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Work-related musculoskeletal disorders (WMSDs) are health problems of the locomotor apparatus that are induced or aggravated by work. If the mechanical workload exceeds the musculoskeletal system’s load-bearing capacity, injuries to ligaments, bones, muscles, and tendons are typical consequences (1). Coal mining is characterized by heavy workloads, and miners typically engage in prolonged postures requiring repetitive operations and heavy load carrying, which can easily trigger local muscle fatigue and ultimately lead to the development of WMSDs. According to research from the China CDC, the prevalence of WMSDs in the population working in key industries was estimated to be 41.2% (2). One survey of Indian underground coal miners reported that approximately 65.45% of them complained of WMSD pain (3). Data from another study in China indicate that the total symptom prevalence of WMSDs among coal miners has reached 75.6%; the five body parts with the highest symptom prevalence include the lower back (54.1%), neck (42.1%), shoulder (37.2%), knee (29.1%), and back (28.1%), in order of prevalence (4). The “National Occupational Disease Prevention and Control Plan (2021–2025)” prioritizes research on the causes of WMSD damage (5).
China is vigorously developing new quality productive forces (NQPF) and upgrading traditional industries with AI and other technologies. In 2020, the Chinese government released the “Guiding Opinions on Accelerating the Intelligent Development of Coal Mines.” This document proposed that all coal mines will realize intelligent mining by 2035. The essence of intelligent mining is using intelligent equipment to replace miners engaged in dangerous, heavy, and repetitive labor. One example is the use of inspection robots. According to the “Typical Case Collection of Intelligent Construction in Coal Mines” published by the China National Energy Administration in 2023, inspectors at the Tang Kou coal mine previously inspected the mine hoist 22 times daily, with each inspection taking 30 minutes. With the intelligent inspection robot, the robot completes all on-site inspections, and the inspector only needs to operate it remotely from the control room (6). Therefore, intelligent mining can potentially reduce the prevalence of WMSDs among miners. However, relevant literature is lacking, and this study aims to investigate this issue from the perspective of intelligent equipment usage (IEU).
In June–July 2023, this study used single-stage cluster sampling to select underground miners from six coal mines with different levels of intelligent automation in Shanxi Province for a questionnaire survey. Three coal mines were designated as intermediate intelligent coal mines and three as primary intelligent coal mines. A total of 2,245 questionnaires were collected, of which 2,165 were deemed valid. All participants were male and employed in underground production units, such as excavation, mining, maintenance, ventilation, and transport teams. According to labor law in China, female workers are prohibited from working in underground mines. All questionnaires were electronic and distributed to miners through the coal mine human resources departments. The entire process was completed under the guidance of investigators at the participants’ workplaces.
The questionnaire collected data on four areas: IEU, workload, WMSDs prevalence, and pain level. Self-reported IEU data were collected using questionnaires covering two aspects: 1) direct IEU (“I work with a lot of intelligent equipment,” IEU-D); and 2) indirect IEU (“There is a lot of intelligent equipment in my workplace,” IEU-I). Questionnaire responses were scored on a 5-point Likert scale (from 1 to 5), ranging from “very inconsistent” to “very consistent.” Scores were classified as low (1 and 2), medium (3), and high (4 and 5). The workload scale, adapted from a similar scale developed by Johansson et al. in 1991 (7), comprised four questions: 1) “In the course of your work, are you exposed to inappropriate work postures (e.g., bending, twisting)?”; 2) “Do you have to lift heavy objects at work?”; 3) “Do you sweat every day at work?”; and 4) “Do you think your job is unsafe?” Responses were scored on a 5-point scale (from 1 to 5), ranging from “never” to “always.” Miners’ WMSDs prevalence was measured using the Chinese Musculoskeletal Disorder Questionnaire (CMDQ) developed by the China CDC. WMSDs were defined as pain, numbness, or limited movement in a relevant body part lasting >1 week, or lasting <1 week but occurring >2 times in the past 12 months; response options were yes or no. Miners’ pain levels in different body parts were assessed using a visual analog scale (VAS). Scores ranged from 0 to 10, indicating “no pain” to “unbearable and severe pain.” The questionnaire’s Cronbach’s α coefficient was 0.858, and its Kaiser-Meyer-Olkin (KMO) value was 0.719, demonstrating good reliability and validity.
Table 1 shows the miners’ WMSDs prevalence rates at different IEU-D levels. Higher levels of IEU-D correspond to lower WMSDs prevalence rates. Based on the odds ratio (OR), the differences in WMSDs prevalence rates were statistically significant at different levels of intelligent automation. Compared with the low IEU-D group, the high IEU-D group showed a 7.49% average decrease in WMSDs prevalence rates. The most notable improvements were in the knees, upper back, and neck. Compared with the low IEU-D group, the prevalence of WMSDs in these areas decreased by more than 8% in the high IEU-D group.
Body part IEU-D Number Number of WMSDs Rate of WMSDs（%） OR (95% CI) P Neck Low 559 417 74.597 1.473 <0.050 Medium 729 544 74.623 1.475 <0.050 High 877 584 66.591 1 Shoulders Low 559 360 64.401 1.254 <0.050 Medium 729 476 65.295 1.304 <0.050 High 877 518 59.065 1 Upper back Low 559 348 62.254 1.409 <0.050 Medium 729 465 63.786 1.504 <0.050 High 877 473 53.934 1 Lower back Low 559 381 68.157 1.336 <0.050 Medium 729 493 67.627 1.304 <0.050 High 877 540 61.574 1 Wrists/hands Low 559 316 56.530 1.346 <0.050 Medium 729 406 55.693 1.301 <0.050 High 877 431 49.145 1 Elbows Low 559 291 52.057 1.375 <0.050 Medium 729 374 51.303 1.334 <0.050 High 877 387 44.128 1 Hips/thighs Low 559 260 46.512 1.341 <0.050 Medium 729 331 45.405 1.282 <0.050 High 877 345 39.339 1 Knees Low 559 339 60.644 1.429 <0.050 Medium 729 437 59.945 1.388 <0.050 High 877 455 51.881 1 Ankles/feet Low 559 276 49.374 1.374 <0.050 Medium 729 350 48.011 1.302 <0.050 High 877 364 41.505 1 Abbreviation: WMSDs=work-related musculoskeletal disorders; IEU-D=direct intelligent equipment usage; OR=odds ratio; CI=confidence interval. Table 1. WMSDs prevalence of miners with different levels of IEU-D in Shanxi Province, China, 2023.
Table 2 describes the differences in body pain at different levels of IEU-D, suggesting that IEU can significantly improve pain associated with WMSDs. Compared to the low IEU-D group, the average pain in all body parts was 13.69% lower in the high IEU-D group. The most pronounced pain reduction occurred in the following body parts, ranked in order of pain severity: hips/thighs, elbows, knees, upper back, ankles/feet, and neck. Pain decreases in these areas were all greater than 10%.
Body part Low IEU-D Medium IEU-D High IEU-D F P Number Pain degree* Number Pain degree* Number Pain degree* Neck 559 3.322±2.773 729 3.361±2.651 877 2.949±2.743 5.540 <0.01 Shoulders 559 2.964±2.908 729 2.912±2.741 877 2.697±2.842 1.906 <0.01 Upper back 559 2.900±2.918 729 2.925±2.810 877 2.452±2.803 6.946 <0.01 Lower back 559 3.301±2.978 729 3.296±2.937 877 3.038±3.034 1.985 <0.01 Elbows 559 2.422±2.847 729 2.403±2.786 877 2.023±2.755 5.073 <0.01 Wrists/hands 559 2.599±2.888 729 2.517±2.823 877 2.235±2.782 3.441 <0.01 Hips/thighs 559 2.197±2.851 729 2.156±2.870 877 1.778±2.674 5.307 <0.01 Knees 559 2.995±3.069 729 2.864±2.980 877 2.505±2.967 5.314 <0.01 Ankles/feet 559 2.270±2.875 729 2.225±2.841 877 1.961±2.790 2.656 <0.01 Abbreviation: WMSDs=work-related musculoskeletal disorders; IEU-D=direct intelligent equipment usage; SD=standard deviation.
* Pain degree: Mean±SD.Table 2. WMSDs pain with different levels of IEU-D in Shanxi Province, China, 2023.
The primary occupational hazards for WMSDs include heavy physical loads, repetitive operations, and poor posture, categorized as workload (8). Age, position, and length of service are definitive individual influencing factors (9). Although not included in this study, these individual factors can influence regression results and were controlled for in the analysis. The following regression equation describes the moderation:
$$ P = \beta_0 + \alpha_1A + \alpha_2L + \alpha_3J + \beta_1W + \beta_2I + \beta_3WI + \varepsilon $$ (1) P value represents the degree of pain in different body parts. Age (A), length of service (L), and job position (J) are control variables. Workload (W) is the independent variable. The moderator variable is IEU level (I).
$\beta_ 1$ and$\beta_ 2$ represent the direct effects of W and I, and$\beta_ 3$ represents the moderator effect. If$\beta_ 3 \ne 0$ , it proves that there is a moderating effect.It can be seen in Table 3, we gradually added the following variables to the stepwise regression model: independent variable (W), moderator variable (I), and interaction term (W×I). From Model 1 to Model 4, the effects of all variables were significant, with Model 4 demonstrating the most significant results.
$ \beta_ 1 = 0.068 $ and$\beta _2 = - 0.068$ show that workload positively affected the degree of body pain, while IEU negatively affected it.$\beta_ 3 = - 0.183$ indicates that IEU acts as a negative moderator, diminishing the effect of workload on the degree of body pain.Dependent variable Coefficient Model 1 Model 2 Model 3 Model 4 Intercept $\beta_ 0$ 0.485 0.024 0.835 0.170 Age (A) $\alpha_ 1$ 0.194† 0.195† 0.199† 0.203† length of service (L) $\alpha_ 2$ 0.114† 0.111† 0.112† 0.131† Job position (J) $\alpha _3$ −0.116† −0.116† −0.115† −0.113† Workload (W) $\beta _1$ 0.055* 0.024 0.068* IEU level (I) $\beta _2$ −0.092† −0.068* W×I $\beta_ 3$ −0.183† R² 0.083 0.086 0.094 0.126 ΔR² 0.083 0.003 0.008 0.032 F-test 65.626† 7.064* 18.034† 78.143† Abbreviation: WMSDs=work-related musculoskeletal disorders.
* P<0.01;
† P<0.001.Table 3. Step-wise regression results of the degree of pain associated with WMSDs.
The analysis above explains the effect of IEU on WMSDs. IEU not only directly reduces the prevalence of WMSDs but also significantly reduces the harmful effects of workload on WMSDs.
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As a chronic condition, WMSDs place a substantial burden on healthcare systems and decrease productivity. Epidemiological data on WMSDs in coal miners, a high-risk group, are representative of trends in other occupations. By replacing manual labor with intelligent equipment, IEU reduces the intense repetitive work required of miners, thereby lowering the risk of WMSDs and disease onset. Compared with low levels of IEU-D, high levels can reduce WMSDs prevalence rates among miners by 7.49% and decrease WMSDs pain by an average of 13.69%. This study found the following declines in the prevalence rate of WMSDs by body part: ankles/feet (15.94%), hips/thighs (15.42%), elbows (15.23%), knees (14.45%), upper back (13.36%), wrists/hands (13.06%), neck (10.73%), lower back (9.66%), and shoulders (8.29%). Declines in WMSDs pain by body part were as follows: hips/thighs (19.07%), elbows (16.47%), knees (16.39%), upper back (15.45%), wrists/hands (14.01%), ankles/feet (13.61%), neck (11.23%), shoulders (9.01%), and lower back (7.97%). Considering age, length of service, position, and workload, stepwise regression analysis revealed that IEU can reduce WMSDs pain and moderate the harmful effects of workload.
The NQPFs should also focus on health productivity. MSDs are the second leading cause of non-fatal disability worldwide, affecting over 1.63 billion people (10). WMSDs, a preventable subset of MSDs, can be mitigated by improving working conditions. As a form of NQPF, intelligence mining presents a win-win scenario for productivity and occupational health. However, evaluations of intelligence mining often neglect the associated occupational health benefits, leading to an underestimation of its positive impact. From a life-cycle perspective, intelligence mining is valuable in preventing WMSDs in older miners. Public health departments can introduce occupational health evaluation standards and certification systems to provide a basis for assessing the health benefits of intelligent equipment. Notably, at high IEU-D, prevalence rates of WMSDs in the neck, shoulders, upper back, lower back, and wrists remain higher than 50%, indicating substantial potential for further WMSD control. Public health departments can provide increased technical support for intelligent equipment design, enhancing ergonomic compatibility. Coal mines, bearing primary responsibility for miners’ health, should prioritize ergonomic requirements when selecting intelligent equipment. In positions necessitating poor posture, repetitive motions, or weight-bearing work, coal mines should implement regular job rotation.
This study was subject to some limitations. First, there was potential recall bias, as WMSD prevalence rates relied heavily on miners’ recollections Second, the cross-sectional design precluded comparative analysis before and after intelligent mining implementation. As intelligent mining advances, the impact of IEU on WMSDs may evolve. Future studies could employ a follow-up cohort to analyze long-term effects.
Intelligent mining is not only a new quality productive force but also a health productivity force. WMSDs are preventable workplace diseases. Public health departments should capitalize on this opportunity, using coal mines as a pilot industry, to develop ergonomics certification for intelligent equipment. This will help companies integrate intelligent transformation with WMSDs prevention.
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