Comparison of Exposure Time to Hazards Between Pain Complainants and Non-Complainants Among Food Manufacturing Production Workers, and Factors Influencing Musculoskeletal Pain

Abstract

This study examines the hazards faced by food manufacturing workers during their daily work hours and compares the exposure time between pain complainants and non-complainants. Also, this study identifies key factors influencing musculoskeletal pain. This study selected 552 food manufacturing workers and compared the exposure time to hazards between groups using t-tests. Additionally, this study conducted a logistic regression analysis to identify factors contributing to musculoskeletal pain, considering worker-related factors (gender, age, work experience, working hours per week, occupation, and industry type) and hazard exposure levels (physical, biochemical, and ergonomic hazards) as independent variables. The results of the exposure time analysis for hazards indicated that workers were most exposed to standing or repetitive movements, followed by awkward postures, handling heavy loads, and vibration. The mean comparison test of hazard exposure time between groups revealed that workers with musculoskeletal pain exhibited the greatest difference in low-temperature exposure time compared to workers without pain, followed by awkward postures and handling heavy loads. Logistic regression analysis showed that musculoskeletal pain increases with years of work experience and exposure time to handling heavy loads. In addition, females were more likely to complain of pain in the upper and lower limbs than males, and older workers were more likely to complain of back pain. Meanwhile, vibration exposure time increased the likelihood of upper limb pain, and low-temperature exposure time increased the likelihood of lower limb pain. The analysis of factors affecting musculoskeletal pain among food manufacturing workers showed that improvements are needed to focus on a universal design that considers female and older workers. Furthermore, this study’s results can help prioritize improvements and provide baseline data for developing tailored strategies and preventive measures.

1. Introduction

1.1. Purpose of Study

Food manufacturing, as defined by the Korean Standard Industrial Classification (KSIC), involves industrial processes that transform outputs from agriculture, forestry, and fisheries into food products and animal feeds intended for human or animal consumption [1]. In 2022, a total of 3,988,609 manufacturing workers in Korea signed up for industrial accident compensation insurance. Among them, 338,515 workers, or 11.8%, were involved in the food manufacturing industry. Additionally, out of 4696 victims of musculoskeletal disease in the manufacturing sector, 243, or 5.4%, were from the food manufacturing industry [2].

Manufacturing workers face a range of hazards during their tasks [3,4,5]. Workers may encounter physical hazards during food manufacturing and processing, including noise, vibration, and extreme temperatures [3,6,7]. They may be exposed to biochemical hazards such as grain dust, spice vapors, and cleaning chemicals [8,9,10,11]. Additionally, they are commonly exposed to ergonomic hazards during work, such as awkward posture, repetitive motion, and handling heavy loads. These factors are recognized as contributing to musculoskeletal disorders, which are among the common occupational health issues in the food manufacturing industry [12]. However, comprehensive research on exposure to hazards targeting production workers is still lacking. Comparing exposure to hazards between workers who complain of musculoskeletal pain and those who do not may help in understanding the factors that influence musculoskeletal pain. First, this study compares hazard exposure between workers with and without musculoskeletal pain. This research seeks to answer the following questions: “Is there a difference in the level of exposure to hazardous factors between those who complain of musculoskeletal pain and those who do not complain?” and “What are the hazardous factors that workers are frequently exposed to during working hours?”

Musculoskeletal disorders cause sleep problems, burnout, decreased physical activity performance, and decreased worker productivity during work [13,14,15]. Worker-related characteristics that affect musculoskeletal disorders are known to include gender, age, working hours, and work experience [4,5,16]. However, there is a lack of research analyzing the relative priorities of factors affecting musculoskeletal pain by thoroughly considering worker-related characteristics and hazardous factors. Therefore, the second purpose of this study is to comprehensively consider the general characteristics and hazards of food manufacturing workers and to identify the main factors affecting musculoskeletal pain. That is, this study aims to answer the following questions: “When considering worker-related factors and exposure levels to hazards, what are the main factors affecting musculoskeletal pain?” and “What characteristics of pain complaints do groups with statistically significant differences have compared to the reference group among the main factors of pain?”

1.2. Theoretical Background

1.2.1. Hazards of Food Manufacturing Production Workers

Exposure to physical hazards includes high noise levels during food processing and vibrations from power hand tools [6,13]. Additionally, workers are exposed to low- and high-temperature environments during manufacturing and storage [3,6]. Exposure to biochemical hazards includes grain dust, flavoring vapors, contact with cleaning preservatives and chemicals, and exposure to infections during meat and poultry processing [8,9,10,11]. Exposure to ergonomic hazards includes awkward posture, standing posture, handling heavy loads, excessive force, and repetitive motion during processing [4,5,16,17,18,19].

1.2.2. Research Variables Related to Hazards

This study selected research variables from the Korean Working Conditions Survey (KWCS) questionnaire based on its specific research purpose. The KWCS’s hazards-related questions investigate the respondents’ subjective responses to the degree of exposure during the day [20]. The KWCS and the European Working Conditions Survey define the working environment by identifying three main categories of hazards: (1) Physical hazards. (2) Biochemical hazards. (3) Ergonomic hazards [20,21].

Exposure time and intensity are used as research variables to analyze musculoskeletal pain hazards [20]. However, since the subjects of the KWCS’s research perform various daily tasks depending on the company’s size or job type, it may be difficult to remember their subjective evaluation of the intensity of exposure to hazards for each task. Therefore, the questionnaire items in the KWCS only investigate the degree of exposure time to hazards. The U.S. Occupational Safety and Health Administration (OSHA) also categorizes exposure time into three zones: less than 2 h, 2 to 4 h, and more than 4 h [22,23].

1.2.3. Musculoskeletal Pains

According to previous studies, musculoskeletal pain is associated with workers and their work characteristics. It is known that women report higher rates of upper limb pain complaints than men [16], and the likelihood of experiencing musculoskeletal pain increases with age [24]. Furthermore, as the length of work experience increases, complaints of musculoskeletal pain also rise [16,24,25], and longer weekly working hours correlate with higher complaint rates [16,24]. Additionally, there are variations in complaint rates based on the type of industry, with the complaint rate in the food manufacturing industry being higher than that in the mining industry [2].

The complaint rate for workers in different occupations varies depending on the type of job or task [26]. Repetitive and excessive tasks that involve a specific body part can cause musculoskeletal disorders [15]. Food manufacturing workers have a high rate of complaining of musculoskeletal pain when working in a low-temperature environment [3]. Exposure to vibrations when using power hand tools can cause pain in the elbows, hands, and wrists [5]. Exposure to ergonomic hazards, such as awkward posture and standing posture, can also cause musculoskeletal pain [5,27]. Poultry processing workers frequently report upper limb pain caused by the fast work pace and repetitive motions [28]. Meat processing workers experience musculoskeletal pain related to awkward postures, manual handling of raw materials, excessive force, and heavy lifting during tasks like butchering, mixing, cutting, packing, and carrying [5,29].

2. Materials and Methods

2.1. Data Collection and Subjects

Raw data and questionnaire items were obtained from the KWCS [20]. This study extracted data for research purposes utilizing Korean Standard Industrial Classification (KSIC) [1] classification criteria. Among the research subjects, skilled workers, semi-skilled workers, equipment and machine operators, assembly workers, and unskilled laborers were classified as production workers.

Respondents with missing values for the research variables were excluded, resulting in 552 workers being selected as research subjects. In the raw data, the proportion of females was 57.8% and that of the males was 42.2%. In the data with missing values removed, the proportion of females was 59.1% and that of the males was 40.9%. The chi-square test for homogeneity between the raw data and the data with missing values removed did not find a significant difference between the two groups (chi-square = 0.207, p = 0.649).

2.2. Comparison Between Pain Complaints and Non-Complaints

This study estimated the duration of hazard exposure using responses from the questionnaires. This estimation followed the method described in detail by Kim et al. [16], and the distribution of daily exposure time to hazards was obtained. In addition, a t-test was conducted to assess differences in the average duration time between the group complaining of pain in the back, upper limbs, or lower limbs and the group not complaining of pain. The hazards included physical, biochemical, and ergonomic hazards. The t-test was conducted using SPSS version 27.0 with a significance level of 0.05.

2.3. Logistic Regression Analysis and Model Fit Test

Logistic regression analysis can identify key factors that affect musculoskeletal pain by comprehensively considering various factors. Additionally, it can determine the likelihood that a specific group is more prone to reporting musculoskeletal pain compared to a reference group, taking into account the identified key factors.

The dependent variable was musculoskeletal pain measured in each body part. In contrast, the independent variables consisted of worker-related factors and exposure levels to hazards. Worker-related factors included gender (male, female), age (≤40s, 50s, ≥60s), work experience (<3, 3–9, ≥10 years), working hours per week (≤40, >40 h), occupation (plant and machine operators and assemblers, craft and related trades workers, elementary workers), and industry type (manufacture of other food products, processing and preserving). The hazards included physical, biochemical, and ergonomic hazards. The exposure levels for each hazard are classified into three categories: under 2 h, 2–4 h, and 4 h or more of exposure [22,23].

The input method for the logistic regression analysis was the backward Wald method. The regression model’s explanatory power was assessed using Nagelkerke’s method. To evaluate the goodness of fit for the model, the Hosmer–Lemeshow test was conducted, and a significance probability of 0.05 or higher indicated an acceptable fit. Additionally, the accuracy of the predictions was expressed as a percentage (%).

2.4. Reliability Analysis

The reliability analysis results, as displayed in

Table 1. Results of reliability analysis of variables using Cronbach’s alpha.

Latent Variable	Initial Items	Removed Question Item	Final Items	Cronbach’s Alpha
Physical hazards	4		4	0.797
Biological hazards	5	Fumes and dust, Infection	3	0.913
Ergonomics hazards	6	Patient lifting/carrying, Sitting posture	4	0.593

, evaluate the consistency of the subjective survey data. For the physical hazard factors, the Cronbach’s alpha value was found to be 0.797. In the case of biochemical hazard factors, the items ‘fumes and dust’ and ‘infection’ were excluded from the analysis, and Cronbach’s alpha was 0.913. For ergonomic hazardous factors, the items ‘patient lifting/carrying’ and ‘sitting posture’ were also removed, yielding a Cronbach’s alpha of 0.593.

3. Results

3.1. Daily Exposure Time to Hazards

Figure 1 and

Table 2. Distribution and mean of hazard exposure time.

Factor	Hazard	Exposure Time		Exposure Time Distribution
		Mean *	SD	<2 h	2–4 h	≥4 h
Physical hazards	Vibration	2.432	2.314	58.2%	14.9%	27.0%
	Noise	1.778	1.927	73.0%	12.1%	14.9%
	High temperature	1.432	1.472	81.3%	9.1%	9.6%
	Low temperature	1.519	1.779	83.2%	5.8%	11.1%
Biochemical hazards	Vapor	0.880	0.374	98.2%	1.1%	0.7%
	Chemical contact	0.866	0.355	99.1%	0.5%	0.4%
	Tobacco smoke	0.847	0.218	99.5%	0.5%	0.0%
Ergonomics hazards	Awkward posture	2.766	2.308	45.3%	22.3%	32.4%
	Handling heavy loads	2.513	2.102	43.8%	32.8%	23.4%
	Standing posture	4.404	2.402	16.7%	21.2%	62.1%
	Repetitive motion	4.367	2.463	21.6%	17.0%	61.4%

Note: * hours per day, SD = standard deviation.

present the average daily exposure time and distribution of exposure for hazards. The hazard exposure analysis reveals that the highest exposure occurred in the standing posture, with an average of 4.404 h per day. This was closely followed by repetitive motion at 4.367 h daily. Other notable exposure times included awkward posture (2.766 h daily), handling heavy loads (2.513 h daily), vibration (2.432 h daily), noise (1.778 h daily), low temperature (1.519 h daily), high temperature (1.432 h daily), and exposure to fumes and dust (1.225 h daily). Overall, food manufacturing workers reported the longest exposure time for ergonomic hazards, followed by physical hazards and biochemical hazards. In the distribution of exposure time, the proportion of workers exposed for more than 4 h and those exposed for 2–4 h was also highest in ergonomic hazards, followed by physical and biochemical hazards.

3.2. Exposure Time to Hazards According to Musculoskeletal Pain

3.2.1. Hazard Exposure Related to Back Pain

Figure 2 and

Table 3. T-test results comparing daily hazard exposure between groups regarding back pain.

Factor	Hazard	No Pain		Back Pain		p-Value
		Mean **	SD	Mean **	SD
Physical hazards	Vibration	2.293	2.245	2.698	2.425	0.051
	Noise	1.659	1.833	2.005	2.080	0.054
	High temperature	1.350	1.424	1.587	1.550	0.081
	Low temperature	1.328	1.510	1.882	2.160	0.002 *
Biochemical hazards	Vapor	0.858	0.331	0.921	0.444	0.083
	Chemical contact	0.837	0.229	0.923	0.513	0.029 *
	Tobacco smoke	0.832	0.204	0.875	0.242	0.038 *
Ergonomic hazards	Awkward posture	2.448	2.155	3.371	2.469	<0.001 *
	Handling heavy loads	2.222	1.934	3.066	2.294	<0.001 *
	Standing posture	4.378	2.352	4.454	2.500	0.725
	Repetitive motion	4.303	2.491	4.488	2.411	0.404

Note: * significant at 0.05, ** hours per day, SD = standard deviation.

present the t-test results comparing daily hazard exposure between workers with back pain and those without. Among physical hazards, a significant difference was observed in the average daily exposure time between complainers and non-complainers only in the low-temperature environment (t = −3.155, p = 0.002). Specifically, the low-temperature exposure time for those complaining of back pain was 1.417 (1.882/1.328) times longer than that of those who did not complain. Among the biochemical hazards, there were significant differences in daily exposure times between groups, specifically regarding chemical contact (t = −2.199, p = 0.029) and tobacco smoke (t = −2.086, p = 0.038). Workers complaining of back pain were exposed to chemical contact 1.103 (0.923/0.837) times longer and to tobacco smoke 1.052 times longer than those who did not complain. In terms of ergonomic hazards, there was a significant difference between the groups regarding the daily exposure time to awkward posture (t = −4.546, p < 0.001) and handling heavy loads (t = −4.326, p < 0.001). Complainers experienced awkward posture 1.377 (3.371/2.448) times longer and handled heavy loads 1.380 times longer than non-complainers.

3.2.2. Hazard Exposure Related to Upper Limb Pain

Figure 3 and

Table 4. T-test results comparing daily hazard exposure between groups regarding upper limb pain.

Factor	Hazard	No Pain		Upper Limb Pain		p-Value
		Mean **	SD	Mean **	SD
Physical hazards	Vibration	2.241	2.184	2.672	2.450	0.030 *
	Noise	1.588	1.713	2.017	2.145	0.011 *
	High temperature	1.313	1.363	1.581	1.588	0.036 *
	Low temperature	1.264	1.438	1.838	2.901	<0.001 *
Biochemical hazards	Vapor	0.853	0.359	0.913	0.391	0.060
	Chemical contact	0.821	0.200	0.923	0.479	0.002 *
	Tobacco smoke	0.821	0.204	0.879	0.231	0.002 *
Ergonomic hazards	Awkward posture	2.344	2.082	3.293	2.468	<0.001 *
	Handling heavy loads	2.188	1.936	2.921	2.231	<0.001 *
	Standing posture	4.232	2.342	4.619	2.464	0.060
	Repetitive motion	4.117	2.495	4.367	2.463	0.007 *

Note: * significant at 0.05, ** hours per day, SD = standard deviation.

show the t-test results comparing daily hazard exposure between workers who complain of upper limb pain and those who do not. Regarding physical hazards, workers between groups show significant differences in their exposure times to vibration (t = −2.181, p = 0.030), noise (t = 2.553, p = 0.011), high temperature (t = −2.102, p = 0.036), and low temperature (t = −3.666, p < 0.001). Workers with upper limb pain experienced a daily exposure time that was 1.454 (1.838/1.264) times longer for low temperature, 1.270 times longer for noise, 1.204 times longer for high temperature, and 1.192 times longer for vibration than those without pain.

Among biochemical hazards, there was a significant difference in average daily exposure time between groups for chemical contact (t = −3.131, p = 0.002) and tobacco smoke (t = −3.052, p = 0.002). Those who reported upper limb pain had 1.124 times longer exposure to chemical contact and 1.071 times longer exposure to tobacco smoke compared to those who did not.

In terms of ergonomic risks, there was a significant difference in daily exposure time between complainers and non-complainers of upper limb pain in awkward posture (t = −4.807, p < 0.001), handling heavy loads (t = −4.058, p < 0.001), and repetitive motion (t = −2.690, p = 0.007). Complainers of upper limb pain were exposed 1.405 times more in awkward posture, 1.335 times more in handling heavy loads, and 1.061 times more in repetitive motion than non-complainers.

3.2.3. Hazard Exposure Related to Lower Limb Pain

Figure 4 and

Table 5. T-test results comparing daily hazard exposure between groups regarding lower limb pain.

Factor	Hazard	No Pain		Lower Limb Pain		p-Value
		Mean **	SD	Mean **	SD
Physical hazards	Vibration	2.294	2.227	2.910	2.546	0.016 *
	Noise	1.643	1.791	2.244	2.283	0.008 *
	High temperature	1.378	1.435	1.619	1.585	0.108
	Low temperature	1.343	1.547	2.125	2.321	0.001 *
Biochemical hazards	Vapor	0.869	0.359	0.917	0.421	0.207
	Chemical contact	0.854	0.337	0.907	0.411	0.191
	Tobacco smoke	0.841	0.213	0.867	0.235	0.255
Ergonomic hazards	Awkward posture	2.485	2.146	3.732	2.582	<0.001 *
	Handling heavy loads	2.310	1.990	3.210	2.323	<0.001 *
	Standing posture	4.301	2.351	4.757	2.549	0.063
	Repetitive motion	4.223	2.465	4.861	2.401	0.011 *

Note: * significant at 0.05, ** hours per day, SD = standard deviation.

display the t-test results comparing daily hazard exposure among workers who complain of lower limb pain and those who do not. Among physical hazards, the daily exposure times showed significant differences for vibration (t = −2.437, p = 0.016), noise (t = −2.699, p = 0.008), and low temperature (t = −3.532, p = 0.001). Workers who reported lower limb pain experienced daily exposure times that were 1.582 times longer for low temperature, 1.366 times longer for noise, and 1.269 times longer for vibration than those without complaints. No significant differences were found between the groups regarding biochemical hazards.

Regarding ergonomic hazards, significant differences were found between groups in daily exposure time for awkward posture (t = −4.909, p < 0.001), handling heavy loads (t = −3.917, p < 0.001), and repetitive motion (t = −2.552, p = 0.011). Workers reporting lower limb pain indicated that their daily exposure time was 1.502 (3.732/2.485) times longer for awkward posture, 1.390 times longer for handling heavy loads, and 1.151 times longer for repetitive motion than those who did not report pain.

3.3. Logistic Regression Analysis on Musculoskeletal Pain

3.3.1. Logistic Regression Analysis on Back Pain

The logistic regression model for back pain had a Nagelkerke value of 0.130. It demonstrated that the model’s explanatory power was satisfactory. Additionally, the model fit was considered adequate, as reflected by a chi-square value of 3.473 and a significance level of 0.901. The prediction accuracy of the model was reported at 69.0%.

Table 6. Logistic regression analysis on back pain.

Variables	N	Prevalence Rate (%)	B	p-Value	OR	95% C.I. for OR
						Lower	Upper
Age				0.012 *
$\le$40s (ref)	179	25.7%
50s	181	33.7%	0.430	0.085	1.537	0.943	2.505
$\ge$60s	192	43.2%	0.766	0.003 *	2.152	1.299	3.564
Work experience				0.007 *
<3 years (ref)	150	26.7%
3–9 years	190	28.9%	0.102	0.689	1.108	0.671	1.828
$\ge$10 years	212	44.8%	0.725	0.006 *	2.064	1.228	3.470
Industry type
Manufacture of other food products (ref)	333	32.4%
Processing and storage	219	37.4%	0.611	0.003 *	1.841	1.231	2.755
Handling heavy loads				<0.001 *
<2 h (ref)	242	24.3%
2–4 h	181	37.1%	0.713	0.001 *	2.041	1.317	3.163
$\ge$4 h	129	47.3%	1.261	<0.001 *	3.529	2.163	5.758
Constant			−2.209	<0.001 *	0.110

Note: * Significant at 0.05, ref = reference, OR = odds ratio, C.I. = confidence interval.

shows the key factors contributing to back pain in food manufacturing workers based on logistic regression analysis, considering the general characteristics of workers and physical, biochemical, and ergonomic hazards. These contributors include age (p = 0.012), work experience (p = 0.007), industry type (p = 0.003), and handling heavy loads (p < 0.001). Research shows that individuals in their 60s are 2.152 times more likely to report back pain compared to those in their 40s or younger (p = 0.003). Moreover, workers with work experience of ≥10 years are 2.064 times more likely than those with <3 years (p = 0.006). Workers who handle heavy loads for 2–4 h are 2.041 times more likely to experience back pain than those under 2 h (p = 0.001), while workers handling loads for 4 h or more are 3.529 times more likely than those under 2 h (p < 0.001).

3.3.2. Logistic Regression Analysis on Upper Limb Pain

Table 7. Logistic regression analysis on upper limb pain.

Variables	N	Prevalence Rate (%)	B	p-Value	OR	95% C.I. for OR
						Lower	Upper
Gender
Male (ref)	226	33.6%
Female	326	51.8%	1.807	<0.001 *	2.966	2.005	4.389
Work experience				<0.001 *
<3 years (ref)	150	34.7%
3–9 years	190	42.1%	0.444	0.065	1.558	0.972	2.497
$\ge$10 years	212	53.3%	0.964	<0.001 *	2.621	1.634	4.206
Vibration				0.041 *
<2 h (ref)	321	40.5%
2–4 h	82	56.1%	0.597	0.031 *	1.816	1.057	3.119
$\ge$4 h	149	46.3%	−0.136	0.573	0.873	0.545	1.399
Low temperature				0.063
<2 h (ref)	459	41.2%
2–4 h	32	56.3%	0.195	0.627	1.215	0.554	2.664
$\ge$4 h	61	62.3%	0.741	0.019 *	2.098	1.128	3.900
Handling heavy loads				0.002 *
<2 h (ref)	242	35.1%
2–4 h	181	48.6%	0.571	0.009 *	1.769	1.153	2.716
$\ge$4 h	129	55.2%	0.852	0.001 *	2.345	1.412	3.896
Constant			−1.970	<0.001 *	0.139

Note: * Significant at 0.05, ref = reference, OR = odds ratio, C.I. = confidence interval.

presents the model’s results regarding upper limb pain. The model demonstrated satisfactory explanatory power with a 0.175 Nagelkerke value. Moreover, the result regarding model fit was considered suitable, with a chi-square value of 4.527 and a significance level of 0.807. The model’s prediction accuracy was 65.9%.

In Table 7, the factors influencing upper limb pain were found to be gender (p < 0.001), work experience (p < 0.001), vibration (p = 0.041), and handling heavy loads (p = 0.002). Female was 2.966 times more likely than male (p < 0.001). Additionally, workers with over 10 years of work experience were 2.621 times more likely to be affected than those under 3 years (p < 0.001). Workers exposed to vibration for 2–4 h were 1.816 times more likely to experience upper limb pain than those under 2 h (p = 0.031). Workers who handled heavy loads for 2–4 h were 1.769 times more likely than those under 2 h (p = 0.009), whereas workers for over 4 h were 2.345 times greater than those under 2 h (p = 0.001).

3.3.3. Logistic Regression Analysis on Lower Limb Pain

Table 8. Logistic regression analysis on lower limb pain.

Variables	N	Prevalence Rate (%)	B	p-Value	OR	95% C.I. for OR
						Lower	Upper
Gender
Male (ref)	226	15.0%
Female	326	27.6%	0.981	<0.001 *	2.667	1.649	4.313
Work experience				0.001 *
<3 years (ref)	150	16.0%
3–9 years	190	21.1%	0.455	0.130	1.576	0.875	2.838
$\ge$10 years	212	28.3%	1.077	<0.001 *	2.936	1.614	5.340
Working hours
$\le$40 h (ref)	246	22.8%
>40 h	306	22.2%	−0.482	0.039 *	0.618	0.390	0.977
Low temperature				0.013 *
<2 h (ref)	459	19.0%
2–4 h	32	43.8%	1.028	0.011 *	2.795	1.267	6.165
$\ge$4 h	61	37.7%	0.597	0.066	1.817	0.962	3.433
Handling heavy loads				<0.001 *
<2 h (ref)	242	15.3%
2–4 h	181	22.7%	0.565	0.035 *	1.759	1.040	2.975
$\ge$4 h	129	35.7%	1.276	<0.001 *	3.582	2.025	6.336
Constant			−3.084	<0.001 *	0.046

Note: * Significant at 0.05, ref = reference, OR = odds ratio, C.I. = confidence interval.

shows the logistic regression analysis results on lower limb pain. The model exhibited satisfactory explanatory power with a 0.167 Nagelkerke value. Furthermore, the result regarding model fit was considered suitable, as evidenced by the 5.151 chi-square value and 0.741 significance value. The model’s prediction accuracy was 77.4%.

The key factors affecting lower limb pain were gender (p < 0.001), work experience (p = 0.001), weekly working hours (p = 0.039), low temperature (p = 0.013), and handling heavy loads (p < 0.001). Females were 2.667 times more likely to report lower limb pain than males (p < 0.001), and workers with over 10 years of work experience were 2.936 times more likely than those under 3 years (p < 0.001). Workers exposed to low temperatures for 2 to 4 h are 2.795 times more likely to report lower limb pain than those exposed for less than 2 h (p = 0.011). Additionally, workers who handle heavy loads for 2 to 4 h are 1.759 times more likely than those under 2 h (p = 0.035), while workers handling heavy loads for 4 h or more are 3.582 times more likely than those under 2 h (p < 0.001). Conversely, contrary to general results, workers who worked less than 40 h were 1.618 (1/0.618) times more likely than those over 40 h (p = 0.039).

4. Discussion

This study investigated the hazards to which food manufacturing workers are most exposed during their daily working hours and analyzed whether there were differences in daily exposure times between those who complained of musculoskeletal pain and those who did not. Food manufacturing workers reported the longest exposure time for ergonomic hazards, followed by physical hazards and biochemical hazards. The factors with a daily exposure time of more than 2 h were the highest exposure in the standing position (4.404 h per day), followed by repetitive motion (4.367 h per day), awkward posture (2.766 h per day), handling heavy loads (2.513 h per day), and vibration (2.432 h per day). The results of this study indicate that efforts are needed to improve repetitive motion exposure for more than 4 h within the OSHA hazard zone and to reduce awkward posture, handling of heavy loads, and vibration factors for more than 2 h, which falls within the caution zone.

Looking at the three factors with the most significant difference, workers who reported musculoskeletal pain experienced significantly longer exposure times to low temperatures (1.417 to 1.582 times) than those who did not, followed by reports of awkward posture (1.377 to 1.502 times) and handling heavy loads (1.335 to 1.390 times). These findings suggest that reducing exposure to low temperatures, addressing awkward posture, and minimizing heavy lifting can effectively alleviate musculoskeletal pain.

According to the results of the logistic regression analysis regarding back pain, workers in their 60s are 2.152 times more likely than those in their 40s or younger (p = 0.003). These results confirm that musculoskeletal pain affects older workers more than younger workers [30]. Muscle mass progressively decreases with age, and the time to recover from musculoskeletal injuries also increases [30]. Workers with over 10 years of experience are 2.064 times more likely than those under 3 years (p = 0.006). This supports findings that prolonged exposure to the same work environment can raise the likelihood of musculoskeletal pain due to the physical strain it imposes on the body [31]. Workers in processing and storage are 1.841 times more likely than those in other food manufacturing jobs (p = 0.003). This aligns with findings that seafood and frozen food processing workers report back pain caused by awkward posture and poor working conditions [32,33]. Workers who handle heavy loads for 2–4 h are 2.041 times more likely than workers under 2 h (p = 0.001), while workers handling loads for 4 h or more are 3.529 times more likely than workers under 2 h (p < 0.001). This is consistent with research findings that seafood processing workers may suffer from musculoskeletal pain, especially back pain, due to the handling of heavy loads [34].

The results of the logistic regression analysis on upper limb pain showed that females were 2.966 times more likely than males (p < 0.001). This is consistent with research findings that female workers generally report higher rates of musculoskeletal pain than male workers [35,36,37,38]. Workers with over 10 years of work experience were 2.621 times more likely than those under 3 years (p < 0.001). This aligns with the findings indicating that workers with more years of work experience in the automobile manufacturing and nursing industries were more prone to report pain [16,17,18,19,20,21,22,23,24,25]. Workers exposed to vibration for 2–4 h were 1.816 times more likely than those under 2 h (p = 0.031). This supports findings that hand–arm vibration syndrome arises from using vibrating equipment, such as knives, in the food processing industry [39]. Workers who handled heavy loads for 2–4 h were 1.769 times more likely than those under 2 h (p = 0.009), whereas workers who worked for 4 h or more were 2.345 times greater than those under 2 h (p = 0.001). This was consistent with the result that construction workers handling heavy loads for extended periods are more likely to complain of upper limb pain [24].

As a result of key factors affecting lower limb pain, females present a rate 2.667 times greater than males (p < 0.001). This supports the finding that women have a higher rate of reporting lower limb pain, including pain in the hips and thighs, as well as upper limb pain, such as in the neck, shoulders, and wrists, when compared to men [40]. Workers with $\ge$10 years of work experience show 2.936 times greater pain in lower limbs than those under 3 years (p < 0.001). In contrast, workers exposed to low temperatures for 2–4 h experience 2.795 times more lower limb pain than those under 2 h (p = 0.011). This aligns with the finding that musculoskeletal pain in food manufacturing workers was influenced by working in a low-temperature environment [3]. Workers with long work experience tend to complain of lower limb pain, including back and upper limb pain. Workers who handle heavy loads for 2 to 4 h are 1.759 times more likely to complain than those under 2 h (p = 0.035). Also, workers handling heavy loads for 4 h or more are 3.582 times more likely to complain than those under 2 h (p < 0.001). Workers who handle heavy loads for extended periods are more likely to experience pain in their back, upper limbs, and lower limbs. Conversely, contrary to general results, lower limb pain in workers who worked less than 40 h per week was 1.618 (1/0.618) times more than in workers over 40 h (p = 0.039).

The food manufacturing industry has a higher percentage of women, older workers, and employees with extended work experience than other manufacturing sectors [41,42,43]. This study found that workers with extensive work experience are the primary contributors to musculoskeletal pain. Additionally, it was discovered that women significantly influence pain in both the upper and lower limbs. Furthermore, older workers were also found to be a contributing factor to back pain. Women and older workers often have lower physical abilities, which can lead to increased strain and pain, even in the same working conditions. The results of this study on musculoskeletal pain suggest that tailored improvements are needed to ensure that physically weak women and elderly food manufacturing workers can work safely. Furthermore, improvement policies that extend the concept of universal design are necessary to enable physically weaker workers to work comfortably [44,45]. Research has shown that exposure time to handling heavy loads is a significant factor contributing to back pain, as well as pain in the upper and lower limbs. This suggests that workers in these situations may require assistance, such as power devices or wearable assistive tools, to manage heavy loads more effectively [46]. In addition, vibration exposure affects upper limb pain, while low-temperature exposure affects lower limb pain. Therefore, support tools [47] that reduce vibration and protective clothing [48] for low temperatures are necessary.

This study has some limitations. First, musculoskeletal pain is assessed using a subjective questionnaire rather than through medical diagnosis. Therefore, caution must be taken when interpreting the factors that impact patients with musculoskeletal diseases. Second, exposure time and intensity are often used as research variables related to the hazards of musculoskeletal pain, but the KWCS questionnaire only examined the extent of exposure time. Therefore, future studies need to consider both exposure time and exposure intensity. Third, since this study utilized data on food manufacturing workers from the KWCS, it may not encompass all occupations and jobs. Therefore, caution is necessary in interpretation. Fourth, in this study, the daily working hours factor that affects lower limb pain differed from the general results, and the cause could not be inferred. Therefore, further research on this is required. Despite these limitations, this study identified the level of exposure to hazards among food manufacturing workers and identified key factors affecting musculoskeletal pain.

5. Conclusions

Food manufacturing workers reported being exposed to standing and repetitive motions for more than four hours a day, as well as to awkward posture, handling heavy loads, and vibration for two hours a day. Comparing exposure to risk factors between complainers and non-complainers of musculoskeletal pain suggests that reducing exposure to low temperature, addressing awkward posture, and minimizing handling heavy loads can effectively alleviate musculoskeletal pain.

The analysis of factors affecting musculoskeletal pain reveals that improvements are needed that focus on universal design, considering vulnerable groups such as women, those with long-term work experience, and the elderly. Additionally, assistive devices, protective gear, and clothing are necessary to reduce exposure time when handling heavy loads, vibrations, and low temperatures, all of which are significant factors contributing to pain.

The findings from this study may help prioritize improvements for the identified influencing factors. They can serve as foundational data for developing customized improvement strategies and preventive measures against musculoskeletal pain in food manufacturing workers.