Investigation of Work-related Musculoskeletal Disorders among Chinese Construction Workers: An Integrated Application of Questionnaire and Observational Methods

Investigation of Work-related Musculoskeletal Disorders among Chinese Construction Workers: An Integrated Application of Questionnaire and Observational Methods | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigation of Work-related Musculoskeletal Disorders among Chinese Construction Workers: An Integrated Application of Questionnaire and Observational Methods Xiongda He, Chunshuo Chen, Bin Xiao, Zhipeng He, Guoyong Xu, Hua Yan, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9149878/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Objectives Work-related Musculoskeletal Disorders (WMSDs) are a major occupational health issue in construction industry. However, there are few analyses about WMSDs in this field. This study aims to evaluate the prevalence of WMSDs among constructionworkers. Methods This study first conducted a cross-sectional survey of 10,781 construction workers using a musculoskeletal questionnaire to determine WMSDs prevalence and identify associated factors via multivariate logistic regression. In the second phase, we selected 220 key workers, recorded their work activities, and used Kinovea software for postural analysis. We applied RULA (Rapid Upper Limb Assessment), REBA (Rapid Entire Body Assessment), and OWAS (Ovako Working Posture Analysis System) to assess WMSDs risks and performed multiple comparisons to evaluate the consistency and applicability of these tools in construction settings. Results Construction workers reported a 27.4% prevalence of WMSDs, primarily affecting the shoulders, lower back/lumbar region, and neck. Riveters, welders, carpenters, and rebar workers showed relatively high overall prevalence rates of WMSDs. Individual factors, job type, work organization, and work postures influenced WMSDs. In observation-based method, three methods identified the WMSDs risk in rebar tying, welding and grinding operations, as predominantly above level 3, while the overall risk of WMSDs in woodworking was assessed to be relatively low. Rebar workers and welders shared a high risk from repetitive/prolonged static wrist postures. Carpenters were uniquely exposed to risks from forceful gripping and load handling, combined with demanding arm and lower limb postures. Riveters integrated all these challenges, facing a composite of wrist postures, repetitive/static operations, gripping, and loading. Conclusions Construction workers face a substantial risk of developing WMSDs. It is recommended that enterprises implement comprehensive measures tailored to individual and occupational profiles. A systematic screening for risk factors should be established to facilitate the development of more effective holistic strategy for the prevention and control of WMSDs. Construction worker WMSDs RULA (Rapid Upper Limb Assessment) REBA (Rapid Entire Body Assessment) OWAS (Ovako Working Posture Analysis System) Figures Figure 1 Figure 2 1 Introduction Work-related musculoskeletal disorders (WMSDs) refer to health issues of muscles, tendons, joints, cartilage, ligaments, and nerves induced or aggravated by work, presenting with discomfort, numbness, pain, and limited mobility [ 1 ]. As a major subset of musculoskeletal disorders (MSDs)—the second leading cause of non-fatal disability globally (affecting over 1.63 billion people [ 3 ])—WMSDs pose critical challenges to public health and social economy, especially since occupational ergonomic factors are the primary contributor to MSD-related disability-adjusted life years (DALYs) among 15–39-year-old laborers [ 3 , 4 ]. The construction industry, a cornerstone of national economies (accounting for 9%–15% of GDP globally [ 5 , 6 ]), has grown rapidly in China, with its added value accounting for over 6.70% of GDP since 2014 [ 7 ]. However, limitations such as inadequate supervision, labor-intensive work, and insufficient occupational health education threaten workers’ health, making construction workers highly susceptible to WMSDs [ 8 – 10 ]. Meta-analyses and surveys confirm high WMSDs prevalence: 51.1% for lower back WMSDs among construction workers [ 12 ], with higher rates in the U.S. construction industry than the national average [ 13 ], and elevated risks for male workers in Korea [ 14 ] and as the most common work-related disease in Iran [ 15 ]. WMSDs are linked to ergonomic factors including overexertion, prolonged awkward/static postures, repetitive operations, vibration, and poor work organization [ 16 – 19 ], highlighting the need for ergonomic interventions in construction [ 11 , 20 ]. Biomechanical factors are key drivers of WMSDs development [ 17 ], with three primary assessment approaches: (1) subjective self-report questionnaires (simple but less valid); (2) observation-based tools (e.g., RULA, REBA, OWAS) via real-time observation/video (cost-effective, accessible, and practical for on-site use, with higher validity than self-report [ 21 ]); and (3) direct measurements (highly valid but complex and resource-intensive). Integrating multiple methods is optimal for comprehensive risk evaluation. Few large-scale studies on WMSDs among Chinese construction workers exist. Thus, this study aims to investigate the distribution and risk factors of WMSDs in this population using a combination of questionnaires and observation-based methods, compare the effectiveness of different observational tools to identify the most suitable one, and provide a theoretical basis for targeted ergonomic interventions, WMSDs prevention, and sustainable health promotion for workers and the industry. 2 Methods 2.1 Questionnaire Survey 2.1.1 Participants Using convenience sampling, all eligible construction workers from 10 large-scale construction companies (two companies per city) in five coastal cities—Huizhou City and Lufeng City in Guangdong Province, Fangchenggang City in Guangxi Zhuang Autonomous Region, Wenzhou City in Zhejiang Province, and Ningde City in Fujian Province—were selected as study subjects between February 2023 and August 2024. The participant selection criteria were as follows: (1) aged 18 years or above, with at least one year of work experience in their current jobs; (2) pregnant women, individuals diagnosed with rheumatic diseases, trauma, other medical emergencies, physical disabilities or sequelae of diseases, tumours, other non-occupational musculoskeletal diseases, and those with cognitive or behavioural disorders were excluded. All participants read and signed the informed consent form for this survey. 2.1.2 The content of questionnaire The questionnaire of this survey, adapted from the Musculoskeletal Disorders Questionnaire (Chinese Version) [ 22 ], consisted of five parts. The first part, demographic information, included gender, age, height, weight, body mass index (BMI), educational level, smoking, alcohol consumption, etc. The second section assessed whether the participants had experienced any discomfort symptoms—including numbness, weakness, stiffness, fatigue, pain, or limited mobility—in any of the following nine body regions during the past 12 months: neck, shoulders, upper back, elbows, lower back/waist, hands/wrists, hips/thighs, knees, and ankles/feet. Additionally, information regarding symptom frequency, duration, and severity was collected. The third part evaluated workers' engagement in the following tasks during a typical workday: sedentary work, standing work, squatting or kneeling work, heavy material handling, operation of vibrating tools, upper limb or hand-intensive tasks, work requiring awkward postures, repetitive operations, etc. The fourth part was about the working organisation and environment of participants, such as work duration, shift schedules, staff shortages, high-temperature occupational exposures, etc. The final section evaluated the requirement to maintain awkward postures for prolonged periods and/or perform repetitive motions during work tasks. 2.1.3 Definitions 2.1.3.1 WMSDs According to NIOSH, WMSDs in this study were defined as follows: (1) discomfort within the past year; (2) discomfort beginning after employment in the current job; (3) no prior accident or sudden injury (affecting a focal area of discomfort); and (4) episodes of discomfort occurring monthly or, if not every month, at least exceeding a one-week period of discomfort [ 23 ]. 2.1.4 Data Quality Control Prior to the survey, trained investigators explained the questionnaire content and instructions to all participants using a standardized script to ensure comprehension. Workers were then individually guided to scan a QR code and complete the electronic questionnaire in a one-to-many facilitation setting. The digital system incorporated built-in completeness checks and logical validation to ensure data integrity. Upon completion of the survey, the database was downloaded and the data were checked for inconsistencies prior to formal analysis. 2.1.5 Statistical Analysis Statistical analysis was performed using SPSS 26.0 (IBM Corp., Armonk, NY, USA). Measurement data that conformed to a normal distribution were described as mean ± standard deviation, while data not conforming to a normal distribution were described as median and interquartile range(25th – 75th percentiles). Univariate analysis of WMSDs was conducted using the Chi-square test, and multivariate analysis was performed using logistic regression with the forward stepwise method (entry criterion: P < 0.05, removal criterion: P < 0.10). The significance level was set at α = 0.05 (two-tailed). 2.2 Observation-based methods 2.2.1 Participants The selection of participants was conducted as follows: Key construction trades were identified based on data from the first-phase questionnaire. A simple random sampling method was employed to select at least three workers from each of the ten participating enterprises. The sampling process was carried out from August 2024 to January 2025. Workers who were in good general health, had reported no WMSDs symptoms within the preceding six months, adhered to relevant operational procedures, and demonstrated proficiency in their respective tasks were selected as observation subjects. 2.2.2 Video recording The Fluorite S3 Sports Camera was used to record video footage of construction workers' activities from both frontal and lateral perspectives. The lateral perspective (left or right) was determined based on the worker's dominant hand during task performance (Fig. 1 ). Video recording was initiated 2–3 seconds prior to the commencement of the work activity. The objective was to capture a minimum of three complete work cycles, with each session having a duration of no less than three minutes. The specifications for video recording adhered to the guidelines stipulated by the National Institute for Occupational Safety and Health (NIOSH) in their publication, "Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement" [ 21 ]. 2.2.3 Risk Assessment The recorded footage was examined using a frame-by-frame approach to identify key postures for analysis, guided by selection criteria that prioritized those with the highest repetition, longest sustained duration, and established links to musculoskeletal discomfort. A risk assessment was conducted on the key postures utilizing three established ergonomic tools: the Rapid Upper Limb Assessment (RULA), the Rapid Entire Body Assessment (REBA), and the Ovako Working Posture Analysis System (OWAS), which are most frequently employed in industries for assessing the biomechanical load of the whole body [ 24 ]. The RULA method was designed for the rapid assessment of neck, trunk, and upper limb postures, muscle function, and external loads [ 25 ]. The REBA technique offers a postural analysis system sensitive to musculoskeletal risks across diverse tasks, with particular applicability in healthcare and other service industries [ 26 ]. The OWAS method, developed by the Finnish steel company Ovako Oy, categorizes four back postures, three arm postures, seven lower-limb postures, and three load/force categories [ 27 ]. To facilitate a comparative analysis across the three assessment methods, a unified four-level risk categorization was adopted. Following the approach of Kee [ 28 ], the original five-level REBA score was reclassified into four levels: Level 1 (original 0), Level 2 (original 1–2), Level 3 (original 3), and Level 4 (original 4), ensuring consistency with the risk level descriptors of the other methods. 2.2.4 Extraction of Human Joint Angle Joint angles of construction workers were extracted using the Kinovea software (Version 0.9.5). This open-source tool facilitates detailed motion analysis by enabling frame-by-frame playback, zoom functionality, and the identification of skeletal landmarks to quantify body segment angles during dynamic tasks. Its user-friendly design allows for the acquisition of accurate and reliable measurements without requiring advanced technical expertise [ 29 , 30 ]. To standardize the assessment, anatomical landmarks were defined according to the international standards ISO 11226: 2000 EN [ 31 ] and ISO 7250-1:2017 [ 32 ], supplemented by the methodology of van T Hullenaar [ 33 ]. This framework provided the basis for uniformly defining the range of motion measurements for the body segments specified in the RULA, REBA, and OWAS worksheets. Given the complexity and frequent occlusion of wrist movements during tasks, wrist postures were determined through a synthesis of direct observation, worker inquiry, video analysis, and group discussion, with risk levels assigned directly based on the respective scoring criteria of each assessment tool. The calibration and measurement for the range of motion of human joints were shown in Table S1 and Fig. 2 . 2.2.5 Quality Control All investigators should receive standardized training prior to video recording to ensure familiarity with the recording protocols, competence in technical specifications, and accurate application of each assessment scale. Before the start of recording, participants should be thoroughly informed of the study objectives and procedural details. Throughout the recording process, investigators must avoid interfering with the normal work activities of the operators. Identification of critical working postures is performed independently by two ergonomics specialists through video analysis according to predefined criteria. In cases of disagreement, a third domain expert performs a blinded review to resolve the discrepancies and establish a consensus. 2.2.6 Statistical Analysis Statistical analysis was performed with SPSS Statistics, Version 26.0 (IBM Corp., Armonk, NY, USA). (1) Means ± standard deviations were used for continuous variables while counts and percentages were used for categorical variables. For data that did not conform to a normal distribution, the median and interquartile range were used for description. (2) Inter-rater agreement between assessment methods was evaluated using linearly weighted Kappa statistics. The strength of agreement was interpreted as follows: poor for Kappa < 0.2, fair for 0.2–0.4, moderate for 0.4–0.6, good for 0.6–0.8, and very good for 0.8–1.0 [ 34 ]. Differences in risk ratings across different work tasks were analyzed with the Kruskal–Wallis test ( H-test ). Post-hoc pairwise comparisons were conducted using Dunn's test with Bonferroni adjustment. A corrected p-value < 0.05 was considered statistically significant, with the adjusted significance level ( α' ) calculated as the original alpha ( α = 0.05) divided by the number of comparisons (two-tailed). 3 Results 3.1 Questionnaire Survey 3.1.1 Demographic characteristics A total of 11,994 construction workers were initially invited to participate in the survey. After applying the inclusion and exclusion criteria, 10,781 valid questionnaires were retained, yielding an effective response rate of 89.9%. The study population was predominantly male (88.9%). The mean age of participants was 39.9 (±10.9) years. The mean BMI was 24.3 (±3.6) kg/m². In terms of educational attainment, the vast majority (92.8%) had completed high school or higher. Regarding marital status, 7,759 participants (72.0%) were married, while 3,022 (28.0%) were unmarried or fell into other categories (e.g., divorced or widowed). Smoking and drinking habits were reported by 5,358 (49.7%) and 6,074 (56.3%) participants, respectively. The average work experience in the construction industry was 4.2 (±5.1) years. After work, 4,505 participants (41.8%) engaged in occasional physical exercise, and 1,095 (10.2%) exercised at least twice per month. The most common occupations reported were general laborer (28.0%), rebar worker (16.2%), and carpenter (14.8%). Detailed information is presented in Table 1. Table 1 General demographic characteristics of the objects Indicators Number Proportion (%) Sex Female 1196 11.1 Male 9585 88.9 Age (years) <30 2245 20.8 30-39 2760 25.6 40-49 3109 28.8 ≥50 2667 24.7 BMI 18.5-23.9 kg/m 2 5051 46.9 < 18.5 kg/m 2 370 3.4 ≥24.0 kg/m 2 5360 49.7 Education level Middle school or below 781 7.2 High school or above 10000 92.8 Marital Status Unmarried, divorced, widowed, etc. 3022 28.0 Married 7759 72.0 Smoke No 4166 38.6 Yes 5358 49.7 Smoking cessation 1257 11.7 Alcohol No 3868 35.9 Yes 6074 56.3 Alcohol abstinence 839 7.8 Work experience (years) ≤2 4832 44.8 3-5 4258 39.5 6-10 907 8.4 >10 784 7.3 Physical exercise after work No 5181 48.1 ≤2 times per month 4505 41.8 >2 times per month 1095 10.2 Job type General Laborer 3014 28.0 Rebar Worker 1749 16.2 Carpenter 1591 14.8 Scaffolder 898 8.3 Roofer 795 7.4 Welder 776 7.2 Crane Operator 511 4.7 Concrete Worker 397 3.7 Installer 241 2.2 Painter 149 1.4 Construction Vehicle Driver a 144 1.3 Other b 516 4.8 Note: a "Construction Vehicle Operator" refers to personnel who operate dedicated construction site vehicles, such as trucks, trailers, forklifts, dump trucks, excavators, cranes, and concrete mixers. b The "Other" occupational category includes trades like electricians, mechanics, bricklayers, and insulation workers. 3.1.2 The prevalence of WMSDs The overall prevalence of WMSDs among the construction workers was 27.4%. The most frequently affected body regions were the shoulders (10.6%), lower back/lumbar area (10.4%), neck (10.3%), and feet/ankles (10.1%). The differences in WMSD prevalence across body regions were statistically significant (trend χ² = 594.69, P < 0.001). Among the various occupational trades, welders, riveters, carpenters, and rebar workers showed relatively higher overall WMSD prevalence, at 34.9% (271/776), 33.5% (266/795), 28.5% (453/1591), and 27.5% (481/1749), respectively. Statistically significant differences in prevalence of WMSDs were observed across trades for the overall rate, as well as for the neck, shoulder, lower back/lumbar, knee, and ankle/foot regions ( P < 0.001). Welders had the highest prevalence of WMSDs in the neck, shoulders, lower back/lumbar, and angle/foot area. The highest prevalence of ankle/foot region among all occupations was observed in welders and general laborers.. Detailed results are provided in Table S2. 3.1.3 Influencing factors of WMSDs This study focused on factors influencing work-related musculoskeletal disorders (WMSDs) of the neck, shoulder, and lower back (highly prevalent in the study population). Variables were first screened via Chi-square Test , followed by multivariable logistic regression analysis. Below are the key findings (all P < 0.05): ①Shoulder WMSDs Individual factors: Risk factors (former/current drinking); Protective factors (age 30–39, 40–49, or ≥50 years; married; leisure-time physical exercise ≤2 times/month) Work tasks: Risk factors (sedentary work >4 hours/day; uncomfortable postures for 1–4 hours/day or >4 hours/day) Work organization: Risk factors (weekly working time >64 hours; daily repetitive tasks; high-temperature exposure; shift work; understaffing); Protective factor (adequate rest periods) Working postures: Risk factors (frequent repetitive lower back movements; hand positions above shoulder level during work) ②Lower Back WMSDs Individual factors: Risk factors (drinking; severe post-work fatigue); Protective factors (married; leisure-time physical exercise ≤2 times/month) Work tasks: Risk factors (sedentary work >4 hours/day; squatting/kneeling for 1–4 hours/day or >4 hours/day; uncomfortable postures for 1–4 hours/day or >4 hours/day) Work organization: Risk factors (weekly working time >64 hours; daily repetitive tasks; frequent overtime; understaffing); Protective factor (adequate rest periods) Working postures: Risk factors (frequent moderate/severe lower back flexion; repetitive lower back movements) ③Neck WMSDs Individual factors: Risk factor (severe post-work fatigue); Protective factors (age 30–39, 40–49, or ≥50 years; married; leisure-time physical activity ≤2 times/month) Work tasks: Risk factors (sedentary work for 1–4 hours/day or >4 hours/day; uncomfortable postures for 1–4 hours/day or >4 hours/day) Work organization: Risk factors (weekly working time >64 hours; daily repetitive tasks; shift work; frequent overtime; understaffing); Protective factors (task rotation among colleagues; adequate rest breaks) Working postures: Risk factors (frequent moderate/severe lower back bending; repetitive lower back movements) Detailed results are presented in Table 2. Table 2 The results of logistic regression analysis on the influencing factors of WMSDs among construction workers Influencing Factors OR (95% CI ) Shoulder WMSDs Lower Back WMSDs Neck WMSDs Age （ years ） <30 1.000 - 1.000 30-39 0.667(0.550-0.817)* - 0.745(0.608~0.914)* 40-49 0.601(0.483-0.748)* - 0.730(0.584~0.913)* ≥50 0.671(0.531-0.848)* - 0.717(0.564~0.911)* Marital Status Unmarried, divorced, widowed, etc. 1.000 1.000 1.000 Married 0.709(0.594-0.845)* 0.741(0.644-0.851)* 0.745(0.622~0.892)* Alcohol No 1.000 1.000 - Alcohol abstinence 1.379(1.069-1.777)* 1.248(0.957-1.628) - Yes 1.193(1.035-1.374)* 1.348(1.164-1.560)* - Physical exercise after work No 1.000 1.000 1.000 ≤2 times per month 0.803(0.699-0.923)* 0.762(0.661-0.880)* 0.765(0.663~0.882)* >2 times per month 0.916(0.729-1.152) 1.104(0.884-1.379) 1.017(0.814~1.271) Post-work fatigue Mild - 1.000 1.000 Moderate - 1.223(0.769-1.947) 1.339(0.852~2.104) Severe - 1.646(1.040-2.606)* 1.690(1.079~2.647)* Sedentary work 4 hours per day 1.196(1.030-1.388)* 1.329(1.141-1.549)* 1.571(1.341~1.840)* W orking in a squatting or kneeling position 4 hours per day - 1.610(1.309~1.981)* - Note: “*” for “ P <0.05” Continued Table 2 The results of logistic regression analysis on the influencing factors of WMSDs among construction workers Influencing Factors OR (95% CI ) Shoulder WMSDs Lower Back WMSDs Neck WMSDs Working in uncomfortable postures 4 hours per day 1.947(1.631-2.323)* 1.938(1.613-2.328)* 2.012(1.681~2.408)* Weekly working time （ hours ） ≤48 1.000 1.000 1.000 49-56 1.014(0.801-1.285) 1.022(0.802-1.303) 0.978(0.771~1.240) 57-64 1.124(0.894-1.415) 1.030(0.812-1.307) 0.969(0.767~1.224) >64 1.348(1.081-1.681)* 1.279(1.016-1.608)* 1.275(1.019~1.595)* Posutre of ower back during working Straight - 1.000 1.000 Moderate bending - 1.250(1.067~1.465)* 1.180(1.014~1.373)* Severe bending - 1.635(1.355~1.973)* 1.230(1.017~1.488)* Performing the same tasks daily 1.339(1.156-1.551)* 1.543(1.321-1.801)* 1.481(1.272~1.725)* Task rotation among colleagues - - 0.800(0.681~0.941)* Frequent overtime - 1.207(1.037-1.405)* 1.218(1.047~1.416)* Exposure to high-temperature environments 1.190(1.019-1.391)* - - Shift work 1.227(1.066-1.412)* - 1.213(1.051~1.400)* Adequate rest periods 0.658(0.573-0.755)* 0.589(0.511-0.680)* 0.638(0.553~0.735)* Understaffing 1.533(1.342-1.751)* 1.567(1.368-1.794)* 1.555(1.357~1.780)* Frequently performing repetitive movements with the lower back 1.570(1.249-1.972)* 1.518(1.184-1.946)* 1.290(1.034~1.608)* Maintaining hand positions above shoulder level 1.173(1.017-1.354)* - - Note: “*” for “ P <0.05” 3.2 Observation-Based Posture Assessment 3.2.1 Demographic characteristics Based on the results of the questionnaire survey, our study selected rebar workers, carpenters, welders, and riveters as the key occupational groups for focus. Given that workers on construction sites often perform multiple tasks without a fixed sequence, and based on field investigations and interviews, the following operational definitions were established for the key occupations and their primary work content in this study: Rebar Worker: A worker engaged in rebar tying activities for ≥4 hours per day. Carpenter: A worker performing wood processing tasks for ≥4 hours per day. Welder: A worker carrying out welding operations for ≥4 hours per day. Riveter: A worker involved in grinding tasks for ≥4 hours per day. Additionally, as these occupations are predominantly male, the study population for this component was restricted to male workers. Our study finally enrolled a total of 220 construction workers in key occupational roles, including 60 rebar workers, 60 carpenters, 50 welders, and 50 riveters. A total of 220 work videos were collected. The median age of rebar workers was 46 years (IQR: 37–51), with 55 individuals (91.7%) being right-handed. Their median BMI was 24.2 kg/m² (IQR: 21.7–26.2), and their median work experience was 2 years (IQR: 1–4). Among carpenters, the median age was 44 years (IQR: 36–51), 53 (88.3%) were right-handed, the median BMI was 24.2 kg/m² (IQR: 22.2–26.2), and the median work experience was 3 years (IQR: 1–5). Welders had a median age of 36 years (IQR: 30–47), 45 (90.0%) were right-handed, a median BMI of 23.6 kg/m² (IQR: 21.6–27.3), and a median work experience of 4 years (IQR: 1–6). For riveters, the median age was 39 years (IQR: 32–46), 46 (92.0%) were right-handed, the median BMI was 24.2 kg/m² (IQR: 22.3–27.5), and the median work experience was 3 years (IQR: 1–6). A total of 220 critical working postures were selected for risk assessment according to predefined criteria, captured during rebar tying by rebar workers, wood processing by carpenters, welding by welders, and grinding by riveters. Figure S1 through S4 illustrate the activity patterns for each operation, and Table S2 provided the corresponding postural angles of different body regions during these tasks. 3.2.2 Comparison of assessment using the same methods for different critical working postures 3.2.2.1 RULA While no postures were rated at Level 1 risk, the distribution of higher-risk postures varied by task. Level 4 was the most common risk level in rebar tying and grinding, followed by Level 3. Conversely, Level 3 postures predominated in wood processing and welding, followed by Level 4. Post-hoc multiple comparisons indicated a significantly higher overall risk level for rebar tying and grinding compared to wood processing and welding ( P < 0.008). Details are shown in Table S4. Post-hoc analysis of RULA subscores identified significant inter-task differences. Localized risks were pronounced in rebar tying (for wrist flexion/rotation and leg postures) and wood processing (for forearm posture). Regarding muscular load, the risks for both repetitive/static postures (Group A and Group B) and force/load (Group A) were consistently lower in wood processing, while rebar tying and grinding showed elevated force/load risks. Consequently, the overall Group A risk ranking was: rebar tying > grinding > welding > wood processing ( P < 0.008). Details are shown in Table 3. Table 3 Comparison of RULA risk levels among different tasks Group Average Rank Rebar Tying Wood Processing Welding Grinding Upper Arm - - - - Lower Arm 110.83 136.41 c, d 97.56 b 91.95 b Wrist Bend 159.60 b, c, d 90.75 a 89.68 a 96.10 a Wrist Twist 136.83 b, c, d 100.17 a 105.30 a 96.50 a Muscle Use A 128.33 b 56.83 a, c, d 132.00 b 132.00 b Force/Load A 164.00 b, c 54.84 a, d 65.50 a, d 158.09 b, c Wrist & Arm Score 169.48 b, c, d 53.29 a, c, d 87.73 a, b, d 131.15 a, b, c Neck - - - - Trunk - - - - Leg 115.83 b, c, d 108.50 a 108.50 a 108.50 a Muscle Use B 119.83 b 83.17 a, c, d 123.50 b 119.10 b Force/Load B - - - - Neck, Trunk, Leg Score - - - - Risk Level 124.46 b 69.28 a, c, d 112.08 b 141.63 b Note: Within the same row, pairwise comparisons were performed using Dunn’ s test . a.Compared with the mean rank of rebar tying, P < 0.008; b.Compared with the mean rank of wood processing, P < 0.008； c.Compared with the mean rank of welding, P < 0.008； d.Compared with the mean rank of grinding, P < 0.008. 3.2.2.2 REBA The distribution of risk levels varied by task. In both rebar tying and welding, Level 3 postures were most frequent, followed by Level 2. For wood processing, Level 2 was predominant, followed by Level 3. During grinding, Level 3 postures accounted for the highest proportion, with Level 4 being the second most frequent. Details were shown in Table S5. The post hoc analysis revealed that: the postural risk for the legs in rebar tying was significantly higher than in wood processing and grinding; the risk score for Body Part A in grinding exceeded that in wood processing; the risk for wrist postures in wood processing was lower than in the other three tasks; the coupling/grip appropriateness was lower in rebar tying than in wood processing and grinding, and lower in welding than in wood processing and grinding; the risk of whole-body repetition/static activity in wood processing was lower than in the other three tasks; and the REBA risk level in grinding was higher than in wood processing ( P < 0.008). Details were shown in Table 4. Table 4 Comparison of REBA risk levels among different tasks Group Average Rank Rebar Tying Wood Processing Welding Grinding Neck - - - - Trunk - - - - Leg 129.28 b, d 93.17 a 120.78 98.48 a Force/Load - - - - Score A 107.82 95.47 d 109.19 133.07 b Upper Arm - - - - Lower Arm - - - - Wrist 132.50 b 84.83 a, c, d 112.70 b 112.70 b Coupling 63.00 b, d 139.54 a, c 80.04 b, d 163.11 a, c Score B - - - - Activity 127.77 b 56.60 a, c, d 134.50 b 130.46 b REBA Score 109.23 87.67 d 114.33 135.60 b Note: Within the same row, pairwise comparisons were performed using Dunn’ s test . a.Compared with the mean rank of rebar tying, P < 0.008; b.Compared with the mean rank of wood processing, P < 0.008； c.Compared with the mean rank of welding, P < 0.008； d.Compared with the mean rank of grinding, P < 0.008. 3.2.2.3 OWAS The distribution of postural risk levels was task-specific. In rebar tying, Level 3 postures were predominant, followed by Level 1. For both wood processing and welding, Level 2 was the most frequent, followed by Level 3. In grinding, Level 2 and Level 3 postures collectively showed the highest prevalence, followed by Level 1. Details were shown in Table S6. The multiple comparisons revealed that the postural risk for the legs during grinding was significantly lower than during rebar tying and wood processing; the manual lifting risk in wood processing was significantly higher than in the other three tasks; and the OWAS risk score for welding was significantly higher than for rebar tying ( P < 0.008). Details were shown in Table 5. Table 5 Comparison of OWAS risk levels among different tasks Group Average Rank Rebar Tying Wood Processing Welding Grinding Trunk - - - - Arm - - - - Leg 123.08 d 124.80 d 106.15 82.59 a, b Lifting heavy objects with hands 107.50 b 118.50 a, c, d 107.50 b 107.50 b OWAS Score 87.67 c 112.55 121.33 a 114.61 Note: Within the same row, pairwise comparisons were performed using Dunn’ s test . a.Compared with the mean rank of rebar tying, P < 0.008; b.Compared with the mean rank of wood processing, P < 0.008； c.Compared with the mean rank of welding, P < 0.008； d.Compared with the mean rank of grinding, P < 0.008. 3.2.3 Comparison of assessment from different methods for the same critical working postures 3.2.3.1 RULA VS REBA The agreement between RULA and REBA in risk assessments for all working postures was 46.8% (κ = 0.318, P < 0.05). Task-specific agreement rates were as follows: 28.3% (κ = 0.099, P < 0.05) for rebar tying, 48.3% (κ = 0.373, P < 0.05) for wood processing, 57.9% (κ = 0.324, P < 0.05) for welding, and 56.0% (κ = 0.374, P < 0.05) for grinding. For all working postures assessed, a statistically significant difference was observed between RULA and REBA assessment results, with RULA yielding systematically higher scores than REBA ( P < 0.017). The detailed results were summarized in Tables S7-S9. 3.2.3.2 RULA VS OWAS The inter-method agreement between RULA and OWAS for risk assessment of working postures was 14.5% (κ = 0.044, P 0.05) for rebar tying, 20.0% (κ = 0.320, P 0.05) for welding, and no significant agreement was observed for grinding (κ = 0.014, P > 0.05). A statistically significant difference was observed between RULA and OWAS assessment results across all working postures, with RULA consistently yielding higher scores than OWAS ( P < 0.017). The detailed results were summarized in Tables S7-S9. 3.2.3.3 REBA VS OWAS The inter-method agreement between REBA and OWAS for risk assessment across all working postures was 28.2% (κ = 0.229, P < 0.05). Task-specific agreement rates were as follows: 25.0% (κ = 0.245, P < 0.05) for rebar tying, 48.3% (κ = 0.297, P < 0.05) for wood processing, 57.9% (κ = 0.298, P < 0.05) for welding, and 56.0% (κ = 0.090, P < 0.05) for grinding. REBA demonstrated systematically higher assessment scores compared to OWAS across all working postures, with the difference being statistically significant ( P < 0.017). The detailed results were summarized in Tables S7-S9. 4 Discussion 4.1 Prevalence of WMSDs A large-scale survey on WMSDs among construction workers in China was conducted in this study. The results indicated an overall WMSD prevalence rate of 27.4%, with the most commonly affected regions being the lower back/lumbar, shoulders, and neck. While the high-prevalence body sites align with findings from several domestic studies [ 35 – 37 ], the overall prevalence rate observed in this study is relatively lower. This discrepancy may be attributed to differences in WMSDs diagnostic criteria, sample size, geographical distribution, and population composition. Construction workers demonstrated a lower prevalence of WMSDs relative to other occupational groups, such as administrative personnel, nurses, and drivers [ 38 – 40 ]. The construction industry is characterized by high-intensity physical demands, significant occupational exposure risks, and a highly mobile workforce, which collectively contribute to a pronounced healthy worker effect. The substantial physical requirements of construction work selectively retain individuals with greater physical capacity, while those with poorer health are more likely to leave or be excluded from the workforce. This selection mechanism results in a lower observed prevalence of WMSDs among construction workers compared to occupational groups in manufacturing, healthcare, and office-based settings [ 38 ]. This study also identified riveters, welders, carpenters, and rebar workers as subgroups with a higher prevalence of WMSDs than the broader construction workforce. This elevated risk is linked to occupational exposures characterized by sustained static/awkward postures (e.g., stooping, forward head posture, overhead work) and repetitive manual tasks (including twisting, flexing, and lateral deviations of the hands) [ 41 ], with contributing environmental aggravators like dust and noise further potentiating the burden in the neck, shoulder, and lower back [ 42 ]. 4.2 Influencing factors of WMSDs 4.2.1 Questionnaire Method The development of work-related musculoskeletal disorders (WMSDs) involves complex interactions between individual characteristics, work-related biomechanical factors, and work organization: (1) Individual Characteristics Age: Neck/shoulder WMSD prevalence decreases with age, possibly due to senior workers acquiring labor-saving postures/techniques or being reassigned to less physically demanding roles [ 43 , 44 ], consistent with Le et al. [ 43 ]. Marital status: Married workers have lower WMSD risk (lower back, shoulder, neck) than unmarried peers, attributed to socioeconomic/emotional stability, spousal support (reducing non-work physical load), and reduced psychological stress/loneliness [ 45 ]. Alcohol consumption: A risk factor for lower back/shoulder WMSDs [ 46 ], as long-term drinking impairs muscle metabolism (fiber breakdown, atrophy) and disrupts neuromotor control/vascular function, increasing fatigue and injury risk [ 47 ]. Leisure-time physical activity & fatigue: Moderate activity is protective, but excessive activity provides no additional benefit (compounding occupational physical load) [ 48 – 50 ]; self-perceived fatigue increases WMSD risk by reducing strength, endurance, and coordination [ 51 ]. (2) Work-Related Biomechanical Factors (Work Types & Postures) Prolonged sitting: A dose-dependent risk factor for lower back/shoulder-neck WMSDs [ 52 ]; construction-related sitting (combined with dynamic movements, vibration, awkward postures) exacerbates risks. Squatting/kneeling (≥ 1 hour/day) increases lower back strain via elevated L4/L5 vertebral load, altered pelvic tilt, and reduced lumbar lordosis [ 53 , 54 ]. Uncomfortable postures: Long-duration exposure impairs local blood flow/ nutrient uptake in musculoskeletal tissues, with risk increasing with exposure time [ 55 ]. Specific postures/tasks: Sustained torso flexion, repetitive lower back movements, and overhead work (hands above shoulder level) elevate WMSD risk [ 56 – 58 ]; overhead work induces sustained shoulder muscle contraction and joint stress, promoting fatigue and cartilage degeneration [ 59 ]. (3) Work Organization Long working hours: Weekly working time > 64 hours increases WMSD risk via cumulative ergonomic exposure and heightened psychological stress [ 60 ]. Task monotony & staffing: Low job diversity (monotonous tasks) and understaffing lead to boredom, fatigue, and overtime, reducing recovery time [ 61 ]; shift work disrupts circadian rhythms, impairing sleep and causing chronic fatigue [ 62 ]. Rest periods: Adequate rest mitigates musculoskeletal fatigue and WMSD symptom intensity/clustering without reducing productivity [ 63 ]. High-temperature exposure: Prevalent in construction, thermal stress causes discomfort, fatigue, and impaired cognitive/motor function, increasing injury risk [ 64 , 65 ]. 4.2.2 Observation-based Method The assessment of risk is susceptible to subjectivity when based purely on self-reported questionnaires [ 66 ]. To address this limitation, our methodology extended beyond questionnaires by incorporating structured observational techniques. These techniques were used to capture specific postural characteristics during tasks and to apply standardized observational scales for a more objective analysis of posture-related risks among construction workers. 4.2.2.1 Risk of Rebar Tying Operation RULA (Level 4), REBA (Level 3), and OWAS (Level 3) have all identified high WMSDs risks in rebar tying, highlighting an urgent need for immediate improvements. First, RULA and REBA both detected significant risks associated with wrist movements: during rebar tying, workers use binding guns to secure steel wires, which involves more frequent and extensive repetitive wrist motions (including pronounced rotation, flexion-extension, and deviation) compared to welding or grinding—tasks characterized by moderate-to-low frequency wrist actions with smaller amplitudes and limited motion range. Chen et al. [ 67 ] noted that such repetitive wrist movements contribute more significantly to wrist WMSDs than sustained flexed postures. Additionally, RULA and REBA confirmed high lower limb WMSD risks for rebar workers, as the task often requires prolonged squatting or kneeling on uneven steel bar grids, resulting in inadequate lower-limb support and unstable body balance. Beyond postural hazards, RULA and REBA also emphasized risks from task repetitiveness: rebar tying involves repetitive wrist movements combined with frequent torso bending, which are particularly acute in pits or confined spaces where workers must maintain sustained neck/torso forward flexion alongside squatting or kneeling. Regarding force demands, RULA further indicated a high-risk level, as securing rebars requires workers to repeatedly perform a "grip-hook, rotate, pull-tighten" cycle, with considerable manual force needed especially during the "pull-tighten" phase. 4.2.2.2 Risk of Wood Processing Operation Although the three methods assessed the overall WMSD risk in woodworking as relatively low, the inherent hazards of this trade should not be overlooked. RULA detected a high risk of forearm injury in wood processing (including cutting and sanding with saws), attributed to on-site constraints: varying wood sizes/shapes, limited machine layout flexibility, and fixed workstation heights frequently force workers to hold forearms in prolonged excessive forward flexion, away from neutral postures, or operate arms across the midline (midsagittal plane) with excessive forward reach. Such deviations significantly increase mechanical stress on muscles, tendons, and ligaments around the arm joints, shoulder-neck complex, and upper back, compressing nerves and irritating tendons—predisposing workers to neck-shoulder syndrome, rotator cuff injuries, subacromial impingement syndrome, and biceps tendinitis [ 68 ]. For lower limbs, OWAS classified wood processing as high-risk, primarily due to its far higher walking frequency compared to other trades. Notably, OWAS also identified elevated hand load risk among carpenters: they often use hands/arms to briefly stabilize wood and prevent slipping during processing, while vibrations from cutting and the wood’s own weight further contribute to increased hand injury risk. 4.2.2.3 Risk of Welding and Grinding Operations The three assessment methods consistently rated the WMSD risk for welding and grinding operations at Level 3, indicating a clear need for ergonomic improvements. REBA and RULA showed that the two operations pose comparable WMSD risks: while their wrist movements are somewhat similar to those in rebar tying, they are mainly characterized by low-to-moderate frequency and small amplitudes, with a smaller overall range of motion and lower repetitiveness than rebar tying. To ensure optimal visibility during welding and grinding, workers often adopt prolonged postures such as flexed knees, bent backs, or unsupported arms, which compromise body mechanics. Additionally, process-specific techniques—including linear/circular grinding and repetitive weld beads—require highly repetitive arm and wrist motions, predisposing workers to musculoskeletal strain [ 69 ]. A further key concern is that welders frequently operate in confined spaces, forcing them to maintain prolonged kneeling or squatting postures, which are established risk factors for lower limb WMSDs [ 70 ]. In contrast to the other methods, RULA consistently assessed postural load as having a higher risk level in this study, indicating its greater sensitivity in identifying posture-related risks [ 54 ], which aligns with existing study results [ 28 , 71 , 72 ]. Given the low inter-method agreement observed for the same tasks, which reflects divergent assessment approaches, RULA is suggested for rapid screening in resource-limited settings. However, to compensate for the inherent risk of underestimation in the complex, multi-joint dynamics of construction, a holistic assessment strategy is imperative. This involves tailoring the evaluation to task-specific movements and utilizing a combination of methods or an integrated scale to leverage their complementary nature for a full picture of postural risk. 5 Conclusion Construction workers face high risk of WMSDs, with the lower back, shoulders, and neck as the most affected regions—riveters, welders, carpenters, and steel fixers are among the highest-prevalence trades. Multiple factors (individual attributes, job tasks, work organization) influence WMSD development, with awkward postures as the predominant risk factor. Among RULA, REBA, and OWAS, RULA is most sensitive to postural risk and preferable for rapid on-site screening, but overall agreement between the tools is low (each prioritizes different joint movements/kinematic chains). Due to the multi-joint complexity of construction tasks, single tools may underestimate risk; comprehensive evaluation with multiple tools (synthesizing local/overall risks) is essential when feasible. Limitations: 1) Cross-sectional design cannot establish causality between risk factors and WMSDs; 2) Focus on only four trades limits generalizability (future research should include more professions); 3) No direct measurement of muscular exertion/force may introduce bias. Future studies should adopt laboratory-based kinematic quantification, combined with real-world ergonomic data, to fully elucidate WMSD risk factors. Abbreviations WMSDs: Work-related Musculoskeletal Disorders LBP: Low back pain MSDs: Musculoskeletal Disorders DALYs: Disability Adjusted Life Years GDP: Gross Domestic Product RULA: Rapid Upper Limb Assessment REBA: Rapid Entire Body Assessment OWAS: Ovako Working Analysis System BMI : Body Mass Index NIOSH: National Institute for Occupational Safety and Health OR: Odds Ratios CI: Confidence Interval Declarations Ethics approval and consent to participate The study adhered to the ethical guidelines outlined in the Declaration of Helsinki and received approval from the Guangdong Province Hospital for Occupational Disease Prevention and Treatment Ethics Committee (No: GDHOD MEC 2024027). All study participants provided written informed consent before being included in the research. The participants shown in Figure 1 and 2 were consent for publication. Data availability The datasets underlying the results of this study can be obtained from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by grants from Guangdong Medical Research Foundation(A2023061). Author information Authors and Affiliations Guangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou, Guangdong, 510300, China Xiongda He, Chunshuo Chen, Bin Xiao, Zhipeng He, Guoyong Xu, Hua Yan, Yongjian Jiang, Junle Wu, Jianyu Guo, Maosheng Yan Department of Preventive Health Care, Dongguan Huangjiang Hosipital, Dongguan, Guangdong, 523750, China Xiongda He School of Public Health, Southern Medical University, Guangzhou, Guangdong, 510515, China Xiongda He, Yuan Qiu, Maosheng Yan School of Public Health, Guangzhou Medical University, Guangzhou, Guangdong, 511436, China Chunshuo Chen, Maosheng Yan School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, 030002, China Yanhui Lan, Xinyi Zou, Maosheng Yan Corresponding author Maosheng Yan. Email Address: [email protected] Contributions Xiongda He & Chunshuo Chen: data collection, data curation, methodology, data analysis, original draft preparation. Bin Xiao & Guoyong Xu: liaising and communicating with enterprises, supervision, writing-review and editing. Zhipeng He, Hua Yan, Yongjian Jiang, Junle Wu, Jianyu Guo: data collection, data curation. Yanhui Lan, Xinyi Zou, Yuan Qiu: data analysis. Maosheng Yan: funding acquisition, project administration, liaising and communicating with enterprises, supervision, conceptualization, writing-review and editing. Acknowledgements We are grateful to all researchers and every member of the staff involved in the survey for their contribution to the data collection. Clinical trial number Not applicable. References Luttmann, A. & Jäger, Matthias & Griefahn, B. & Caffier, G. & Liebers, Falk & Steinberg, U.. (2003). Protecting Workers' Health Series No. 5: Preventing musculoskeletal disorders in the workplace. Briggs AM, Shiffman J, Shawar YR, Åkesson K, Ali N, Woolf AD. Global health policy in the 21st century: Challenges and opportunities to arrest the global disability burden from musculoskeletal health conditions. Best Pract Res Clin Rheumatol. 2020 Oct;34(5):101549. GBD 2021 Other Musculoskeletal Disorders Collaborators. Global, regional, and national burden of other musculoskeletal disorders, 1990-2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021. Lancet Rheumatol. 2023 Oct 23;5(11):e670-e682. Guan SY, Zheng JX, Sam NB, Xu S, Shuai Z, Pan F. Global burden and risk factors of musculoskeletal disorders among adolescents and young adults in 204 countries and territories, 1990-2019. Autoimmun Rev. 2023 Aug;22(8):103361. Du Q, Zhou J, Pan T, Sun Q, Wu M. Relationship of carbon emissions and economic growth in China's construction industry. J Clean Prod. 2019;220: 99–109. Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry. Computers in Industry. 2016 Dec;83: 121–139. Zhao F, Wang YW, Jin L, et al. Statistics Analysis of China's Construction Industry Development in 2023. Journal of Engineering Management. 2024,38(02):1-6. van der Molen HF, Basnet P, Hoonakker PL, Lehtola MM, Lappalainen J, Frings-Dresen MH, Haslam R, Verbeek JH. Interventions to prevent injuries in construction workers. Cochrane Database Syst Rev. 2018 Feb 5;2(2):CD006251. Forteza FJ, Carretero-Gómez JM, Sesé A. Organizational factors and specific risks on construction sites. J Safety Res. 2022 Jun;81:270-282. Estudillo B, Forteza FJ, Carretero-Gómez JM, Rejón-Guardia F. The role of organizational factors in promoting workers' health in the construction sector: A comprehensive analysis. J Safety Res. 2024 Feb;88:41-55. Antwi-Afari MF, Li H, Chan AHS, Seo J, Anwer S, Mi HY, Wu Z, Wong AYL. A science mapping-based review of work-related musculoskeletal disorders among construction workers. J Safety Res. 2023 Jun;85:114-128. Umer W, Antwi-Afari MF, Li H, Szeto GPY, Wong AYL. The prevalence of musculoskeletal symptoms in the construction industry: a systematic review and meta-analysis. Int Arch Occup Environ Health. 2018 Feb;91(2):125-144. Wang X, Dong XS, Choi SD, Dement J. Work-related musculoskeletal disorders among construction workers in the United States from 1992 to 2014. Occup Environ Med. 2017 May;74(5):374-380. Park J, Kim Y, Han B. Work Sectors with High Risk for Work-Related Musculoskeletal Disorders in Korean Men and Women. Saf Health Work. 2018 Mar;9(1):75-78. Derakhshan Jazari M, Jahangiri M, Khaleghi H, Abbasi N, Hassanipour S, Shakerian M, Kamalinia M. Prevalence of self-reported work-related illness and injuries among building construction workers, Shiraz, Iran. EXCLI J. 2018 Jul 25;17:724-733. da Costa BR, Vieira ER. Risk factors for work-related musculoskeletal disorders: A systematic review of recent longitudinal studies. Am J Ind Med. 2010 Mar;53(3):285-323. Evanoff B, Dale AM, Descatha A. A conceptual model of musculoskeletal disorders for occupational health practitioners. Int J Occup Med Environ Health. 2014 Jan;27(1):145-8. Stock SR, Nicolakakis N, Vézina N, Vézina M, Gilbert L, Turcot A, Sultan-Taïeb H, Sinden K, Denis MA, Delga C, Beaucage C. Are work organization interventions effective in preventing or reducing work-related musculoskeletal disorders? A systematic review of the literature. Scand J Work Environ Health. 2018 Mar 1;44(2):113-133. van Rijn RM, Huisstede BM, Koes BW, Burdorf A. Associations between work-related factors and specific disorders of the shoulder--a systematic review of the literature. Scand J Work Environ Health. 2010 May;36(3):189-201. International Labour Office and International Ergonomics Association. Ergonomic checkpoints: Practical and easy-to-implement solutions for improving safety, health and working conditions. Second edition. International Labour Office, Geneva, 2010. NIOSH [2014]. Observation-based posture assessment: review of current practice and recommendations for improvement. By Lowe BD, Weir PL, Andrews DM. Cincinnati, OH: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, DHHS (NIOSH) Publication No. 2014–131. Yang L, Hildebrandt VH, Yu SF, Ling RJ, He LH, Chen WH, Xia ZL, Wang JX, Li LP, Wang S. Musculoskeletal Disorders Questionnaire Introduction Attached questionnaire. Ind Health Occup Dis. 2009;35:25–31.Salvendy G. Handbook of human factors and ergonomics. 4. Hoboken: John Wiley & Sons, Inc.; 2012. Wang Y, Zhao L, Gao L, Pan A, Xue H. Health policy and public health implications of obesity in China. Lancet Diabetes Endocrinol. 2021 July;9(7):446-461. Kee D. Systematic Comparison of OWAS, RULA, and REBA Based on a Literature Review. Int J Environ Res Public Health. 2022 Jan 5;19(1):595. McAtamney L, Nigel CE. RULA: a survey method for the investigation of work-related upper limb disorders. Appl Ergon, 1993, 24(2): 91-99. Hignett S, McAtamney L. Rapid entire body assessment (REBA). Appl Ergon, 2000, 31(2): 201-205. Gomez-Galan M, Perez-Alonso J, Callejon-Ferre AJ, et al. Musculoskeletal disorders: OWAS review. Ind Health, 2017, 55(4): 314-337. Kee D. Comparison of OWAS, RULA and REBA for assessing potential work-related musculoskeletal disorders. Int J Ind Ergon, 2021, 83: 103140. Balsalobre-Fernandez C, Tejero-Gonzalez CM, Del CJ, et al. The concurrent validity and reliability of a low-cost, high-speed camera-based method for measuring the flight time of vertical jumps. J Strength Cond Res, 2014, 28(2): 528-533. Elwardany SH, El-Sayed WH, Ali MF. Reliability of Kinovea Computer Program in Measuring Cervical Range of Motion in Sagittal Plane. OALib, 2015, 02(09): 1-10. Ergonomics—Evaluation of Static Working Postures. International Standard Organization (ISO); Geneva, Switzerland: 2000. Basic human body measurements for technological design—Part 1: Body measurement definitions and landmarks. International Standard Organization (ISO); Geneva, Switzerland: 2017. van T Hullenaar CDP, Bos P, Broeders IAMJ. Ergonomic assessment of the first assistant during robot-assisted surgery. J Robot Surg, 2019, 13(2): 283-288. Rigby AS. Statistical methods in epidemiology. v. Towards an understanding of the kappa coefficient. Disabil Rehabil, 2000, 22(8): 339-344. Wang X, Dong XS, Choi SD, Dement J. Work-related musculoskeletal disorders among construction workers in the United States from 1992 to 2014. Occup Environ Med. 2017 May;74(5):374-380. Kashif M, Albalwi A, Raqib A, Farooq M, Ullah R, Sakoor M, Kamran Z. Work-related musculoskeletal disorders among Pakistani construction workers: Prevalence, characteristics, and associated risk factors. Work. 2022;72(1):119-126. Lee YC, Hong X, Man SS. Prevalence and Associated Factors of Work-Related Musculoskeletal Disorders Symptoms among Construction Workers: A Cross-Sectional Study in South China. Int J Environ Res Public Health. 2023 Mar 6;20(5):4653. Su Y, Jin KZH, Pinnaduwage Vijitha DESILVA, Truls ØSTBYE. [Comparisons of occupational ergonomic exposure patterns and health status of Chinese expatriate and local construction workers in Sri Lanka]. Journal of Environmental and Occupational Medicine. 2021,38(02):105-112. Esmaeili SV, Alboghobeish A, Izadi N, Azizi F, Dorfeshan F, Salehi Sahlabadi A. Predictive power of the HEMPA risk assessment method for musculoskeletal disorders in nurses and caregivers: insights and implications. BMC Nurs. 2025 Jul 1;24(1):811. Chen C, Xiao B, He X, Wu J, Li W, Yan M. Prevalence of low back pain in professional drivers: a meta-analysis. Public Health. 2024 Jun;231:23-30. Umer W, Antwi-Afari MF, Li H, et al. The prevalence of musculoskeletal symptoms in the construction industry: a systematic review and meta-analysis. Int Arch Occup Environ Health, 2018, 91(2): 125-144. Gherscovici ED, Mayer JM. Relationship of Healthy Building Determinants With Musculoskeletal Disorders of the Extremities: A Systematic Review. Cureus. 2023 Apr 11;15(4):e37456. Le TTT, Jalayondeja W, Mekhora K, et al. Prevalence and risk factors of work-related musculoskeletal disorders among physical therapists in Ho Chi Minh City, Vietnam. BMC Public Health, 2024, 24(1): 6. Jia N, Zhang MB, Xu Q, et al. [Study on the epidemiological characteristics of work-related musculoskeletal disorders among 29 industries or occupational groups in China]. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi. 2024 Dec 20;42(12):884-895. Cano A. Pain catastrophizing and social support in married individuals with chronic pain: the moderating role of pain duration. Pain, 2004, 110(3): 656-664. Chen B, Li L, Donovan C, et al. Prevalence and characteristics of chronic body pain in China: a national study. Springerplus, 2016, 5(1): 938. Alleyne J, Dopico AM. Alcohol Use Disorders and Their Harmful Effects on the Contractility of Skeletal, Cardiac and Smooth Muscles. Adv Drug Alcohol Res, 2021, 1. Kirsch MJ, Blafoss R, Sundstrup E, et al. Association between lifestyle and musculoskeletal pain: cross-sectional study among 10,000 adults from the general working population. BMC Musculoskelet Disord, 2019, 20(1): 609. Smith BE, Hendrick P, Bateman M, et al. Musculoskeletal pain and exercise-challenging existing paradigms and introducing new. Br J Sports Med, 2019, 53(14): 907-912. Moreira RF, Foltran FA, Albuquerque-Sendin F, et al. Comparison of randomized and non-randomized controlled trials evidence regarding the effectiveness of workplace exercise on musculoskeletal pain control. Work, 2012, 41 Suppl 1: 4782-4789. Armon G, Melamed S, Shirom A, et al. Elevated burnout predicts the onset of musculoskeletal pain among apparently healthy employees. J Occup Health Psychol, 2010, 15(4): 399-408. Dzakpasu F, Carver A, Brakenridge CJ, et al. Musculoskeletal pain and sedentary behaviour in occupational and non-occupational settings: a systematic review with meta-analysis. Int J Behav Nutr Phys Act, 2021, 18(1): 159. Chen N, Zhang H, Zhao J, Wang S. [Biomechanics Analysis and Improvement of Working Posture of Steel Fixer]. Industrial Safety and Environmental Protection, 2017, 43(12): 37-40+54. Pope MH, Goh KL, Magnusson ML. Spine ergonomics. Annu Rev Biomed Eng, 2002, 4: 49-68. Jia N, Zhang M, Zhang H, et al. Prevalence and risk factors analysis for low back pain among occupational groups in key industries of China. BMC Public Health, 2022, 22(1): 1493. Antwi-Afari MF, Li H, Edwards DJ, et al. Biomechanical analysis of risk factors for work-related musculoskeletal disorders during repetitive lifting task in construction workers. Autom Constr, 2017, 83: 41-47. Wang A, Zhou Y, Li X, et al. Investigating and analyzing the current situation and factors influencing chronic neck, shoulder, and lumbar back pain among medical personnel after the epidemic. BMC Musculoskelet Disord, 2024, 25(1): 316. Movahed M, Ohashi JY, Kurustien N, et al. Fatigue sensation, electromyographical and hemodynamic changes of low back muscles during repeated static contraction. Eur J Appl Physiol, 2011, 111(3): 459-467. Waersted M, Koch M, Veiersted KB. Work above shoulder level and shoulder complaints: a systematic review. Int Arch Occup Environ Health, 2020, 93(8): 925-954. Ervasti J, Pentti J, Nyberg ST, et al. Long working hours and risk of 50 health conditions and mortality outcomes: a multicohort study in four European countries. Lancet Reg Health Eur, 2021, 11: 100212. Bao SS, Kapellusch JM, Merryweather AS, et al. Relationships between job organisational factors, biomechanical and psychosocial exposures. Ergonomics, 2016, 59(2): 179-194. Matre D, Christensen JO, Mork PJ, Ferreira P, Sand T, Nilsen KB. Shift work, inflammation and musculoskeletal pain-The HUNT Study. Occup Med (Lond). 2021 Dec 24;71(9):422-427. Stock SR, Nicolakakis N, VÃ Zina N, et al. Are work organization interventions effective in preventing or reducing work-related musculoskeletal disorders? A systematic review of the literature. Scandinavian Journal of Work, Environment & Health, 2017. Gariazzo C, Taiano L, Bonafede M, et al. Association between extreme temperature exposure and occupational injuries among construction workers in Italy: An analysis of risk factors. Environ Int, 2023, 171: 107677. Gibb K, Beckman S, Vergara XP, et al. Extreme Heat and Occupational Health Risks. Annu Rev Public Health, 2024, 45(1): 315-335. Ayvaz O, Ozyildirim BA, Issever H, et al. Ergonomic risk assessment of working postures of nurses working in a medical faculty hospital with REBA and RULA methods. Sci Prog, 2023, 106(4): 332187036. Chen N, Li G, Sun X, et al. Prevalence status and associated factors of wrist postural injury in the Chinese occupational population. Front Public Health, 2022, 10: 1047814. Namwongsa S, Puntumetakul R, Neubert MS, et al. Ergonomic risk assessment of smartphone users using the Rapid Upper Limb Assessment (RULA) tool. PLoS One, 2018, 13(8): e203394. Barr AE, Barbe MF, Clark BD. Work-related musculoskeletal disorders of the hand and wrist: epidemiology, pathophysiology, and sensorimotor changes. J Orthop Sports Phys Ther. 2004 Oct;34(10):610-27. Zhang X, Jia N, Sun X, et al. [Structural equation analysis and modeling of fect and ankles WMSDs and its adverse ergonomic factors]. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi. 2025 Feb 20;43(2):101-109. Micheletti Cremasco M, Giustetto A, Caffaro F, et al. Risk Assessment for Musculoskeletal Disorders in Forestry: A Comparison between RULA and REBA in the Manual Feeding of a Wood-Chipper. International journal of environmental research and public health, 2019, 16(5): 793. Domingo JRT, Pano MTSD, Ecat DAG, et al. Risk Assessment on Filipino Construction Workers. Procedia Manufacturing, 2015, 3: 1854-186. Additional Declarations No competing interests reported. Supplementary Files supplementfile.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 06 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviews received at journal 01 May, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 10 Apr, 2026 Editor assigned by journal 07 Apr, 2026 Editor invited by journal 06 Apr, 2026 Submission checks completed at journal 06 Apr, 2026 First submitted to journal 06 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9149878","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625034194,"identity":"7d6204fc-6263-4863-a2c0-9e46e825b7c7","order_by":0,"name":"Xiongda He","email":"","orcid":"","institution":"Guangdong Province Hospital for Occupational Disease Prevention and Treatment","correspondingAuthor":false,"prefix":"","firstName":"Xiongda","middleName":"","lastName":"He","suffix":""},{"id":625034196,"identity":"9907ba2b-54bb-4383-bf23-44e863012876","order_by":1,"name":"Chunshuo Chen","email":"","orcid":"","institution":"Guangdong Province Hospital for Occupational Disease Prevention and Treatment","correspondingAuthor":false,"prefix":"","firstName":"Chunshuo","middleName":"","lastName":"Chen","suffix":""},{"id":625034198,"identity":"0fbf282b-aa51-4b0a-a311-682657e13596","order_by":2,"name":"Bin Xiao","email":"","orcid":"","institution":"Guangdong Province Hospital for Occupational Disease Prevention and Treatment","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Xiao","suffix":""},{"id":625034200,"identity":"70bc761f-70b0-4b53-bee5-22fe13b5ee7e","order_by":3,"name":"Zhipeng He","email":"","orcid":"","institution":"Guangdong Province Hospital for Occupational Disease Prevention and Treatment","correspondingAuthor":false,"prefix":"","firstName":"Zhipeng","middleName":"","lastName":"He","suffix":""},{"id":625034204,"identity":"2b0b526a-8302-415b-87fc-8dab90d05865","order_by":4,"name":"Guoyong Xu","email":"","orcid":"","institution":"Guangdong Province Hospital for Occupational Disease Prevention and Treatment","correspondingAuthor":false,"prefix":"","firstName":"Guoyong","middleName":"","lastName":"Xu","suffix":""},{"id":625034210,"identity":"9c2622e4-0a0c-4107-bd15-134eb63bd379","order_by":5,"name":"Hua Yan","email":"","orcid":"","institution":"Guangdong Province Hospital for Occupational Disease Prevention and Treatment","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Yan","suffix":""},{"id":625034211,"identity":"1eea67d9-32ac-434d-aff7-63840c686a99","order_by":6,"name":"Yongjian Jiang","email":"","orcid":"","institution":"Guangdong Province Hospital for Occupational Disease Prevention and Treatment","correspondingAuthor":false,"prefix":"","firstName":"Yongjian","middleName":"","lastName":"Jiang","suffix":""},{"id":625034214,"identity":"95b411d3-922e-4876-99d3-cf573b90795b","order_by":7,"name":"Junle Wu","email":"","orcid":"","institution":"Guangdong Province Hospital for Occupational Disease Prevention and Treatment","correspondingAuthor":false,"prefix":"","firstName":"Junle","middleName":"","lastName":"Wu","suffix":""},{"id":625034216,"identity":"6254c70a-bdef-4197-ad7b-378083564646","order_by":8,"name":"Jianyu Guo","email":"","orcid":"","institution":"Guangdong Province Hospital for Occupational Disease Prevention and Treatment","correspondingAuthor":false,"prefix":"","firstName":"Jianyu","middleName":"","lastName":"Guo","suffix":""},{"id":625034217,"identity":"f2b319a6-5c32-4f3d-b60c-df977ed723ff","order_by":9,"name":"Yanhui Lan","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanhui","middleName":"","lastName":"Lan","suffix":""},{"id":625034219,"identity":"9f63469f-b325-41e0-add7-ff3b4ac25c77","order_by":10,"name":"Xinyi Zou","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Zou","suffix":""},{"id":625034221,"identity":"53ac459e-3c9a-4ad7-ae10-a31b4c90900c","order_by":11,"name":"Yuan Qiu","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Qiu","suffix":""},{"id":625034222,"identity":"1fc68233-99c5-4781-92e1-e734b903aab7","order_by":12,"name":"Maosheng Yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYLACxgYbHn72BtK0pMlI9hwgTcthG4MbDkSqNjh+9ujGnzvO8zDcYGD88DGHGC1n8tJu8565zcM4u4FZcuY2YrQcyDG7zdh2m4dZ5gAbMy9RWs6/Mbv5s+0cD5tEArFabuSY3eBtO8DDQ7QWyRtvzG7ztiXzSPAcbCbOL3znc0AOs7O3P9588MNHYrQoHIAzGRuIUA8E8kSqGwWjYBSMgpEMAP2DObej7Ly1AAAAAElFTkSuQmCC","orcid":"","institution":"Guangdong Province Hospital for Occupational Disease Prevention and Treatment","correspondingAuthor":true,"prefix":"","firstName":"Maosheng","middleName":"","lastName":"Yan","suffix":""}],"badges":[],"createdAt":"2026-03-17 14:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9149878/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9149878/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107484808,"identity":"2607a25f-6142-4edd-a168-25a7d8b7a328","added_by":"auto","created_at":"2026-04-22 02:33:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":743382,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFront and three-quarter views of construction worker at work\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9149878/v1/b30001a02d7b5c607352653e.png"},{"id":107359290,"identity":"61c190da-b2c3-4d45-9218-606322efb4f2","added_by":"auto","created_at":"2026-04-20 17:48:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":580771,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagram of extracting the human joint angles\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9149878/v1/51da2065fb11aafcd13c7523.png"},{"id":109252353,"identity":"ae96ab8e-529c-4b10-8889-7f93afe6e247","added_by":"auto","created_at":"2026-05-14 09:25:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2519501,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9149878/v1/ef6fa2af-9634-4040-82f9-e4a4ebcf65dd.pdf"},{"id":107488569,"identity":"796afe75-89ef-4893-a9de-6b7a06d0f6d6","added_by":"auto","created_at":"2026-04-22 02:45:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2347784,"visible":true,"origin":"","legend":"","description":"","filename":"supplementfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-9149878/v1/ac26f0552aba627af72ccd4d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigation of Work-related Musculoskeletal Disorders among Chinese Construction Workers: An Integrated Application of Questionnaire and Observational Methods","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWork-related musculoskeletal disorders (WMSDs) refer to health issues of muscles, tendons, joints, cartilage, ligaments, and nerves induced or aggravated by work, presenting with discomfort, numbness, pain, and limited mobility [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As a major subset of musculoskeletal disorders (MSDs)\u0026mdash;the second leading cause of non-fatal disability globally (affecting over 1.63\u0026nbsp;billion people [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e])\u0026mdash;WMSDs pose critical challenges to public health and social economy, especially since occupational ergonomic factors are the primary contributor to MSD-related disability-adjusted life years (DALYs) among 15\u0026ndash;39-year-old laborers [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe construction industry, a cornerstone of national economies (accounting for 9%\u0026ndash;15% of GDP globally [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]), has grown rapidly in China, with its added value accounting for over 6.70% of GDP since 2014 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, limitations such as inadequate supervision, labor-intensive work, and insufficient occupational health education threaten workers\u0026rsquo; health, making construction workers highly susceptible to WMSDs [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Meta-analyses and surveys confirm high WMSDs prevalence: 51.1% for lower back WMSDs among construction workers [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], with higher rates in the U.S. construction industry than the national average [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and elevated risks for male workers in Korea [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and as the most common work-related disease in Iran [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. WMSDs are linked to ergonomic factors including overexertion, prolonged awkward/static postures, repetitive operations, vibration, and poor work organization [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], highlighting the need for ergonomic interventions in construction [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBiomechanical factors are key drivers of WMSDs development [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], with three primary assessment approaches: (1) subjective self-report questionnaires (simple but less valid); (2) observation-based tools (e.g., RULA, REBA, OWAS) via real-time observation/video (cost-effective, accessible, and practical for on-site use, with higher validity than self-report [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]); and (3) direct measurements (highly valid but complex and resource-intensive). Integrating multiple methods is optimal for comprehensive risk evaluation.\u003c/p\u003e \u003cp\u003eFew large-scale studies on WMSDs among Chinese construction workers exist. Thus, this study aims to investigate the distribution and risk factors of WMSDs in this population using a combination of questionnaires and observation-based methods, compare the effectiveness of different observational tools to identify the most suitable one, and provide a theoretical basis for targeted ergonomic interventions, WMSDs prevention, and sustainable health promotion for workers and the industry.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Questionnaire Survey\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Participants\u003c/h2\u003e \u003cp\u003eUsing convenience sampling, all eligible construction workers from 10 large-scale construction companies (two companies per city) in five coastal cities\u0026mdash;Huizhou City and Lufeng City in Guangdong Province, Fangchenggang City in Guangxi Zhuang Autonomous Region, Wenzhou City in Zhejiang Province, and Ningde City in Fujian Province\u0026mdash;were selected as study subjects between February 2023 and August 2024. The participant selection criteria were as follows: (1) aged 18 years or above, with at least one year of work experience in their current jobs; (2) pregnant women, individuals diagnosed with rheumatic diseases, trauma, other medical emergencies, physical disabilities or sequelae of diseases, tumours, other non-occupational musculoskeletal diseases, and those with cognitive or behavioural disorders were excluded. All participants read and signed the informed consent form for this survey.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 The content of questionnaire\u003c/h2\u003e \u003cp\u003eThe questionnaire of this survey, adapted from the Musculoskeletal Disorders Questionnaire (Chinese Version) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], consisted of five parts.\u003c/p\u003e \u003cp\u003eThe first part, demographic information, included gender, age, height, weight, body mass index (BMI), educational level, smoking, alcohol consumption, etc.\u003c/p\u003e \u003cp\u003eThe second section assessed whether the participants had experienced any discomfort symptoms\u0026mdash;including numbness, weakness, stiffness, fatigue, pain, or limited mobility\u0026mdash;in any of the following nine body regions during the past 12 months: neck, shoulders, upper back, elbows, lower back/waist, hands/wrists, hips/thighs, knees, and ankles/feet. Additionally, information regarding symptom frequency, duration, and severity was collected.\u003c/p\u003e \u003cp\u003eThe third part evaluated workers' engagement in the following tasks during a typical workday: sedentary work, standing work, squatting or kneeling work, heavy material handling, operation of vibrating tools, upper limb or hand-intensive tasks, work requiring awkward postures, repetitive operations, etc.\u003c/p\u003e \u003cp\u003eThe fourth part was about the working organisation and environment of participants, such as work duration, shift schedules, staff shortages, high-temperature occupational exposures, etc.\u003c/p\u003e \u003cp\u003eThe final section evaluated the requirement to maintain awkward postures for prolonged periods and/or perform repetitive motions during work tasks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 Definitions\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section4\"\u003e \u003ch2\u003e2.1.3.1 WMSDs\u003c/h2\u003e \u003cp\u003eAccording to NIOSH, WMSDs in this study were defined as follows: (1) discomfort within the past year; (2) discomfort beginning after employment in the current job; (3) no prior accident or sudden injury (affecting a focal area of discomfort); and (4) episodes of discomfort occurring monthly or, if not every month, at least exceeding a one-week period of discomfort [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.1.4 Data Quality Control\u003c/h2\u003e \u003cp\u003ePrior to the survey, trained investigators explained the questionnaire content and instructions to all participants using a standardized script to ensure comprehension. Workers were then individually guided to scan a QR code and complete the electronic questionnaire in a one-to-many facilitation setting. The digital system incorporated built-in completeness checks and logical validation to ensure data integrity. Upon completion of the survey, the database was downloaded and the data were checked for inconsistencies prior to formal analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.1.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using SPSS 26.0 (IBM Corp., Armonk, NY, USA). Measurement data that conformed to a normal distribution were described as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while data not conforming to a normal distribution were described as median and interquartile range(25th \u0026ndash; 75th percentiles). Univariate analysis of WMSDs was conducted using the Chi-square test, and multivariate analysis was performed using logistic regression with the forward stepwise method (entry criterion: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, removal criterion: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10). The significance level was set at α\u0026thinsp;=\u0026thinsp;0.05 (two-tailed).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Observation-based methods\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Participants\u003c/h2\u003e \u003cp\u003eThe selection of participants was conducted as follows:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eKey construction trades were identified based on data from the first-phase questionnaire.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eA simple random sampling method was employed to select at least three workers from each of the ten participating enterprises.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe sampling process was carried out from August 2024 to January 2025.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWorkers who were in good general health, had reported no WMSDs symptoms within the preceding six months, adhered to relevant operational procedures, and demonstrated proficiency in their respective tasks were selected as observation subjects.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Video recording\u003c/h2\u003e \u003cp\u003e \u003cp\u003eThe Fluorite S3 Sports Camera was used to record video footage of construction workers' activities from both frontal and lateral perspectives. The lateral perspective (left or right) was determined based on the worker's dominant hand during task performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eVideo recording was initiated 2\u0026ndash;3 seconds prior to the commencement of the work activity. The objective was to capture a minimum of three complete work cycles, with each session having a duration of no less than three minutes.\u003c/p\u003e \u003cp\u003eThe specifications for video recording adhered to the guidelines stipulated by the National Institute for Occupational Safety and Health (NIOSH) in their publication, \"Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement\" [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Risk Assessment\u003c/h2\u003e \u003cp\u003eThe recorded footage was examined using a frame-by-frame approach to identify key postures for analysis, guided by selection criteria that prioritized those with the highest repetition, longest sustained duration, and established links to musculoskeletal discomfort.\u003c/p\u003e \u003cp\u003eA risk assessment was conducted on the key postures utilizing three established ergonomic tools: the Rapid Upper Limb Assessment (RULA), the Rapid Entire Body Assessment (REBA), and the Ovako Working Posture Analysis System (OWAS), which are most frequently employed in industries for assessing the biomechanical load of the whole body [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The RULA method was designed for the rapid assessment of neck, trunk, and upper limb postures, muscle function, and external loads [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The REBA technique offers a postural analysis system sensitive to musculoskeletal risks across diverse tasks, with particular applicability in healthcare and other service industries [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The OWAS method, developed by the Finnish steel company Ovako Oy, categorizes four back postures, three arm postures, seven lower-limb postures, and three load/force categories [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. To facilitate a comparative analysis across the three assessment methods, a unified four-level risk categorization was adopted. Following the approach of Kee [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], the original five-level REBA score was reclassified into four levels: Level 1 (original 0), Level 2 (original 1\u0026ndash;2), Level 3 (original 3), and Level 4 (original 4), ensuring consistency with the risk level descriptors of the other methods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Extraction of Human Joint Angle\u003c/h2\u003e \u003cp\u003eJoint angles of construction workers were extracted using the Kinovea software (Version 0.9.5). This open-source tool facilitates detailed motion analysis by enabling frame-by-frame playback, zoom functionality, and the identification of skeletal landmarks to quantify body segment angles during dynamic tasks. Its user-friendly design allows for the acquisition of accurate and reliable measurements without requiring advanced technical expertise [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo standardize the assessment, anatomical landmarks were defined according to the international standards ISO 11226: 2000 EN [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and ISO 7250-1:2017 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], supplemented by the methodology of van T Hullenaar [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This framework provided the basis for uniformly defining the range of motion measurements for the body segments specified in the RULA, REBA, and OWAS worksheets. Given the complexity and frequent occlusion of wrist movements during tasks, wrist postures were determined through a synthesis of direct observation, worker inquiry, video analysis, and group discussion, with risk levels assigned directly based on the respective scoring criteria of each assessment tool.\u003c/p\u003e \u003cp\u003eThe calibration and measurement for the range of motion of human joints were shown in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Quality Control\u003c/h2\u003e \u003cp\u003eAll investigators should receive standardized training prior to video recording to ensure familiarity with the recording protocols, competence in technical specifications, and accurate application of each assessment scale. Before the start of recording, participants should be thoroughly informed of the study objectives and procedural details. Throughout the recording process, investigators must avoid interfering with the normal work activities of the operators. Identification of critical working postures is performed independently by two ergonomics specialists through video analysis according to predefined criteria. In cases of disagreement, a third domain expert performs a blinded review to resolve the discrepancies and establish a consensus.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.2.6 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed with SPSS Statistics, Version 26.0 (IBM Corp., Armonk, NY, USA).\u003c/p\u003e \u003cp\u003e(1) Means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations were used for continuous variables while counts and percentages were used for categorical variables. For data that did not conform to a normal distribution, the median and interquartile range were used for description.\u003c/p\u003e \u003cp\u003e(2) Inter-rater agreement between assessment methods was evaluated using linearly weighted Kappa statistics. The strength of agreement was interpreted as follows: poor for Kappa\u0026thinsp;\u0026lt;\u0026thinsp;0.2, fair for 0.2\u0026ndash;0.4, moderate for 0.4\u0026ndash;0.6, good for 0.6\u0026ndash;0.8, and very good for 0.8\u0026ndash;1.0 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Differences in risk ratings across different work tasks were analyzed with the \u003cem\u003eKruskal\u0026ndash;Wallis test\u003c/em\u003e (\u003cem\u003eH-test\u003c/em\u003e). Post-hoc pairwise comparisons were conducted using \u003cem\u003eDunn's test\u003c/em\u003e with Bonferroni adjustment. A corrected p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant, with the adjusted significance level (\u003cem\u003eα'\u003c/em\u003e) calculated as the original alpha (\u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05) divided by the number of comparisons (two-tailed).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Questionnaire Survey\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.1 Demographic characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 11,994 construction workers were initially invited to participate in the survey. After applying the inclusion and exclusion criteria, 10,781 valid questionnaires were retained, yielding an effective response rate of 89.9%. The study population was predominantly male (88.9%). The mean age of participants was 39.9 (\u0026plusmn;10.9) years. The mean BMI was 24.3 (\u0026plusmn;3.6) kg/m\u0026sup2;. In terms of educational attainment, the vast majority (92.8%) had completed high school or higher. Regarding marital status, 7,759 participants (72.0%) were married, while 3,022 (28.0%) were unmarried or fell into other categories (e.g., divorced or widowed). Smoking and drinking habits were reported by 5,358 (49.7%) and 6,074 (56.3%) participants, respectively. The average work experience in the construction industry was 4.2 (\u0026plusmn;5.1) years. After work, 4,505 participants (41.8%) engaged in occasional physical exercise, and 1,095 (10.2%) exercised at least twice per month. The most common occupations reported were general laborer (28.0%), rebar worker (16.2%), and carpenter (14.8%). Detailed information is presented in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 \u0026nbsp;General demographic characteristics of the objects\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndicators\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProportion (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e1196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e9585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e88.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026lt;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e2245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e20.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e2760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e25.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e3109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e28.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026ge;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e2667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e24.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e18.5-23.9 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e5051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e46.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026ge;24.0 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e5360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e49.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eMiddle school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eHigh school or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e10000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e92.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eUnmarried, divorced, widowed, etc.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e3022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e7759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e72.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\u003cstrong\u003e\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003cp\u003e\u003cstrong\u003eSmoke\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e4166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e38.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e5358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e49.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eSmoking cessation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e1257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e3868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e35.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e6074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e56.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAlcohol abstinence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWork experience (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026le;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e4832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e44.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e3-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e4258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e39.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e6-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical exercise after work\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e5181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e48.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026le;2 times per month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e4505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e41.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026gt;2 times per month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e1095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eGeneral Laborer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e3014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eRebar Worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e1749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eCarpenter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e1591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eScaffolder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eRoofer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eWelder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eCrane Operator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eConcrete Worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eInstaller\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003ePainter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eConstruction Vehicle Driver \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003eOther \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 28px;\"\u003e\n \u003cp\u003e516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 32px;\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote: a\u003c/strong\u003e \u0026quot;Construction Vehicle Operator\u0026quot; refers to personnel who operate dedicated construction site vehicles, such as trucks, trailers, forklifts, dump trucks, excavators, cranes, and concrete mixers. \u003cstrong\u003eb\u0026nbsp;\u003c/strong\u003eThe \u0026quot;Other\u0026quot; occupational category includes trades like electricians, mechanics, bricklayers, and insulation workers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.2 The prevalence of WMSDs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall prevalence of WMSDs among the construction workers was 27.4%. The most frequently affected body regions were the shoulders (10.6%), lower back/lumbar area (10.4%), neck (10.3%), and feet/ankles (10.1%). The differences in WMSD prevalence across body regions were statistically significant (trend \u003cem\u003e\u0026chi;\u0026sup2;\u0026nbsp;\u003c/em\u003e= 594.69, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eAmong the various occupational trades, welders, riveters, carpenters, and rebar workers showed relatively higher overall WMSD prevalence, at 34.9% (271/776), 33.5% (266/795), 28.5% (453/1591), and 27.5% (481/1749), respectively. Statistically significant differences in prevalence of WMSDs were observed across trades for the overall rate, as well as for the neck, shoulder, lower back/lumbar, knee, and ankle/foot regions (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). Welders had the highest prevalence of WMSDs in the neck, shoulders, lower back/lumbar, and angle/foot area. The highest prevalence of ankle/foot region among all occupations was observed in welders and general laborers.. Detailed results are provided in Table S2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.3 Influencing factors of WMSDs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study focused on factors influencing work-related musculoskeletal disorders (WMSDs) of the neck, shoulder, and lower back (highly prevalent in the study population). Variables were first screened via \u003cem\u003eChi-square Test\u003c/em\u003e, followed by multivariable logistic regression analysis. Below are the key findings (all P \u0026lt; 0.05):\u003c/p\u003e\n\u003cp\u003e①Shoulder WMSDs\u003c/p\u003e\n\u003cp\u003eIndividual factors: Risk factors (former/current drinking); Protective factors (age 30\u0026ndash;39, 40\u0026ndash;49, or \u0026ge;50 years; married; leisure-time physical exercise \u0026le;2 times/month)\u003c/p\u003e\n\u003cp\u003eWork tasks: Risk factors (sedentary work \u0026gt;4 hours/day; uncomfortable postures for 1\u0026ndash;4 hours/day or \u0026gt;4 hours/day)\u003c/p\u003e\n\u003cp\u003eWork organization: Risk factors (weekly working time \u0026gt;64 hours; daily repetitive tasks; high-temperature exposure; shift work; understaffing); Protective factor (adequate rest periods)\u003c/p\u003e\n\u003cp\u003eWorking postures: Risk factors (frequent repetitive lower back movements; hand positions above shoulder level during work)\u003c/p\u003e\n\u003cp\u003e②Lower Back WMSDs\u003c/p\u003e\n\u003cp\u003eIndividual factors: Risk factors (drinking; severe post-work fatigue); Protective factors (married; leisure-time physical exercise \u0026le;2 times/month)\u003c/p\u003e\n\u003cp\u003eWork tasks: Risk factors (sedentary work \u0026gt;4 hours/day; squatting/kneeling for 1\u0026ndash;4 hours/day or \u0026gt;4 hours/day; uncomfortable postures for 1\u0026ndash;4 hours/day or \u0026gt;4 hours/day)\u003c/p\u003e\n\u003cp\u003eWork organization: Risk factors (weekly working time \u0026gt;64 hours; daily repetitive tasks; frequent overtime; understaffing); Protective factor (adequate rest periods)\u003c/p\u003e\n\u003cp\u003eWorking postures: Risk factors (frequent moderate/severe lower back flexion; repetitive lower back movements)\u003c/p\u003e\n\u003cp\u003e③Neck WMSDs\u003c/p\u003e\n\u003cp\u003eIndividual factors: Risk factor (severe post-work fatigue); Protective factors (age 30\u0026ndash;39, 40\u0026ndash;49, or \u0026ge;50 years; married; leisure-time physical activity \u0026le;2 times/month)\u003c/p\u003e\n\u003cp\u003eWork tasks: Risk factors (sedentary work for 1\u0026ndash;4 hours/day or \u0026gt;4 hours/day; uncomfortable postures for 1\u0026ndash;4 hours/day or \u0026gt;4 hours/day)\u003c/p\u003e\n\u003cp\u003eWork organization: Risk factors (weekly working time \u0026gt;64 hours; daily repetitive tasks; shift work; frequent overtime; understaffing); Protective factors (task rotation among colleagues; adequate rest breaks)\u003c/p\u003e\n\u003cp\u003eWorking postures: Risk factors (frequent moderate/severe lower back bending; repetitive lower back movements)\u003c/p\u003e\n\u003cp\u003eDetailed results are presented in Table 2.\u003cstrong\u003e\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 \u0026nbsp; The results of logistic regression analysis on the influencing factors of WMSDs among construction workers\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfluencing\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFactors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 439px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(95%\u003cem\u003eCI\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShoulder WMSDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower Back WMSDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeck WMSDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eyears\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u0026lt;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.667(0.550-0.817)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.745(0.608~0.914)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.601(0.483-0.748)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.730(0.584~0.913)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u0026ge;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.671(0.531-0.848)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.717(0.564~0.911)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003eUnmarried, divorced, widowed, etc.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.709(0.594-0.845)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.741(0.644-0.851)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.745(0.622~0.892)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003eAlcohol abstinence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.379(1.069-1.777)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.248(0.957-1.628)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.193(1.035-1.374)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.348(1.164-1.560)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical exercise after work\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u0026le;2 times per month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.803(0.699-0.923)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.762(0.661-0.880)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.765(0.663~0.882)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u0026gt;2 times per month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e0.916(0.729-1.152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1.104(0.884-1.379)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1.017(0.814~1.271)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-work fatigue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1.223(0.769-1.947)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1.339(0.852~2.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.646(1.040-2.606)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.690(1.079~2.647)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSedentary work\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u0026lt;1 hours per day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e1-4 hours per day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1.048(0.891-1.231)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.109(0.942-1.305)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.296(1.103~1.524)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u0026gt;4 hours per day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.196(1.030-1.388)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.329(1.141-1.549)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.571(1.341~1.840)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eW\u003c/strong\u003e\u003cstrong\u003eorking in a squatting or kneeling position\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u0026lt;1 hours per day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e1-4 hours per day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.332(1.151~1.542)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u0026gt;4 hours per day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.610(1.309~1.981)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: \u0026ldquo;*\u0026rdquo; for \u0026ldquo;\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05\u0026rdquo;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContinued Table 2 \u0026nbsp;The results of logistic regression analysis on the influencing factors of WMSDs among construction workers\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfluencing\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFactors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 439px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(95%\u003cem\u003eCI\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShoulder WMSDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower Back WMSDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeck WMSDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking in uncomfortable postures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u0026lt;1 hours per day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e1-4 hours per day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.486(1.279-1.727)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.490(1.273-1.744)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.483(1.272~1.730)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u0026gt;4 hours per day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.947(1.631-2.323)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.938(1.613-2.328)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.012(1.681~2.408)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeekly working time\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003ehours\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u0026le;48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e49-56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1.014(0.801-1.285)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1.022(0.802-1.303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e0.978(0.771~1.240)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e57-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1.124(0.894-1.415)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1.030(0.812-1.307)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e0.969(0.767~1.224)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u0026gt;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.348(1.081-1.681)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.279(1.016-1.608)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.275(1.019~1.595)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePosutre of ower back during working\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003eStraight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003eModerate bending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.250(1.067~1.465)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.180(1.014~1.373)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003eSevere bending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.635(1.355~1.973)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.230(1.017~1.488)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerforming the same tasks daily\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.339(1.156-1.551)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.543(1.321-1.801)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.481(1.272~1.725)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTask rotation among colleagues\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.800(0.681~0.941)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequent overtime\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 148px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.207(1.037-1.405)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.218(1.047~1.416)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposure to high-temperature environments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.190(1.019-1.391)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShift work\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.227(1.066-1.412)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.213(1.051~1.400)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdequate rest periods\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.658(0.573-0.755)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.589(0.511-0.680)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.638(0.553~0.735)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnderstaffing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.533(1.342-1.751)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.567(1.368-1.794)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.555(1.357~1.780)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequently performing repetitive movements with the lower back\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.570(1.249-1.972)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.518(1.184-1.946)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.290(1.034~1.608)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaintaining hand positions above shoulder level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.173(1.017-1.354)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: \u0026ldquo;*\u0026rdquo; for \u0026ldquo;\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05\u0026rdquo;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Observation-Based Posture Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1 Demographic characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the results of the questionnaire survey, our study selected rebar workers, carpenters, welders, and riveters as the key occupational groups for focus. Given that workers on construction sites often perform multiple tasks without a fixed sequence, and based on field investigations and interviews, the following operational definitions were established for the key occupations and their primary work content in this study:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eRebar Worker: A worker engaged in rebar tying activities for \u0026ge;4 hours per day.\u003c/li\u003e\n \u003cli\u003eCarpenter: A worker performing wood processing tasks for \u0026ge;4 hours per day.\u003c/li\u003e\n \u003cli\u003eWelder: A worker carrying out welding operations for \u0026ge;4 hours per day.\u003c/li\u003e\n \u003cli\u003eRiveter: A worker involved in grinding tasks for \u0026ge;4 hours per day.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAdditionally, as these occupations are predominantly male, the study population for this component was restricted to male workers.\u003c/p\u003e\n\u003cp\u003eOur study finally enrolled a total of 220 construction workers in key occupational roles, including 60 rebar workers, 60 carpenters, 50 welders, and 50 riveters. A total of 220 work videos were collected. The median age of rebar workers was 46 years (IQR: 37\u0026ndash;51), with 55 individuals (91.7%) being right-handed. Their median BMI was 24.2 kg/m\u0026sup2; (IQR: 21.7\u0026ndash;26.2), and their median work experience was 2 years (IQR: 1\u0026ndash;4). Among carpenters, the median age was 44 years (IQR: 36\u0026ndash;51), 53 (88.3%) were right-handed, the median BMI was 24.2 kg/m\u0026sup2; (IQR: 22.2\u0026ndash;26.2), and the median work experience was 3 years (IQR: 1\u0026ndash;5). Welders had a median age of 36 years (IQR: 30\u0026ndash;47), 45 (90.0%) were right-handed, a median BMI of 23.6 kg/m\u0026sup2; (IQR: 21.6\u0026ndash;27.3), and a median work experience of 4 years (IQR: 1\u0026ndash;6). For riveters, the median age was 39 years (IQR: 32\u0026ndash;46), 46 (92.0%) were right-handed, the median BMI was 24.2 kg/m\u0026sup2; (IQR: 22.3\u0026ndash;27.5), and the median work experience was 3 years (IQR: 1\u0026ndash;6).\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eA total of 220 critical working postures were selected for risk assessment according to predefined criteria, captured during rebar tying by rebar workers, wood processing by carpenters, welding by welders, and grinding by riveters.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFigure S1 through S4 illustrate the activity patterns for each operation, and Table S2 provided the corresponding postural angles of different body regions during these tasks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.2 Comparison of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eassessment\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;using the same methods for different\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ecritical\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;working postures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.2.1 RULA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile no postures were rated at Level 1 risk, the distribution of higher-risk postures varied by task. Level 4 was the most common risk level in rebar tying and grinding, followed by Level 3. Conversely, Level 3 postures predominated in wood processing and welding, followed by Level 4. Post-hoc multiple comparisons indicated a significantly higher overall risk level for rebar tying and grinding compared to wood processing and welding (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.008). Details are shown in Table S4.\u003c/p\u003e\n\u003cp\u003ePost-hoc analysis of RULA subscores identified significant inter-task differences. Localized risks were pronounced in rebar tying (for wrist flexion/rotation and leg postures) and wood processing (for forearm posture). Regarding muscular load, the risks for both repetitive/static postures (Group A and Group B) and force/load (Group A) were consistently lower in wood processing, while rebar tying and grinding showed elevated force/load risks. Consequently, the overall Group A risk ranking was: rebar tying \u0026gt; grinding \u0026gt; welding \u0026gt; wood processing (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.008). Details are shown in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 \u0026nbsp;Comparison of RULA risk levels among different tasks\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage Rank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRebar Tying\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWood Processing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWelding\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrinding\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper Arm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower Arm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e110.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e136.41\u003csup\u003ec, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e97.56\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e91.95\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWrist Bend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e159.60\u003csup\u003eb, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e90.75\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e89.68\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e96.10\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWrist Twist\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e136.83\u003csup\u003eb, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e100.17\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e105.30\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e96.50\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMuscle Use A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e128.33\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e56.83\u003csup\u003ea, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e132.00\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e132.00\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForce/Load A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e164.00\u003csup\u003eb, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e54.84\u003csup\u003ea, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e65.50\u003csup\u003ea, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e158.09\u003csup\u003eb, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWrist \u0026amp; Arm Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e169.48\u003csup\u003eb, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e53.29\u003csup\u003ea, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e87.73\u003csup\u003ea, b, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e131.15\u003csup\u003ea, b, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeck\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrunk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e115.83\u003csup\u003eb, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e108.50\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e108.50\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e108.50\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMuscle Use B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e119.83\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e83.17\u003csup\u003ea, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e123.50\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e119.10\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForce/Load B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeck, Trunk, Leg Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e124.46\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e69.28\u003csup\u003ea, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e112.08\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e141.63\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: Within the same row, pairwise comparisons were performed using \u003cem\u003eDunn\u0026rsquo; s test\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003ea.Compared with the mean rank of rebar tying, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.008;\u003c/p\u003e\n\u003cp\u003eb.Compared with the mean rank of wood processing, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.008；\u003c/p\u003e\n\u003cp\u003ec.Compared with the mean rank of welding, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.008；\u003c/p\u003e\n\u003cp\u003ed.Compared with the mean rank of grinding, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.008.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.2.2 REBA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe distribution of risk levels varied by task. In both rebar tying and welding, Level 3 postures were most frequent, followed by Level 2. For wood processing, Level 2 was predominant, followed by Level 3. During grinding, Level 3 postures accounted for the highest proportion, with Level 4 being the second most frequent. Details were shown in Table S5.\u003c/p\u003e\n\u003cp\u003eThe post hoc analysis revealed that: the postural risk for the legs in rebar tying was significantly higher than in wood processing and grinding; the risk score for Body Part A in grinding exceeded that in wood processing; the risk for wrist postures in wood processing was lower than in the other three tasks; the coupling/grip appropriateness was lower in rebar tying than in wood processing and grinding, and lower in welding than in wood processing and grinding; the risk of whole-body repetition/static activity in wood processing was lower than in the other three tasks; and the REBA risk level in grinding was higher than in wood processing (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.008). Details were shown in Table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 \u0026nbsp;Comparison of REBA risk levels among different tasks\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage Rank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRebar Tying\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWood Processing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWelding\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrinding\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeck\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrunk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e129.28\u003csup\u003eb, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e93.17\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e120.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e98.48\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForce/Load\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScore A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e107.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e95.47\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e109.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e133.07\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper Arm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower Arm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWrist\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e132.50\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e84.83\u003csup\u003ea, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e112.70\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e112.70\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoupling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e63.00\u003csup\u003eb, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e139.54\u003csup\u003ea, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e80.04\u003csup\u003eb, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e163.11\u003csup\u003ea, c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScore B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eActivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e127.77\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e56.60\u003csup\u003ea, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e134.50\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e130.46\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREBA Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e109.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e87.67\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e114.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e135.60\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: Within the same row, pairwise comparisons were performed using \u003cem\u003eDunn\u0026rsquo; s test\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003ea.Compared with the mean rank of rebar tying, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.008;\u003c/p\u003e\n\u003cp\u003eb.Compared with the mean rank of wood processing, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.008；\u003c/p\u003e\n\u003cp\u003ec.Compared with the mean rank of welding, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.008；\u003c/p\u003e\n\u003cp\u003ed.Compared with the mean rank of grinding, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.008.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.2.3 OWAS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe distribution of postural risk levels was task-specific. In rebar tying, Level 3 postures were predominant, followed by Level 1. For both wood processing and welding, Level 2 was the most frequent, followed by Level 3. In grinding, Level 2 and Level 3 postures collectively showed the highest prevalence, followed by Level 1. Details were shown in Table S6.\u003c/p\u003e\n\u003cp\u003eThe multiple comparisons revealed that the postural risk for the legs during grinding was significantly lower than during rebar tying and wood processing; the manual lifting risk in wood processing was significantly higher than in the other three tasks; and the OWAS risk score for welding was significantly higher than for rebar tying (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.008). Details were shown in Table 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 \u0026nbsp;Comparison of OWAS risk levels among different tasks\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage Rank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRebar Tying\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWood Processing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWelding\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrinding\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrunk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e123.08\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e124.80\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e106.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e82.59\u003csup\u003ea, b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLifting heavy objects with hands\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e107.50\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e118.50\u003csup\u003ea, c, d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e107.50\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e107.50\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOWAS Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e87.67\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e112.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e121.33\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e114.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: Within the same row, pairwise comparisons were performed using \u003cem\u003eDunn\u0026rsquo; s test\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003ea.Compared with the mean rank of rebar tying, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.008;\u003c/p\u003e\n\u003cp\u003eb.Compared with the mean rank of wood processing, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.008；\u003c/p\u003e\n\u003cp\u003ec.Compared with the mean rank of welding, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.008；\u003c/p\u003e\n\u003cp\u003ed.Compared with the mean rank of grinding, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.008.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.3 Comparison of assessment from different methods for the same critical working postures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.3.1 RULA VS REBA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe agreement between RULA and REBA in risk assessments for all working postures was 46.8% (\u0026kappa; = 0.318, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Task-specific agreement rates were as follows: 28.3% (\u0026kappa; = 0.099, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) for rebar tying, 48.3% (\u0026kappa; = 0.373, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) for wood processing, 57.9% (\u0026kappa; = 0.324, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) for welding, and 56.0% (\u0026kappa; = 0.374, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) for grinding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor all working postures assessed, a statistically significant difference was observed between RULA and REBA assessment results, with RULA yielding systematically higher scores than REBA (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.017).\u003c/p\u003e\n\u003cp\u003eThe detailed results were summarized in Tables S7-S9.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.3.2 RULA VS OWAS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe inter-method agreement between RULA and OWAS for risk assessment of working postures was 14.5% (\u0026kappa; = 0.044, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) across all construction tasks. Task-specific analysis revealed the following agreement levels: 6.7%\u0026nbsp;(\u0026kappa; = 0.009, \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05)\u0026nbsp;for rebar tying, 20.0%\u0026nbsp;(\u0026kappa; = 0.320, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05)\u0026nbsp;for wood processing, 32.0%\u0026nbsp;(\u0026kappa; = 0.064, \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05)\u0026nbsp;for welding, and no significant agreement was observed for grinding\u0026nbsp;(\u0026kappa; = 0.014, \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA statistically significant difference was observed between RULA and OWAS assessment results across all working postures, with RULA consistently yielding higher scores than OWAS (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.017).\u003c/p\u003e\n\u003cp\u003eThe detailed results were summarized in Tables S7-S9.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.3.3 REBA VS OWAS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe inter-method agreement between REBA and OWAS for risk assessment across all working postures was 28.2% (\u0026kappa; = 0.229, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Task-specific agreement rates were as follows:\u0026nbsp;25.0% (\u0026kappa; = 0.245, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) for rebar tying, 48.3% (\u0026kappa; = 0.297, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) for wood processing, 57.9% (\u0026kappa; = 0.298, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) for welding, and 56.0% (\u0026kappa; = 0.090, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) for grinding.\u003c/p\u003e\n\u003cp\u003eREBA demonstrated systematically higher assessment scores compared to OWAS across all working postures, with the difference being statistically significant (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.017).\u003c/p\u003e\n\u003cp\u003eThe detailed results were summarized in Tables S7-S9.\u0026nbsp;\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Prevalence of WMSDs\u003c/h2\u003e \u003cp\u003eA large-scale survey on WMSDs among construction workers in China was conducted in this study. The results indicated an overall WMSD prevalence rate of 27.4%, with the most commonly affected regions being the lower back/lumbar, shoulders, and neck. While the high-prevalence body sites align with findings from several domestic studies [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], the overall prevalence rate observed in this study is relatively lower. This discrepancy may be attributed to differences in WMSDs diagnostic criteria, sample size, geographical distribution, and population composition. Construction workers demonstrated a lower prevalence of WMSDs relative to other occupational groups, such as administrative personnel, nurses, and drivers [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The construction industry is characterized by high-intensity physical demands, significant occupational exposure risks, and a highly mobile workforce, which collectively contribute to a pronounced healthy worker effect. The substantial physical requirements of construction work selectively retain individuals with greater physical capacity, while those with poorer health are more likely to leave or be excluded from the workforce. This selection mechanism results in a lower observed prevalence of WMSDs among construction workers compared to occupational groups in manufacturing, healthcare, and office-based settings [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. This study also identified riveters, welders, carpenters, and rebar workers as subgroups with a higher prevalence of WMSDs than the broader construction workforce. This elevated risk is linked to occupational exposures characterized by sustained static/awkward postures (e.g., stooping, forward head posture, overhead work) and repetitive manual tasks (including twisting, flexing, and lateral deviations of the hands) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], with contributing environmental aggravators like dust and noise further potentiating the burden in the neck, shoulder, and lower back [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Influencing factors of WMSDs\u003c/h2\u003e \u003cdiv id=\"Sec35\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Questionnaire Method\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe development of work-related musculoskeletal disorders (WMSDs) involves complex interactions between individual characteristics, work-related biomechanical factors, and work organization:\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e(1) Individual Characteristics\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAge: Neck/shoulder WMSD prevalence decreases with age, possibly due to senior workers acquiring labor-saving postures/techniques or being reassigned to less physically demanding roles [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], consistent with Le et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMarital status: Married workers have lower WMSD risk (lower back, shoulder, neck) than unmarried peers, attributed to socioeconomic/emotional stability, spousal support (reducing non-work physical load), and reduced psychological stress/loneliness [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAlcohol consumption: A risk factor for lower back/shoulder WMSDs [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], as long-term drinking impairs muscle metabolism (fiber breakdown, atrophy) and disrupts neuromotor control/vascular function, increasing fatigue and injury risk [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLeisure-time physical activity \u0026amp; fatigue: Moderate activity is protective, but excessive activity provides no additional benefit (compounding occupational physical load) [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]; self-perceived fatigue increases WMSD risk by reducing strength, endurance, and coordination [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e(2) Work-Related Biomechanical Factors (Work Types \u0026amp; Postures)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eProlonged sitting: A dose-dependent risk factor for lower back/shoulder-neck WMSDs [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]; construction-related sitting (combined with dynamic movements, vibration, awkward postures) exacerbates risks. Squatting/kneeling (\u0026ge;\u0026thinsp;1 hour/day) increases lower back strain via elevated L4/L5 vertebral load, altered pelvic tilt, and reduced lumbar lordosis [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUncomfortable postures: Long-duration exposure impairs local blood flow/ nutrient uptake in musculoskeletal tissues, with risk increasing with exposure time [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSpecific postures/tasks: Sustained torso flexion, repetitive lower back movements, and overhead work (hands above shoulder level) elevate WMSD risk [\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]; overhead work induces sustained shoulder muscle contraction and joint stress, promoting fatigue and cartilage degeneration [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e(3) Work Organization\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLong working hours: Weekly working time\u0026thinsp;\u0026gt;\u0026thinsp;64 hours increases WMSD risk via cumulative ergonomic exposure and heightened psychological stress [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTask monotony \u0026amp; staffing: Low job diversity (monotonous tasks) and understaffing lead to boredom, fatigue, and overtime, reducing recovery time [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]; shift work disrupts circadian rhythms, impairing sleep and causing chronic fatigue [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRest periods: Adequate rest mitigates musculoskeletal fatigue and WMSD symptom intensity/clustering without reducing productivity [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHigh-temperature exposure: Prevalent in construction, thermal stress causes discomfort, fatigue, and impaired cognitive/motor function, increasing injury risk [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Observation-based Method\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe assessment of risk is susceptible to subjectivity when based purely on self-reported questionnaires [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. To address this limitation, our methodology extended beyond questionnaires by incorporating structured observational techniques. These techniques were used to capture specific postural characteristics during tasks and to apply standardized observational scales for a more objective analysis of posture-related risks among construction workers.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec37\" class=\"Section4\"\u003e \u003ch2\u003e4.2.2.1 Risk of Rebar Tying Operation\u003c/h2\u003e \u003cp\u003eRULA (Level 4), REBA (Level 3), and OWAS (Level 3) have all identified high WMSDs risks in rebar tying, highlighting an urgent need for immediate improvements. First, RULA and REBA both detected significant risks associated with wrist movements: during rebar tying, workers use binding guns to secure steel wires, which involves more frequent and extensive repetitive wrist motions (including pronounced rotation, flexion-extension, and deviation) compared to welding or grinding\u0026mdash;tasks characterized by moderate-to-low frequency wrist actions with smaller amplitudes and limited motion range. Chen et al. [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] noted that such repetitive wrist movements contribute more significantly to wrist WMSDs than sustained flexed postures. Additionally, RULA and REBA confirmed high lower limb WMSD risks for rebar workers, as the task often requires prolonged squatting or kneeling on uneven steel bar grids, resulting in inadequate lower-limb support and unstable body balance. Beyond postural hazards, RULA and REBA also emphasized risks from task repetitiveness: rebar tying involves repetitive wrist movements combined with frequent torso bending, which are particularly acute in pits or confined spaces where workers must maintain sustained neck/torso forward flexion alongside squatting or kneeling. Regarding force demands, RULA further indicated a high-risk level, as securing rebars requires workers to repeatedly perform a \"grip-hook, rotate, pull-tighten\" cycle, with considerable manual force needed especially during the \"pull-tighten\" phase.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec38\" class=\"Section4\"\u003e \u003ch2\u003e4.2.2.2 Risk of Wood Processing Operation\u003c/h2\u003e \u003cp\u003eAlthough the three methods assessed the overall WMSD risk in woodworking as relatively low, the inherent hazards of this trade should not be overlooked. RULA detected a high risk of forearm injury in wood processing (including cutting and sanding with saws), attributed to on-site constraints: varying wood sizes/shapes, limited machine layout flexibility, and fixed workstation heights frequently force workers to hold forearms in prolonged excessive forward flexion, away from neutral postures, or operate arms across the midline (midsagittal plane) with excessive forward reach. Such deviations significantly increase mechanical stress on muscles, tendons, and ligaments around the arm joints, shoulder-neck complex, and upper back, compressing nerves and irritating tendons\u0026mdash;predisposing workers to neck-shoulder syndrome, rotator cuff injuries, subacromial impingement syndrome, and biceps tendinitis [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. For lower limbs, OWAS classified wood processing as high-risk, primarily due to its far higher walking frequency compared to other trades. Notably, OWAS also identified elevated hand load risk among carpenters: they often use hands/arms to briefly stabilize wood and prevent slipping during processing, while vibrations from cutting and the wood\u0026rsquo;s own weight further contribute to increased hand injury risk.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section4\"\u003e \u003ch2\u003e4.2.2.3 Risk of Welding and Grinding Operations\u003c/h2\u003e \u003cp\u003eThe three assessment methods consistently rated the WMSD risk for welding and grinding operations at Level 3, indicating a clear need for ergonomic improvements. REBA and RULA showed that the two operations pose comparable WMSD risks: while their wrist movements are somewhat similar to those in rebar tying, they are mainly characterized by low-to-moderate frequency and small amplitudes, with a smaller overall range of motion and lower repetitiveness than rebar tying. To ensure optimal visibility during welding and grinding, workers often adopt prolonged postures such as flexed knees, bent backs, or unsupported arms, which compromise body mechanics. Additionally, process-specific techniques\u0026mdash;including linear/circular grinding and repetitive weld beads\u0026mdash;require highly repetitive arm and wrist motions, predisposing workers to musculoskeletal strain [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. A further key concern is that welders frequently operate in confined spaces, forcing them to maintain prolonged kneeling or squatting postures, which are established risk factors for lower limb WMSDs [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast to the other methods, RULA consistently assessed postural load as having a higher risk level in this study, indicating its greater sensitivity in identifying posture-related risks [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], which aligns with existing study results [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Given the low inter-method agreement observed for the same tasks, which reflects divergent assessment approaches, RULA is suggested for rapid screening in resource-limited settings. However, to compensate for the inherent risk of underestimation in the complex, multi-joint dynamics of construction, a holistic assessment strategy is imperative. This involves tailoring the evaluation to task-specific movements and utilizing a combination of methods or an integrated scale to leverage their complementary nature for a full picture of postural risk.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eConstruction workers face high risk of WMSDs, with the lower back, shoulders, and neck as the most affected regions\u0026mdash;riveters, welders, carpenters, and steel fixers are among the highest-prevalence trades. Multiple factors (individual attributes, job tasks, work organization) influence WMSD development, with awkward postures as the predominant risk factor.\u003c/p\u003e \u003cp\u003eAmong RULA, REBA, and OWAS, RULA is most sensitive to postural risk and preferable for rapid on-site screening, but overall agreement between the tools is low (each prioritizes different joint movements/kinematic chains). Due to the multi-joint complexity of construction tasks, single tools may underestimate risk; comprehensive evaluation with multiple tools (synthesizing local/overall risks) is essential when feasible.\u003c/p\u003e \u003cp\u003eLimitations: 1) Cross-sectional design cannot establish causality between risk factors and WMSDs; 2) Focus on only four trades limits generalizability (future research should include more professions); 3) No direct measurement of muscular exertion/force may introduce bias. Future studies should adopt laboratory-based kinematic quantification, combined with real-world ergonomic data, to fully elucidate WMSD risk factors.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eWMSDs: Work-related Musculoskeletal Disorders\u003c/p\u003e\n\u003cp\u003eLBP: Low back pain\u003c/p\u003e\n\u003cp\u003eMSDs: Musculoskeletal Disorders\u003c/p\u003e\n\u003cp\u003eDALYs: Disability Adjusted Life Years\u003c/p\u003e\n\u003cp\u003eGDP: Gross Domestic Product\u003c/p\u003e\n\u003cp\u003eRULA: Rapid Upper Limb Assessment\u003c/p\u003e\n\u003cp\u003eREBA: Rapid Entire Body Assessment\u003c/p\u003e\n\u003cp\u003eOWAS: Ovako Working Analysis System\u003c/p\u003e\n\u003cp\u003eBMI\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;: Body Mass Index\u003c/p\u003e\n\u003cp\u003eNIOSH: National Institute for Occupational Safety and Health\u003c/p\u003e\n\u003cp\u003eOR: Odds Ratios\u003c/p\u003e\n\u003cp\u003eCI: Confidence Interval\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study adhered to the ethical guidelines outlined in the Declaration of Helsinki and received approval from the Guangdong Province Hospital for Occupational Disease Prevention and Treatment Ethics Committee (No: GDHOD MEC 2024027). All study participants provided written informed consent before being included in the research. The participants shown in Figure 1 and 2 were consent for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets underlying the results of this study can be obtained from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from Guangdong Medical Research Foundation(A2023061).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eGuangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou, Guangdong, 510300, China\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eXiongda He, Chunshuo Chen, Bin Xiao, Zhipeng He, Guoyong Xu, Hua Yan, Yongjian Jiang, Junle Wu, Jianyu Guo, Maosheng Yan\u0026nbsp;\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003e\u003cstrong\u003eDepartment of Preventive Health Care, Dongguan Huangjiang Hosipital, Dongguan, Guangdong, 523750, China\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eXiongda He\u0026nbsp;\u003c/p\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003e\u003cstrong\u003eSchool of Public Health, Southern Medical University, Guangzhou, Guangdong, 510515, China\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eXiongda He, Yuan Qiu, Maosheng Yan\u0026nbsp;\u003c/p\u003e\n\u003col start=\"4\"\u003e\n \u003cli\u003e\u003cstrong\u003eSchool of Public Health, Guangzhou Medical University, Guangzhou, Guangdong, 511436, China\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eChunshuo Chen, Maosheng Yan \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"5\"\u003e\n \u003cli\u003e\u003cstrong\u003eSchool of Public Health, Shanxi Medical University, Taiyuan, Shanxi, 030002, China\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eYanhui Lan, Xinyi Zou, Maosheng Yan\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMaosheng Yan. Email Address: [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiongda He \u0026amp; Chunshuo Chen: data collection, data curation, methodology, data analysis, original draft preparation.\u003c/p\u003e\n\u003cp\u003eBin Xiao \u0026amp; Guoyong Xu: liaising and communicating with enterprises, supervision, writing-review and editing.\u003c/p\u003e\n\u003cp\u003eZhipeng He, Hua Yan, Yongjian Jiang, Junle Wu, Jianyu Guo: data collection, data curation.\u003c/p\u003e\n\u003cp\u003eYanhui Lan, Xinyi Zou, Yuan Qiu: data analysis.\u003c/p\u003e\n\u003cp\u003eMaosheng Yan: funding acquisition, project administration, liaising and communicating with enterprises, supervision, conceptualization, writing-review and editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all researchers and every member of the staff involved in the survey for their contribution to the data collection.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col class=\"decimal_type\"\u003e\n \u003cli\u003eLuttmann, A. \u0026amp; J\u0026auml;ger, Matthias \u0026amp; Griefahn, B. \u0026amp; Caffier, G. \u0026amp; Liebers, Falk \u0026amp; Steinberg, U.. (2003). Protecting Workers\u0026apos; Health Series No. 5: Preventing musculoskeletal disorders in the workplace.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBriggs AM, Shiffman J, Shawar YR, \u0026Aring;kesson K, Ali N, Woolf AD. Global health policy in the 21st century: Challenges and opportunities to arrest the global disability burden from musculoskeletal health conditions. Best Pract Res Clin Rheumatol. 2020 Oct;34(5):101549.\u003c/li\u003e\n \u003cli\u003eGBD 2021 Other Musculoskeletal Disorders Collaborators. Global, regional, and national burden of other musculoskeletal disorders, 1990-2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021. Lancet Rheumatol. 2023 Oct 23;5(11):e670-e682.\u003c/li\u003e\n \u003cli\u003eGuan SY, Zheng JX, Sam NB, Xu S, Shuai Z, Pan F. Global burden and risk factors of musculoskeletal disorders among adolescents and young adults in 204 countries and territories, 1990-2019. Autoimmun Rev. 2023 Aug;22(8):103361.\u003c/li\u003e\n \u003cli\u003eDu Q, Zhou J, Pan T, Sun Q, Wu M. Relationship of carbon emissions and economic growth in China\u0026apos;s construction industry. J Clean Prod. 2019;220: 99\u0026ndash;109.\u003c/li\u003e\n \u003cli\u003eUnderstanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry. Computers in Industry. 2016 Dec;83: 121\u0026ndash;139.\u003c/li\u003e\n \u003cli\u003eZhao F, Wang YW, Jin L, et al. Statistics Analysis of China\u0026apos;s Construction Industry Development in 2023. Journal of Engineering Management. 2024,38(02):1-6.\u003c/li\u003e\n \u003cli\u003evan der Molen HF, Basnet P, Hoonakker PL, Lehtola MM, Lappalainen J, Frings-Dresen MH, Haslam R, Verbeek JH. Interventions to prevent injuries in construction workers. Cochrane Database Syst Rev. 2018 Feb 5;2(2):CD006251.\u003c/li\u003e\n \u003cli\u003eForteza FJ, Carretero-G\u0026oacute;mez JM, Ses\u0026eacute; A. Organizational factors and specific risks on construction sites. J Safety Res. 2022 Jun;81:270-282.\u003c/li\u003e\n \u003cli\u003eEstudillo B, Forteza FJ, Carretero-G\u0026oacute;mez JM, Rej\u0026oacute;n-Guardia F. The role of organizational factors in promoting workers\u0026apos; health in the construction sector: A comprehensive analysis. J Safety Res. 2024 Feb;88:41-55.\u003c/li\u003e\n \u003cli\u003eAntwi-Afari MF, Li H, Chan AHS, Seo J, Anwer S, Mi HY, Wu Z, Wong AYL. A science mapping-based review of work-related musculoskeletal disorders among construction workers. J Safety Res. 2023 Jun;85:114-128.\u003c/li\u003e\n \u003cli\u003eUmer W, Antwi-Afari MF, Li H, Szeto GPY, Wong AYL. The prevalence of musculoskeletal symptoms in the construction industry: a systematic review and meta-analysis. Int Arch Occup Environ Health. 2018 Feb;91(2):125-144.\u003c/li\u003e\n \u003cli\u003eWang X, Dong XS, Choi SD, Dement J. Work-related musculoskeletal disorders among construction workers in the United States from 1992 to 2014. Occup Environ Med. 2017 May;74(5):374-380.\u003c/li\u003e\n \u003cli\u003ePark J, Kim Y, Han B. Work Sectors with High Risk for Work-Related Musculoskeletal Disorders in Korean Men and Women. Saf Health Work. 2018 Mar;9(1):75-78.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDerakhshan Jazari M, Jahangiri M, Khaleghi H, Abbasi N, Hassanipour S, Shakerian M, Kamalinia M. Prevalence of self-reported work-related illness and injuries among building construction workers, Shiraz, Iran. EXCLI J. 2018 Jul 25;17:724-733.\u003c/li\u003e\n \u003cli\u003eda Costa BR, Vieira ER. Risk factors for work-related musculoskeletal disorders: A systematic review of recent longitudinal studies. Am J Ind Med. 2010 Mar;53(3):285-323.\u003c/li\u003e\n \u003cli\u003eEvanoff B, Dale AM, Descatha A. A conceptual model of musculoskeletal disorders for occupational health practitioners. Int J Occup Med Environ Health. 2014 Jan;27(1):145-8.\u003c/li\u003e\n \u003cli\u003eStock SR, Nicolakakis N, V\u0026eacute;zina N, V\u0026eacute;zina M, Gilbert L, Turcot A, Sultan-Ta\u0026iuml;eb H, Sinden K, Denis MA, Delga C, Beaucage C. Are work organization interventions effective in preventing or reducing work-related musculoskeletal disorders? A systematic review of the literature. Scand J Work Environ Health. 2018 Mar 1;44(2):113-133.\u003c/li\u003e\n \u003cli\u003evan Rijn RM, Huisstede BM, Koes BW, Burdorf A. Associations between work-related factors and specific disorders of the shoulder--a systematic review of the literature. Scand J Work Environ Health. 2010 May;36(3):189-201.\u003c/li\u003e\n \u003cli\u003eInternational Labour Office and International Ergonomics Association. Ergonomic checkpoints: Practical and easy-to-implement solutions for improving safety, health and working conditions. Second edition. International Labour Office, Geneva, 2010.\u003c/li\u003e\n \u003cli\u003eNIOSH \u0026nbsp;[2014]. Observation-based posture assessment: review of current practice and recommendations for improvement. By Lowe BD, Weir PL, Andrews DM. Cincinnati, OH: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, DHHS (NIOSH) Publication No. 2014\u0026ndash;131.\u003c/li\u003e\n \u003cli\u003eYang L, Hildebrandt VH, Yu SF, Ling RJ, He LH, Chen WH, Xia ZL, Wang JX, Li LP, Wang S. Musculoskeletal Disorders Questionnaire Introduction Attached questionnaire. Ind Health Occup Dis. 2009;35:25\u0026ndash;31.Salvendy G. Handbook of human factors and ergonomics. 4. Hoboken: John Wiley \u0026amp; Sons, Inc.; 2012.\u003c/li\u003e\n \u003cli\u003eWang Y, Zhao L, Gao L, Pan A, Xue H. Health policy and public health implications of obesity in China. Lancet Diabetes Endocrinol. 2021 July;9(7):446-461.\u003c/li\u003e\n \u003cli\u003eKee D. Systematic Comparison of OWAS, RULA, and REBA Based on a Literature Review. Int J Environ Res Public Health. 2022 Jan 5;19(1):595.\u003c/li\u003e\n \u003cli\u003eMcAtamney L, Nigel CE. RULA: a survey method for the investigation of work-related upper limb disorders. Appl Ergon, 1993, 24(2): 91-99.\u003c/li\u003e\n \u003cli\u003eHignett S, McAtamney L. Rapid entire body assessment (REBA). Appl Ergon, 2000, 31(2): 201-205.\u003c/li\u003e\n \u003cli\u003eGomez-Galan M, Perez-Alonso J, Callejon-Ferre AJ, et al. Musculoskeletal disorders: OWAS review. Ind Health, 2017, 55(4): 314-337.\u003c/li\u003e\n \u003cli\u003eKee D. Comparison of OWAS, RULA and REBA for assessing potential work-related musculoskeletal disorders. Int J Ind Ergon, 2021, 83: 103140.\u003c/li\u003e\n \u003cli\u003eBalsalobre-Fernandez C, Tejero-Gonzalez CM, Del CJ, et al. The concurrent validity and reliability of a low-cost, high-speed camera-based method for measuring the flight time of vertical jumps. J Strength Cond Res, 2014, 28(2): 528-533.\u003c/li\u003e\n \u003cli\u003eElwardany SH, El-Sayed WH, Ali MF. Reliability of Kinovea Computer Program in Measuring Cervical Range of Motion in Sagittal Plane. OALib, 2015, 02(09): 1-10.\u003c/li\u003e\n \u003cli\u003eErgonomics\u0026mdash;Evaluation of Static Working Postures. International Standard Organization (ISO); Geneva, Switzerland: 2000.\u003c/li\u003e\n \u003cli\u003eBasic human body measurements for technological design\u0026mdash;Part 1: Body measurement definitions and landmarks. International Standard Organization (ISO); Geneva, Switzerland: 2017.\u003c/li\u003e\n \u003cli\u003evan T Hullenaar CDP, Bos P, Broeders IAMJ. Ergonomic assessment of the first assistant during robot-assisted surgery. J Robot Surg, 2019, 13(2): 283-288.\u003c/li\u003e\n \u003cli\u003eRigby AS. Statistical methods in epidemiology. v. Towards an understanding of the kappa coefficient. Disabil Rehabil, 2000, 22(8): 339-344.\u003c/li\u003e\n \u003cli\u003eWang X, Dong XS, Choi SD, Dement J. Work-related musculoskeletal disorders among construction workers in the United States from 1992 to 2014. Occup Environ Med. 2017 May;74(5):374-380.\u003c/li\u003e\n \u003cli\u003eKashif M, Albalwi A, Raqib A, Farooq M, Ullah R, Sakoor M, Kamran Z. Work-related musculoskeletal disorders among Pakistani construction workers: Prevalence, characteristics, and associated risk factors. Work. 2022;72(1):119-126.\u003c/li\u003e\n \u003cli\u003eLee YC, Hong X, Man SS. Prevalence and Associated Factors of Work-Related Musculoskeletal Disorders Symptoms among Construction Workers: A Cross-Sectional Study in South China. Int J Environ Res Public Health. 2023 Mar 6;20(5):4653.\u003c/li\u003e\n \u003cli\u003eSu Y, Jin KZH, Pinnaduwage Vijitha DESILVA, Truls \u0026Oslash;STBYE. [Comparisons of occupational ergonomic exposure patterns and health status of Chinese expatriate and local construction workers in Sri Lanka]. Journal of Environmental and Occupational Medicine. 2021,38(02):105-112.\u003c/li\u003e\n \u003cli\u003eEsmaeili SV, Alboghobeish A, Izadi N, Azizi F, Dorfeshan F, Salehi Sahlabadi A. Predictive power of the HEMPA risk assessment method for musculoskeletal disorders in nurses and caregivers: insights and implications. BMC Nurs. 2025 Jul 1;24(1):811.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eChen C, Xiao B, He X, Wu J, Li W, Yan M. Prevalence of low back pain in professional drivers: a meta-analysis. Public Health. 2024 Jun;231:23-30.\u003c/li\u003e\n \u003cli\u003eUmer W, Antwi-Afari MF, Li H, et al. The prevalence of musculoskeletal symptoms in the construction industry: a systematic review and meta-analysis. Int Arch Occup Environ Health, 2018, 91(2): 125-144.\u003c/li\u003e\n \u003cli\u003eGherscovici ED, Mayer JM. Relationship of Healthy Building Determinants With Musculoskeletal Disorders of the Extremities: A Systematic Review. Cureus. 2023 Apr 11;15(4):e37456.\u003c/li\u003e\n \u003cli\u003eLe TTT, Jalayondeja W, Mekhora K, et al. Prevalence and risk factors of work-related musculoskeletal disorders among physical therapists in Ho Chi Minh City, Vietnam. BMC Public Health, 2024, 24(1): 6.\u003c/li\u003e\n \u003cli\u003eJia N, Zhang MB, Xu Q,\u0026nbsp;et al. [Study on the epidemiological characteristics of work-related musculoskeletal disorders among 29 industries or occupational groups in China]. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi. 2024 Dec 20;42(12):884-895.\u003c/li\u003e\n \u003cli\u003eCano A. Pain catastrophizing and social support in married individuals with chronic pain: the moderating role of pain duration. Pain, 2004, 110(3): 656-664.\u003c/li\u003e\n \u003cli\u003eChen B, Li L, Donovan C, et al. Prevalence and characteristics of chronic body pain in China: a national study. Springerplus, 2016, 5(1): 938.\u003c/li\u003e\n \u003cli\u003eAlleyne J, Dopico AM. Alcohol Use Disorders and Their Harmful Effects on the Contractility of Skeletal, Cardiac and Smooth Muscles. Adv Drug Alcohol Res, 2021, 1.\u003c/li\u003e\n \u003cli\u003eKirsch MJ, Blafoss R, Sundstrup E, et al. Association between lifestyle and musculoskeletal pain: cross-sectional study among 10,000 adults from the general working population. BMC Musculoskelet Disord, 2019, 20(1): 609.\u003c/li\u003e\n \u003cli\u003eSmith BE, Hendrick P, Bateman M, et al. Musculoskeletal pain and exercise-challenging existing paradigms and introducing new. Br J Sports Med, 2019, 53(14): 907-912.\u003c/li\u003e\n \u003cli\u003eMoreira RF, Foltran FA, Albuquerque-Sendin F, et al. Comparison of randomized and non-randomized controlled trials evidence regarding the effectiveness of workplace exercise on musculoskeletal pain control. Work, 2012, 41 Suppl 1: 4782-4789.\u003c/li\u003e\n \u003cli\u003eArmon G, Melamed S, Shirom A, et al. Elevated burnout predicts the onset of musculoskeletal pain among apparently healthy employees. J Occup Health Psychol, 2010, 15(4): 399-408.\u003c/li\u003e\n \u003cli\u003eDzakpasu F, Carver A, Brakenridge CJ, et al. Musculoskeletal pain and sedentary behaviour in occupational and non-occupational settings: a systematic review with meta-analysis. Int J Behav Nutr Phys Act, 2021, 18(1): 159.\u003c/li\u003e\n \u003cli\u003eChen N, Zhang H, Zhao J, Wang S. [Biomechanics Analysis and Improvement of Working Posture of Steel Fixer]. Industrial Safety and Environmental Protection, 2017, 43(12): 37-40+54.\u003c/li\u003e\n \u003cli\u003ePope MH, Goh KL, Magnusson ML. Spine ergonomics. Annu Rev Biomed Eng, 2002, 4: 49-68.\u003c/li\u003e\n \u003cli\u003eJia N, Zhang M, Zhang H, et al. Prevalence and risk factors analysis for low back pain among occupational groups in key industries of China. BMC Public Health, 2022, 22(1): 1493.\u003c/li\u003e\n \u003cli\u003eAntwi-Afari MF, Li H, Edwards DJ, et al. Biomechanical analysis of risk factors for work-related musculoskeletal disorders during repetitive lifting task in construction workers. Autom Constr, 2017, 83: 41-47.\u003c/li\u003e\n \u003cli\u003eWang A, Zhou Y, Li X, et al. Investigating and analyzing the current situation and factors influencing chronic neck, shoulder, and lumbar back pain among medical personnel after the epidemic. BMC Musculoskelet Disord, 2024, 25(1): 316.\u003c/li\u003e\n \u003cli\u003eMovahed M, Ohashi JY, Kurustien N, et al. Fatigue sensation, electromyographical and hemodynamic changes of low back muscles during repeated static contraction. Eur J Appl Physiol, 2011, 111(3): 459-467.\u003c/li\u003e\n \u003cli\u003eWaersted M, Koch M, Veiersted KB. Work above shoulder level and shoulder complaints: a systematic review. Int Arch Occup Environ Health, 2020, 93(8): 925-954.\u003c/li\u003e\n \u003cli\u003eErvasti J, Pentti J, Nyberg ST, et al. Long working hours and risk of 50 health conditions and mortality outcomes: a multicohort study in four European countries. Lancet Reg Health Eur, 2021, 11: 100212.\u003c/li\u003e\n \u003cli\u003eBao SS, Kapellusch JM, Merryweather AS, et al. Relationships between job organisational factors, biomechanical and psychosocial exposures. Ergonomics, 2016, 59(2): 179-194.\u003c/li\u003e\n \u003cli\u003eMatre D, Christensen JO, Mork PJ, Ferreira P, Sand T, Nilsen KB. Shift work, inflammation and musculoskeletal pain-The HUNT Study. Occup Med (Lond). 2021 Dec 24;71(9):422-427.\u003c/li\u003e\n \u003cli\u003eStock SR, Nicolakakis N, V\u0026Atilde; Zina N, et al. Are work organization interventions effective in preventing or reducing work-related musculoskeletal disorders? A systematic review of the literature. Scandinavian Journal of Work, Environment \u0026amp; Health, 2017.\u003c/li\u003e\n \u003cli\u003eGariazzo C, Taiano L, Bonafede M, et al. Association between extreme temperature exposure and occupational injuries among construction workers in Italy: An analysis of risk factors. Environ Int, 2023, 171: 107677.\u003c/li\u003e\n \u003cli\u003eGibb K, Beckman S, Vergara XP, et al. Extreme Heat and Occupational Health Risks. Annu Rev Public Health, 2024, 45(1): 315-335.\u003c/li\u003e\n \u003cli\u003eAyvaz O, Ozyildirim BA, Issever H, et al. Ergonomic risk assessment of working postures of nurses working in a medical faculty hospital with REBA and RULA methods. Sci Prog, 2023, 106(4): 332187036.\u003c/li\u003e\n \u003cli\u003eChen N, Li G, Sun X, et al. Prevalence status and associated factors of wrist postural injury in the Chinese occupational population. Front Public Health, 2022, 10: 1047814.\u003c/li\u003e\n \u003cli\u003eNamwongsa S, Puntumetakul R, Neubert MS, et al. Ergonomic risk assessment of smartphone users using the Rapid Upper Limb Assessment (RULA) tool. PLoS One, 2018, 13(8): e203394.\u003c/li\u003e\n \u003cli\u003eBarr AE, Barbe MF, Clark BD. Work-related musculoskeletal disorders of the hand and wrist: epidemiology, pathophysiology, and sensorimotor changes. J Orthop Sports Phys Ther. 2004 Oct;34(10):610-27.\u003c/li\u003e\n \u003cli\u003eZhang X, Jia N, Sun X, et al. [Structural equation analysis and modeling of fect and ankles WMSDs and its adverse ergonomic factors]. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi. 2025 Feb 20;43(2):101-109.\u003c/li\u003e\n \u003cli\u003eMicheletti Cremasco M, Giustetto A, Caffaro F, et al. Risk Assessment for Musculoskeletal Disorders in Forestry: A Comparison between RULA and REBA in the Manual Feeding of a Wood-Chipper. International journal of environmental research and public health, 2019, 16(5): 793.\u003c/li\u003e\n \u003cli\u003eDomingo JRT, Pano MTSD, Ecat DAG, et al. Risk Assessment on Filipino Construction Workers. Procedia Manufacturing, 2015, 3: 1854-186.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Construction worker, WMSDs, RULA (Rapid Upper Limb Assessment), REBA (Rapid Entire Body Assessment), OWAS (Ovako Working Posture Analysis System)","lastPublishedDoi":"10.21203/rs.3.rs-9149878/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9149878/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWork-related Musculoskeletal Disorders (WMSDs) are a major occupational health issue in construction industry. However, there are few analyses about WMSDs in this field. This study aims to evaluate the prevalence of WMSDs among constructionworkers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study first conducted a cross-sectional survey of 10,781 construction workers using a musculoskeletal questionnaire to determine WMSDs prevalence and identify associated factors via multivariate logistic regression. In the second phase, we selected 220 key workers, recorded their work activities, and used Kinovea software for postural analysis. We applied RULA (Rapid Upper Limb Assessment), REBA (Rapid Entire Body Assessment), and OWAS (Ovako Working Posture Analysis System) to assess WMSDs risks and performed multiple comparisons to evaluate the consistency and applicability of these tools in construction settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConstruction workers reported a 27.4% prevalence of WMSDs, primarily affecting the shoulders, lower back/lumbar region, and neck. Riveters, welders, carpenters, and rebar workers showed relatively high overall prevalence rates of WMSDs. Individual factors, job type, work organization, and work postures influenced WMSDs.\u003c/p\u003e\n\u003cp\u003eIn observation-based method, three methods identified the WMSDs risk in rebar tying, welding and grinding operations, as predominantly above level 3, while the overall risk of WMSDs in woodworking was assessed to be relatively low. Rebar workers and welders shared a high risk from repetitive/prolonged static wrist postures. Carpenters were uniquely exposed to risks from forceful gripping and load handling, combined with demanding arm and lower limb postures. Riveters integrated all these challenges, facing a composite of wrist postures, repetitive/static operations, gripping, and loading.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConstruction workers face a substantial risk of developing WMSDs. It is recommended that enterprises implement comprehensive measures tailored to individual and occupational profiles. A systematic screening for risk factors should be established to facilitate the development of more effective holistic strategy for the prevention and control of WMSDs.\u003c/p\u003e","manuscriptTitle":"Investigation of Work-related Musculoskeletal Disorders among Chinese Construction Workers: An Integrated Application of Questionnaire and Observational Methods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 17:48:39","doi":"10.21203/rs.3.rs-9149878/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-06T18:10:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202318440181656633493501280755140191514","date":"2026-05-05T15:35:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T12:10:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298446962982015593646033455794144611732","date":"2026-04-30T16:59:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310901546035131596043276758629425000762","date":"2026-04-13T16:46:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-10T09:37:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-07T18:57:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-06T16:24:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-06T15:54:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-04-06T14:27:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"afc873b2-cfd8-48a1-8a18-c755a8071232","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-06T18:10:00+00:00","index":61,"fulltext":""},{"type":"reviewerAgreed","content":"202318440181656633493501280755140191514","date":"2026-05-05T15:35:40+00:00","index":60,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T12:10:47+00:00","index":58,"fulltext":""},{"type":"reviewerAgreed","content":"298446962982015593646033455794144611732","date":"2026-04-30T16:59:02+00:00","index":55,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T17:48:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 17:48:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9149878","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9149878","identity":"rs-9149878","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}