56 years. Table 1 presents additional descriptive information about the characteristics of the workers, including both quantitative and qualitative aspects.

Table 1
Quantitative and qualitative descriptive information of workers (n = 148)

Quantitative Variable

Variable Mean±SD Variable Mean±SD

Age (year) 35.85 ± 7.75 Work of experience (year) 8.72 ± 6.56

Height (cm) 171.36 ± 9.89 Work time (Hour) 8.33 ± 0.48

Weight (kg) 77.35 ± 13.96

Qualitative Variable

Variable Category Frequency Percentage Variable Category Frequency Percentage

Gender Male 105 70.9% Work unit Office 45 30.4%

Female 43 29.1% Warehouse 73 49.3%

Marital Status Single 31 20.9% Production/Technical 30 20.3%

Married 117 79.1% Work task Work with Computer 59 39.9%

Level of Education Lesser than Diploma 32 21.6% Basic Production Operator 15 10.1%

Diploma 52 35.1% Stickering Worker 15 10.1%

Associate 6 4.1% Load-Unloading Worker 17 11.5%

Bachelor of Science 41 27.7% Powered Industrial/ Transportation Truck Driver 19 12.8%

Master of Science or Higher 17 11.5% Shift Technician/Services 23 15.5%

Quantitative Variable
Age (year)		35.85 ± 7.75		Work of experience (year)		8.72 ± 6.56
Height (cm)		171.36 ± 9.89		Work time (Hour)		8.33 ± 0.48
Weight (kg)		77.35 ± 13.96
Qualitative Variable
Variable	Category	Frequency	Percentage	Variable	Category	Frequency	Percentage
Gender	Male	105	70.9%	Work unit	Office	45	30.4%
	Female	43	29.1%		Warehouse	73	49.3%
Marital Status	Single	31	20.9%		Production/Technical	30	20.3%
	Married	117	79.1%	Work task	Work with Computer	59	39.9%
Level of Education	Lesser than Diploma	32	21.6%		Basic Production Operator	15	10.1%
	Diploma	52	35.1%		Stickering Worker	15	10.1%
	Associate	6	4.1%		Load-Unloading Worker	17	11.5%
	Bachelor of Science	41	27.7%		Powered Industrial/ Transportation Truck Driver	19	12.8%
	Master of Science or Higher	17	11.5%		Shift Technician/Services	23	15.5%

3.2 Skeletal Abnormalities

Physiotherapists have diagnosed skeletal and structural abnormalities in various parts of the body. The current study focused on abnormalities in the neck region which included forward head posture, weakness in certain muscles, and kyphotic lordotic posture. Some common postural issues include uneven shoulders, scapular dyskinesis (depression and elevation of the scapula), and rounded shoulders. The spine and back can be affected by swaying back or flat back posture, lumbar arch, kyphosis, lordosis, and scoliosis. Other issues can arise in the knees, such as genu valgum, genu varum, and patella attrition. Feet may experience Achilles tendon weakness, inward or outward turning of the foot, Hallux valgus, flat feet, or high arches.

The physiotherapy team’s assessment revealed that the highest percentage of abnormalities among the participants were found in the right and left knees (68.2%), followed by the right shoulder and lower back regions (each 52%) (Fig. 1).

Fig. 1

Abnormalities in different body regions among the participants (n = 148).

3.3 The evaluation results of workers’ body structure status

According to the score categories, 14.9% of participants showed abnormalities in their body’s midsection, while 54.1% of subjects received an overall body score of poor or fair (see Fig. 2). All Abnormalities mentioned in section 3.2 have been checked and identified in the body’s different parts.

Fig. 2

The percentage distribution of body structure scores in different regions categorized and the total body score among participants (n = 148).

3.4 Body Map Questionnaire

The results of the body map questionnaire showed that participants reported the highest prevalence of discomfort in the neck region (51.4%), followed by the lower back (35.1%) and left knee (31.1%) (See Fig. 3).

Fig. 3

Prevalence of MSDs in different regions of the body among study participants (n = 148).

3.5 Results of statistical tests

Table 2 presents an analysis of the relationship between classified abnormality scores of the upper, middle, and lower bodies and the prevalence of pain in various body regions. The results show a significant relationship between the prevalence of pain in the right shoulder and the abnormality score of the upper body (Fisher/F = 9.29, p < 0.05). Additionally, there is a significant relationship between the prevalence of middle back pain and the abnormality score of the middle body (Fisher = 10.28, p < 0.01).

Table 2
The results of the relationship between scores in three body areas and the prevalence of MSDs in different body regions based on the chi-square/Fisher test (n= 148)

Classified Scores\Body Regions Neck Right Shoulder Left Shoulder Upper Back Middle Back Lower Back Right Knee Left Knee Right Foot Left Foot

Upper Body χ² = 4.17 F = 9.29 F = 1.25 F = 2.22 – – – – – –

P = 0.24 P = 0.02^* P = 0.67 P = 0.44 – – – – – –

Middle Body – – – – F = 10.28 F = 5.77 – – – –

– – – – P = 0.01^* P = 0.13 – – – –

Lower Body – – – – – – χ² = 1.88 χ² = 3.74 F = 1.58 F = 0.76

– – – – – – P = 0.57 P = 0.29 P = 0.70 P = 0.87

Classified Scores\Body Regions	Neck	Right Shoulder	Left Shoulder	Upper Back	Middle Back	Lower Back	Right Knee	Left Knee	Right Foot	Left Foot
Upper Body	χ² = 4.17	F = 9.29	F = 1.25	F = 2.22	–	–	–	–	–	–
	P = 0.24	P = 0.02^*	P = 0.67	P = 0.44	–	–	–	–	–	–
Middle Body	–	–	–	–	F = 10.28	F = 5.77	–	–	–	–
	–	–	–	–	P = 0.01^*	P = 0.13	–	–	–	–
Lower Body	–	–	–	–	–	–	χ² = 1.88	χ² = 3.74	F = 1.58	F = 0.76
	–	–	–	–	–	–	P = 0.57	P = 0.29	P = 0.70	P = 0.87

F: Fisher^*P≤0.05.

Table 3 presents the results of the relationship between work units, work tasks, and the prevalence of MSDs in different body regions. The findings indicate that neck pain in the production/technical unit (OR = 0.037, P < 0.05) and tasks performed by production operators (OR = 0.15, P < 0.01) and load and unload workers (OR = 0.24, P < 0.05) had lower statistical significance compared to the office unit and tasks involving computer work. Additional relationships can be found in Table 3.

4 Discussion

4.1 The relationship between musculoskeletal abnormalities and the prevalence of pain in different regions of the body

The highest frequency of abnormalities among the subjects (Fig. 1) was observed in the left and right knees (68.2%) and the lower back (52%). This finding is consistent with a study conducted by Piri et al. on ship staff, which identified hyper-lordosis of the lumbar spine as the most common abnormality [16]. Similar to the results of Doosti et al.’s study on diving coaches, the lower back ranks second in terms of frequency of abnormalities. However, there is a contradiction regarding knee abnormalities which are ranked third [17]. Abnormalities such as genu valgum or genu varum, patella attrition, and being overweight can contribute to knee pain. Research suggests that overweight individuals are more likely to experience frequent knee pain (FKP) [18]. Another study emphasizes the significance of bone attrition in the early stages of knee osteoarthritis (KOA) and knee pain [19]. Additionally, prolonged standing at work can be a contributing factor to knee pain [20]. According to Fig. 3, the prevalence of knee pain among workers ranged from 29.1% to 31.1%.

According to Fig. 3, the prevalence of lower back pain among the workers was 35.1%. Factors such as lumbar curvatures, abdominal obesity [21], prolonged standing or sitting, and poor sitting posture without back support can contribute to the development of low back pain [22].

Among the subjects, the results indicate that 52% exhibit minor to significant abnormalities in the right shoulder, including uneven shoulders, scapular depression and elevation, and rounded shoulders. The reported pain prevalence of 25.7% among participants can be attributed to various factors. These include tasks performed at shoulder level, such as operating a vacuum lifter for moving carton packs, handling large-dimensional loads, palletizing, and driving powered industrial trucks (forklifts, reach trucks, and electric pallet jacks). Additionally, moving raw materials in metallic buckets can contribute to shoulder pain.

Based on the findings in Table 2, there is a statistically significant relationship between the prevalence of musculoskeletal pain in the right shoulder (25.7%) and middle back regions (29.7%) and the corresponding abnormalities (52% and 39.2%, respectively). These results suggest that musculoskeletal abnormalities in these regions can contribute to pain in those body parts. Furthermore, the type of work posture adopted by the subjects in the workplace may be one of the factors contributing to the occurrence of these abnormalities. These findings align with a study by Bodin et al. on shoulder pain among male industrial workers, which demonstrated a positive association between shoulder pain and biomechanical exposure in two different samples [23].

4.2 The relationship between the type of work unit or task and the prevalence of pain in different regions of the body

Based on Fig. 3, the highest prevalence of pain was observed in the neck, lower back, and left knee regions, respectively. This finding is consistent with a study by Kliniec et al. on the correlation between the frequency of work-related disorders and the type of work among Polish employees [24]. Workers who spend a significant amount of time sitting and standing, performing repetitive back flexion and extension, and lifting heavy loads may experience increased pressure on the spine and a higher prevalence of lower back pain [10].

According to the results presented in Table 3, neck pain prevalence is significantly higher in production/technical units compared to office units (2.7 times less), as well as in production operator and load-unload worker tasks compared to tasks involving computer work (6.6 and 4.2 times less, respectively). The static posture, prolonged neck flexion, and forward head posture maintained during office work and computer tasks [25] can contribute to these findings. Furthermore, 43.9% of the subjects (Fig. 1) exhibit abnormalities, which, when combined with workplace conditions, could contribute to the observed prevalence of 51.4% of neck pain among participants (Fig. 3). Additionally, Govaerts et al. reported in their research on work-related MSDs in European secondary industries of the 21st century that neck work-related MSDs are highly prevalent [26].

Table 3
Results of the relationship between work unit, task, and prevalence of the MSDs based on binary logistic regression (n = 148)

Univariate (CI 95%)

Independent Variables Neck Right Shoulder Left Shoulder Upper Back Middle Back Lower Back Right Knee Left Knee Right Foot Left Foot

Work Units Office 1 1 1 1 1 1 1 1 1 1

Warehouse 0.50 (0.24–1.09) 1.33 (0.57–3.10) 2.08 (0.87–5.0) 2.21 (0.92–5.30) 2.28 (1.00–5.19)^* 1.54 (0.70–3.38) 1.52 (0.67–3.44) 1.62 (0.73–3.60) 2.48 (0.65–9.44) 3.03 (0.81–11.30)

Production/Technical 0.37 (0.14–0.95)^* 0.62 (0.19–2.0) 0.28 (0.06–1.43) 0.61 (0.17–2.21) 0.34 (0.08–1.35) 0.80 (0.29–2.25) 0.55 (.017–1.76) 0.38 (0.11–1.30) 1.55 (0.29–8.27) 3.50 (0.80–15.28)

Work Tasks Computer Work 1 1 1 1 1 1 1 1 1 1

Basic Production Operator 0.15 (0.4–0.58) ^** 0.21 (0.025–1.73) 0.25 (0.03–2.10) 0.21 (0.02–1.73) 0.23 (0.02–1.90) 0.57 (0.14–2.26) 0.15 (0.02–1.23) 0(0–0) 1.66 (0.29–9.54) 1.36 (0.24–7.52)

Stickering Worker 1.63 (0.46–5.76) 1.46 (0.43–4.98) 0.88 (0.22–3.61) 2.56 (0.79–8.28) 3.67 (1.13–11.93)^* 1.52 (0.47–4.90) 0.53 (0.13–2.08) 0.76 (0.21–2.72) 0.77 (0.08–0.71) 0(0–0)

Load–Unloading Worker 0.24 (0.07–0.79)^* 0.90 (0.25–3.19) 1.93 (0.59–6.22) 1.60 (0.50–5.07) 3.61 (1.17–11.14)^* 2.56 (0.85–7.71) 1.47 (0.48–4.47) 1.87 (0.62–5.61) 4.5 (1.12–18.04)^* 2.72 (0.67–11.05)

Powered Industrial/ Transportation Truck Driver 0.81 (0.28–2.34) 1.71 (0.57–5.15) 3.93 (1.32–11.70)^* 1.71 (0.56–5.14) 2.89 (0.98–8.53) 1.65 (0.57–4.81) 1.23 (0.42–3.62) 1.89 (0.66–5.43) 2.02 (0.43–9.41) 4.07 (1.13–14.72)^*

Shift Technician/Services 0.38 (0.14–1.03) 1.03 (0.34–3.11) 0.053 (0.14–2.07) 0.44 (0.11–1.69) 0.67 (0.19–2.32) 1.21 (0.43–3.37) 0.74 (0.25–2.18) 0.74 (0.25–2.18) 0.49 (0.05–4.45) 1.86 (0.47–7.31)

		Univariate (CI 95%)
Work Units	Office	1	1	1	1	1	1	1	1	1	1
	Warehouse	0.50 (0.24–1.09)	1.33 (0.57–3.10)	2.08 (0.87–5.0)	2.21 (0.92–5.30)	2.28 (1.00–5.19)^*	1.54 (0.70–3.38)	1.52 (0.67–3.44)	1.62 (0.73–3.60)	2.48 (0.65–9.44)	3.03 (0.81–11.30)
	Production/Technical	0.37 (0.14–0.95)^*	0.62 (0.19–2.0)	0.28 (0.06–1.43)	0.61 (0.17–2.21)	0.34 (0.08–1.35)	0.80 (0.29–2.25)	0.55 (.017–1.76)	0.38 (0.11–1.30)	1.55 (0.29–8.27)	3.50 (0.80–15.28)
Work Tasks	Computer Work	1	1	1	1	1	1	1	1	1	1
	Basic Production Operator	0.15 (0.4–0.58) ^**	0.21 (0.025–1.73)	0.25 (0.03–2.10)	0.21 (0.02–1.73)	0.23 (0.02–1.90)	0.57 (0.14–2.26)	0.15 (0.02–1.23)	0(0–0)	1.66 (0.29–9.54)	1.36 (0.24–7.52)
	Stickering Worker	1.63 (0.46–5.76)	1.46 (0.43–4.98)	0.88 (0.22–3.61)	2.56 (0.79–8.28)	3.67 (1.13–11.93)^*	1.52 (0.47–4.90)	0.53 (0.13–2.08)	0.76 (0.21–2.72)	0.77 (0.08–0.71)	0(0–0)
	Load–Unloading Worker	0.24 (0.07–0.79)^*	0.90 (0.25–3.19)	1.93 (0.59–6.22)	1.60 (0.50–5.07)	3.61 (1.17–11.14)^*	2.56 (0.85–7.71)	1.47 (0.48–4.47)	1.87 (0.62–5.61)	4.5 (1.12–18.04)^*	2.72 (0.67–11.05)
	Powered Industrial/ Transportation Truck Driver	0.81 (0.28–2.34)	1.71 (0.57–5.15)	3.93 (1.32–11.70)^*	1.71 (0.56–5.14)	2.89 (0.98–8.53)	1.65 (0.57–4.81)	1.23 (0.42–3.62)	1.89 (0.66–5.43)	2.02 (0.43–9.41)	4.07 (1.13–14.72)^*
	Shift Technician/Services	0.38 (0.14–1.03)	1.03 (0.34–3.11)	0.053 (0.14–2.07)	0.44 (0.11–1.69)	0.67 (0.19–2.32)	1.21 (0.43–3.37)	0.74 (0.25–2.18)	0.74 (0.25–2.18)	0.49 (0.05–4.45)	1.86 (0.47–7.31)

^*P≤0.05 ^**P≤0.01

The prevalence of left shoulder pain is significantly higher in powered industrial/transportation truck driving tasks compared to computer tasks, with approximately four times the discomfort. In these tasks, subjects are exposed to prolonged periods of moving their arms and maintaining postures, which can contribute to shoulder pain. Furthermore, the presence of left shoulder abnormalities (40.5% in Fig. 1) can be exacerbated by workplace risk factors, intensifying the pain experienced.

Middle back pain is significantly more common in warehouse units, stickering workers, and load-unload worker tasks. In warehouse units, the prevalence is 2.3 times higher than in office units, while in stickering and load-unload tasks, it is 3.7 and 3.6 times higher, respectively, compared to working with computers. Contributing factors to back pain include prolonged bending, lifting or picking loads with awkward back postures, and exposure to whole-body vibration while driving powered industrial/transportation trucks. In load-unload and stickering workers, risk factors for middle back pain include back rotation, prolonged standing or sitting, awkward posture when handling product packs, and using manual jack pallets. Interestingly, these findings contradict the results of a study by Kanniappan et al. among sewing machine workers in the leather industry, which found that lower back pain was the most prevalent WMSDs [22]. In our study, lower back pain ranked second in terms of prevalence.

Foot pain is significantly higher in load-unload tasks (right foot 4.5 times) and powered industrial/transportation truck driver tasks (left foot 4 times) compared to working with computers. Factors such as exposure to whole-body vibration, prolonged periods of standing or sitting, and pedal pressure contribute to this pain. These findings are consistent with a study by Wang et al. among industrial employees in Beijing, China, which reported that foot pain prevalence was high among frontline industrial workers compared to other staff members [27].

Therefore based on the discomforts mentioned, implementing engineering control measures such as providing newer or renovated powered industrial trucks for drivers and operators, preparing adjustable ergonomic chairs with backrests for basic production line operators and stickering workers, and automating load-unload tasks with machines, robots, or creating adjustable platform for loading vehicles can be considered to reduce the prevalence and intensity of musculoskeletal issues in regions that have shown significant statistics as shown in Table 3. Additionally, as a secondary priority administrative control measures such as limb rehabilitation, worker rotation, work breaks, and on-site exercise can also help reduce the prevalence and intensity of pain in these regions.

4.3 Limitations and suggestions of study

One of the limitations of the present study was the selection of participants based on abnormalities or problems in their skeletal structure as documented in their medical checkup records, without evaluation by physiotherapists. Therefore, future studies should involve assessments by experts to ensure accurate identification of structural defects. Another limitation was the restriction of the study population to a single industry. To enhance the generalizability of results, future research could include participants from all detergent industries. Additionally, the study only provided information on the frequency of abnormalities in body areas and general scoring of limb classification. Future research should aim to specify the type of abnormality in each region, such as lordosis, or kyphosis, and provide scoring for each area.

5 Conclusion

Approximately 10% of individuals experience poor structural status, which, along with workplace risk factors, contributes to pain in the right shoulder and middle back regions. Over a third of long-term standing workers and powered industrial/transportation truck drivers experience pain in the left knee and lower back (lumbar) regions. Additionally, over half of individuals experience neck pain due to neck flexion. Office workers have 2.7 to 6.6 times higher rates of neck pain compared to other workers involved in production, attributed to flexion, twisting, and maintaining a static neck position. Conversely, production workers have 2.2 to 4 times higher rates of pain in the left shoulder, middle back, and feet, resulting from working at elevated heights, handling loads, prolonged standing, awkward postures, and pedal pressure.

Ethical approval

It should be noted that this article is taken from the research plan approved by the Research Assistant of Qazvin University of Medical Sciences under IR.QUMS.REC.1402.052.

Informed consent

Not applicable.

Conflict of interest

The authors declare they have no conflict of interest.

Footnotes

Acknowledgments

The authors would like to express their gratitude to employees and workers of the detergent industry for their sincere cooperation in the data collection process.

Funding

The Student Research Committee of Qazvin University of Medical Sciences supported this research with 28.20.24551 Contract number.

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Quantitative Variable
Variable		Mean±SD		Variable		Mean±SD
Age (year)		35.85 ± 7.75		Work of experience (year)		8.72 ± 6.56
Height (cm)		171.36 ± 9.89		Work time (Hour)		8.33 ± 0.48
Weight (kg)		77.35 ± 13.96
Qualitative Variable
Variable	Category	Frequency	Percentage	Variable	Category	Frequency	Percentage
Gender	Male	105	70.9%	Work unit	Office	45	30.4%
	Female	43	29.1%		Warehouse	73	49.3%
Marital Status	Single	31	20.9%		Production/Technical	30	20.3%
	Married	117	79.1%	Work task	Work with Computer	59	39.9%
Level of Education	Lesser than Diploma	32	21.6%		Basic Production Operator	15	10.1%
	Diploma	52	35.1%		Stickering Worker	15	10.1%
	Associate	6	4.1%		Load-Unloading Worker	17	11.5%
	Bachelor of Science	41	27.7%		Powered Industrial/ Transportation Truck Driver	19	12.8%
	Master of Science or Higher	17	11.5%		Shift Technician/Services	23	15.5%

		Univariate (CI 95%)
Independent Variables		Neck	Right Shoulder	Left Shoulder	Upper Back	Middle Back	Lower Back	Right Knee	Left Knee	Right Foot	Left Foot
Work Units	Office	1	1	1	1	1	1	1	1	1	1
	Warehouse	0.50 (0.24–1.09)	1.33 (0.57–3.10)	2.08 (0.87–5.0)	2.21 (0.92–5.30)	2.28 (1.00–5.19)^*	1.54 (0.70–3.38)	1.52 (0.67–3.44)	1.62 (0.73–3.60)	2.48 (0.65–9.44)	3.03 (0.81–11.30)
	Production/Technical	0.37 (0.14–0.95)^*	0.62 (0.19–2.0)	0.28 (0.06–1.43)	0.61 (0.17–2.21)	0.34 (0.08–1.35)	0.80 (0.29–2.25)	0.55 (.017–1.76)	0.38 (0.11–1.30)	1.55 (0.29–8.27)	3.50 (0.80–15.28)
Work Tasks	Computer Work	1	1	1	1	1	1	1	1	1	1
	Basic Production Operator	0.15 (0.4–0.58) ^**	0.21 (0.025–1.73)	0.25 (0.03–2.10)	0.21 (0.02–1.73)	0.23 (0.02–1.90)	0.57 (0.14–2.26)	0.15 (0.02–1.23)	0(0–0)	1.66 (0.29–9.54)	1.36 (0.24–7.52)
	Stickering Worker	1.63 (0.46–5.76)	1.46 (0.43–4.98)	0.88 (0.22–3.61)	2.56 (0.79–8.28)	3.67 (1.13–11.93)^*	1.52 (0.47–4.90)	0.53 (0.13–2.08)	0.76 (0.21–2.72)	0.77 (0.08–0.71)	0(0–0)
	Load–Unloading Worker	0.24 (0.07–0.79)^*	0.90 (0.25–3.19)	1.93 (0.59–6.22)	1.60 (0.50–5.07)	3.61 (1.17–11.14)^*	2.56 (0.85–7.71)	1.47 (0.48–4.47)	1.87 (0.62–5.61)	4.5 (1.12–18.04)^*	2.72 (0.67–11.05)
	Powered Industrial/ Transportation Truck Driver	0.81 (0.28–2.34)	1.71 (0.57–5.15)	3.93 (1.32–11.70)^*	1.71 (0.56–5.14)	2.89 (0.98–8.53)	1.65 (0.57–4.81)	1.23 (0.42–3.62)	1.89 (0.66–5.43)	2.02 (0.43–9.41)	4.07 (1.13–14.72)^*
	Shift Technician/Services	0.38 (0.14–1.03)	1.03 (0.34–3.11)	0.053 (0.14–2.07)	0.44 (0.11–1.69)	0.67 (0.19–2.32)	1.21 (0.43–3.37)	0.74 (0.25–2.18)	0.74 (0.25–2.18)	0.49 (0.05–4.45)	1.86 (0.47–7.31)