Ergonomic Risk and Musculoskeletal Disorders in Construction: Assessing Job-Related Determinants in the U.S. Workforce

Abstract

Musculoskeletal disorders (MSDs) remain one of the most persistent occupational health challenges in the U.S. construction industry, where physically demanding tasks such as heavy lifting, kneeling, and working in awkward postures contribute to elevated injury rates. This study aims to identify significant job-related determinants of MSDs in construction-sector occupations. By integrating publicly available datasets from the Survey of Occupational Injuries and Illnesses (SOII) and the Occupational Information Network (O*NET) datasets, a stepwise multiple regression analysis was conducted on 344 occupation-condition observations representing 86 construction occupations, yielding a final model that explained 49% of the variance. Ten significant predictors of MSD events were identified and classified as either risk amplifiers or mitigators. Amplifiers included factors such as exposure to noise, disease, hazardous conditions, and time pressure, all of which heightened MSD risk, while mitigators—such as reduced cramped-space exposure and regulated work environments—were associated with lower risk. MSDs resulting from sprains, strains, or tears accounted for 62.8% of all cases, frequently leading to days away from work (36.3%) or job restrictions (26.5%). The findings underscore that ergonomic risk in construction extends beyond physical strain to include scheduling, equipment design, and work organization. These results provide actionable insights for employers and safety professionals to redesign tools, optimize task rotation, and implement realistic work pacing strategies, ultimately reducing MSD incidence and improving productivity in this high-risk sector.

1. Introduction

Musculoskeletal disorders (MSDs) are one of the most pervasive and costly occupational health challenges in the U.S. construction industry [1,2,3,4]. MSDs are injuries or pain affecting muscles, joints and tendons [5,6,7]. These pains can decrease a worker’s flexibility, coordination, and balance, increasing their risk of falling. Construction workers who suffer from MSDs can have reduced work ability, and the symptoms are likely to recur if not treated properly [8,9,10]. Unlike many other sectors, construction work requires continuous engagement in high-force, high-exertion tasks such as lifting heavy materials, working in awkward positions, operating vibrating machinery, and navigating unpredictable environments, including elevated work surfaces, confined spaces, or partially completed structures [11,12,13,14]. These biomechanical demands place construction workers at disproportionately higher risk of MSDs affecting the lower back, shoulders, knees, and upper extremities [15,16,17,18,19,20]. National surveillance data consistently show that MSD rates in the construction industry exceed those of most other industries, contributing to substantial lost work time, long-term disability, and escalating workers’ compensation costs [21,22].

MSD-related workers’ compensation claims account for approximately 30% of all claims, with direct costs reaching $20 billion annually and total costs, including lost productivity, estimated at $54 billion [23,24]. In 2015 alone, 264 million lost workdays were attributed to back and neck pain, resulting in $131.8 billion in lost annual earnings [25]. In 2016, low back pain was identified as the leading cause of years of disability in the U.S., followed by other musculoskeletal disorders, neck pain, osteoarthritis, and rheumatoid arthritis [26]. In 2022, the National Safety Council reported that workplace MSDs resulted in direct costs exceeding $50 billion, with the average compensation cost per strain injury reaching $37,185 [27].

Common construction tasks require workers to adopt postures such as kneeling, squatting, and stooping, which contribute to the development of musculoskeletal disorders [28,29,30,31,32]. A substantial body of ergonomics research has documented specific physical risk factors in construction—such as repetitive overhead work, kneeling on hard surfaces, forceful exertion, and prolonged static postures—as key contributors to MSD development [12,33,34,35]. Additionally, task-based studies highlight that vibration exposure from power tools, heavy PPE burden, and environmental challenges, such as temperature extremes, further exacerbate musculoskeletal strain [10,36,37]. However, although these studies provide valuable insights into task-level exposures, construction work is highly fragmented and varied across trades, worksites, and job roles. As a result, current ergonomic recommendations are often generic, inconsistent across occupations, or difficult to apply uniformly in dynamic construction settings.

A critical gap remains in the construction ergonomics literature: limited quantitative evidence linking standardized, occupation-level descriptors to MSD outcomes across the full range of U.S. construction occupations. Most existing studies focus on individual trades (e.g., roofers, electricians, masons) or specific tasks (e.g., drywall installation, scaffold assembly), leaving little understanding of which job attributes across the entire construction-sector workforce consistently predict MSD risk. Furthermore, the interplay of environmental exposures (noise, disease, hazardous conditions), organizational pressures (time pressure, scheduling), and ergonomic constraints (cramped workspaces, PPE) have rarely been analyzed using integrated occupational datasets.

This limitation is consequential for both researchers and practitioners. Without a unified view of risk across occupations, it is difficult for employers, safety managers, and policy makers to prioritize interventions or allocate ergonomic resources effectively. The construction sector, characterized by tight production schedules, subcontracting layers, and varying levels of worker training, would benefit from a data-driven understanding of which occupational descriptors amplify or mitigate MSD risk.

To address this gap, the present study applies a quantitative modeling approach to U.S. construction-sector occupations using standardized datasets from the Survey of Occupational Injuries and Illnesses (SOII) and the Occupational Information Network (O*NET).

By analyzing 86 construction occupations and 344 detailed observations, this study aims to:

• Identify statistically significant job-related determinants of MSD events within the construction sector.
• Classify those determinants as ergonomic risk amplifiers or mitigators using occupation-level descriptors.
• Provide evidence-based insights that can inform targeted ergonomic interventions, task redesign, and safety management practices explicitly tailored to construction work environments.

Through these contributions, this study offers a comprehensive, occupation-centered understanding of MSD risk in construction—moving beyond task-based ergonomics to uncover broader patterns that can support safer, more sustainable construction workforce practices.

2. Materials and Methods

2.1. Study Approach

This study was designed to identify which occupational characteristics within the U.S. construction sector are most closely associated with the occurrence of MSD events. Rather than beginning with a broad occupational dataset and narrowing down, this study adopted a sector-focused approach by isolating all occupations classified under the Construction and Extraction major group (Standard Occupational Classification (SOC) code 47). The analytical framework centers on determining how construction-specific job attributes—captured through standardized occupational descriptors—relate to variation in MSD incidence across trades. This study followed the terms and definitions of injury facts provided by the National Safety Council [38].

2.2. Data Sources and Sector Extraction

Three publicly available national datasets were combined:

• Survey of Occupational Injuries and Illnesses (SOII): The U.S. Bureau of Labor Statistics (BLS) Survey of Occupational Injuries and Illnesses (SOII) supplied annual counts of MSD ergonomic-related reported injuries [21]. These data were filtered to include only North American Industry Classification System (NAICS) and SOC combinations corresponding to construction occupations.
• Occupational Information Network (O*NET): Job characteristics were obtained from the U.S. Department of Labor’ Occupational Information Network (O*NET), which provides standardized numerical ratings for physical demands, environmental exposures, and work context factors [39]. Only O*NET entries matching SOC codes beginning with “47” were retained.
• BLS Occupational Employment Statistics: Employment totals and wage data were incorporated from BLS Occupational Employment Statistics to contextualize injury counts relative to workforce size [40].

The datasets were aligned based on their occupational identifiers. After removing duplicates and incomplete entries, the final analytic file contained 86 construction occupations and 344 occupation-condition observations, each representing a unique combination of job role and MSD profile.

2.3. Variables and Coding

The categorical moderators were numerically coded to facilitate regression analysis. Following established industry definitions, MSD_outcome was coded as 0 for events resulting in job transfer or restriction (DJTR) and 1 for those resulting in days away from work (DAFW). Similarly, MSD_symptoms was coded as 0 for injuries involving general soreness or pain and 1 for injuries classified as sprains, strains, or tears. The 43 occupational characteristics from O*NET served as continuous numerical predictors, measured on a progressive scale from 0 (no occurrence/importance) to 100 (continual occurrence/extreme importance). These descriptors reflect ergonomic exposures relevant to construction, such as cramped workspaces, hazardous conditions, noise environments, or PPE requirements.

To account for differences in skill and training requirements within construction occupations, the O*NET’s Job Zone classifications were recoded into a five-point numeric scale (0–4). This scale represents the following educational benchmarks: (0) no High School Diploma or General Educational Development (GED) credential; (1) High School Diploma or GED; (2) Associate Degree (AA/AS); (3) Bachelor’s Degree; and (4) Master’s Degree or higher.

It should be noted that O*NET descriptors represent occupation-level aggregated averages rather than task-specific or individual-level exposures. As such, variability in work activities, task sequencing, and exposure intensity among workers within the same occupation may not be fully captured. This aggregation introduces the potential for ecological inference, whereby associations observed at the occupational level may not directly reflect individual worker risk. Consequently, the findings of this study should be interpreted as characterizing relative differences in MSD risk across construction occupations rather than predicting individual-level injury outcomes. Future research should incorporate task-level exposure measurements, wearable sensor data, or observational methods to validate and refine the occupation-level patterns identified here.

2.4. Analytical Procedure

All data preparation, matching, and statistical analyses were performed using SAS^® 9.4 (SAS Institute Inc., Cary, NC, USA). Prior to analysis, MSD counts were log-transformed to address skewness and satisfy the assumption of normality. Subsequently, a stepwise multiple regression procedure was then used to identify which occupational descriptors significantly predicted MSD frequency.

The model-building process proceeded as follows:

• Moderator Variables Entered First: MSD outcome type, MSD symptom type, and adjusted Job Zone level were included at the outset as fixed components of the model.
• Screening of Potential Predictors: All 43 O*NET descriptors defining activities [41], context [42], and styles [43] were evaluated for entry using a liberal threshold (p ≤ 0.25) to avoid prematurely excluding relevant construction-specific factors.
• Retention Based on Contribution: Predictors that did not provide meaningful explanatory value were removed, using a stricter significance criterion (p ≤ 0.05).
• Final Model Interpretation: Predictors that remained were classified as risk amplifiers (positive association) or risk mitigators (negative association), enabling ergonomic interpretation tailored to construction environments.

2.5. Model Performance

The final model retained 10 significant predictors and explained approximately 49% of the variation in MSD event frequency across construction occupations. This level of explanatory power is consistent with ergonomic research where injury outcomes are influenced by multiple physical and organizational factors.

3. Results

3.1. Predictor Screening and Sector-Specific Patterns

To balance model complexity with predictive power, a systematic stepwise selection procedure was employed. A liberal entry criterion of p ≤ 0.25 ensured that potentially relevant construction-specific factors were considered, while a stringent retention threshold of p ≤ 0.05 determined which variables remained in the final model. During each iteration, the model re-evaluated all variables to ensure only those contributing significantly to the explained variance were retained after controlling for fixed moderators. Ultimately, this procedure narrowed the 43 O*NET descriptors down to 10 significant predictors, removing 33 variables from further consideration. These non-significant predictors are shown in Table 1, alphabetized by variable name for easier reference. Many variables traditionally associated with MSD risk—such as bending or twisting (F = 0.03, p = 0.8533), kneeling/crouching (F = 1.44, p = 0.2308), repetitive motions (F = 0.81, p = 0.3703), and vibration exposure (F = 0.85, p = 0.3583)—did not meet the retention threshold in the construction-only model.

Table 1. MSD Non-Predictors Stepwise Regression Results (n = 33).

MSD Non-Predictor	Description	F-Value	Pr > F
accuracy_import	Importance of Being Exact or Accurate	1.70	0.1934
auto_degree	Degree of Automation	1.03	0.3110
balance	Spend Time Keeping or Regaining Balance	0.18	0.6690
bending	Spend Time Bending or Twisting Your Body	0.03	0.8533
climbing	Spend Time Climbing Ladders, Scaffolds, or Poles	1.79	0.1820
common_attire	Wear Common Protective or Safety Equipment such as Safety Shoes, Glasses, Gloves, Hearing Protection, Hard Hats, or Life Jackets	0.15	0.6977
competition	Level of Competition	3.45	0.0640
contaminants	Exposed to Contaminants	0.06	0.8058
crouching	Spend Time Kneeling, Crouching, Stooping, or Crawling	1.44	0.2308
enclosed_veh	In an Enclosed Vehicle or Operate Enclosed Equipment	2.07	0.1508
error_conseq	Consequence of Error	0.24	0.6253
freedom_dec	Freedom to Make Decisions	0.02	0.8903
freq_dec	Frequency of Decision Making	0.77	0.3801
hands	Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls	0.67	0.4154
haz_equip	Exposed to Hazardous Equipment	2.04	0.1546
high_places	Exposed to High Places	1.97	0.1619
impact_rslt	Impact of Decisions on Co-workers or Company Results	0.61	0.4349
indoors_envr_ctrl	Indoors, Environmentally Controlled	0.14	0.7057
indoors_nonenvr_ctrl	Indoors, Not Environmentally Controlled	0.00	0.9510
lights	Exposed to Extremely Bright or Inadequate Lighting Conditions	0.13	0.7194
open_veh	In an Open Vehicle or Operating Equipment	0.11	0.7418
out_exposed_weather	Outdoors, Exposed to All Weather Conditions	0.94	0.3325
out_under_cover	Outdoors, Under Cover	0.00	0.9809
pace_equip	Pace Determined by Speed of Equipment	0.12	0.7305
physical_prox	Physical Proximity	0.36	0.5467
repetitive	Spend Time Making Repetitive Motions	0.81	0.3703
running	Spend Time Walking or Running	0.25	0.6195
standing	Spend Time Standing	2.55	0.1110
structure_wk	Determine Tasks, Priorities and Goals (i.e., can regulate work/life balance)	2.35	0.1259
tasks_repeat	Importance of Repeating Same Tasks	3.75	0.0537
temperature	Exposed to Very Hot or Cold Temperatures	0.09	0.7643
vibration	Exposed to Whole Body Vibration	0.85	0.3583
work_schedule	Work Schedules	0.55	0.4582

This screening pattern aligns with the nature of construction work, where such biomechanical exposures are nearly universal across trades and therefore do not discriminate MSD risk between occupations. In other words, the absence of significance for these variables reflects their ubiquity rather than their irrelevance.

Environmental descriptors that are common across multiple construction roles, such as outdoor weather exposure (F = 0.94, p = 0.3325) and temperature extremes (F = 0.09, p = 0.7643), also failed to reach significance, suggesting that they do not meaningfully explain variation in MSD event counts within the sector.

3.2. Descriptive Characteristics of Predictors Retained in the Model

The 10 occupational descriptors that met significance thresholds are summarized in Table 2. These variables represent high-intensity exposures typical of construction trades.

Table 2. Descriptive Statistics: MSD Predictors (n = 10).

Ref.	MSD Predictor	Description	Mean	SD
a.	cramped	Exposed to Cramped Workspace, Awkward Positions	55.2	13.3
b.	disease	Exposed to Disease or Infections	7.5	7.2
c.	duration_wk	Duration of Typical Work Week	66.8	14.8
d.	haz_conditions	Exposed to Hazardous Conditions	50.4	17.3
e.	minor_bcbs	Exposed to Minor Burns, Cuts, Bites, or Stings	58.8	11.8
f.	noise	Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable	79.7	11.5
g.	radiation	Exposed to Radiation	5.8	7.1
h.	sitting	Spend Time Sitting	27.6	19.2
i.	special_attire	Wear Specialized Protective or Safety Equipment such as Breathing Apparatus, Safety Harness, Full Protection Suits, or Radiation Protection	48.4	16.6
j.	time_pressure	Time Pressure	71.5	8.7

Notes: Letters a–j in the reference (Ref) column are used to identify predictors in narrative. SD = Standard Deviation.

Key examples include:

• Noise exposure: mean = 79.7, SD = 11.5, indicating that most construction occupations operate in consistently loud environments.
• Cramped workspace: mean = 55.2, SD = 13.3, consistent with framing, mechanical, and finishing tasks performed in enclosed areas.
• Special protective attire: mean = 48.4, SD = 16.6, characteristic of trades requiring respirators, harnesses, or heat-resistant gear.
• Time pressure: mean = 71.5, SD = 8.7, reflects production-driven task pacing typical in construction.

These descriptive statistics support the view that the retained predictors capture the most intense and variable exposures within SOC 47, enabling meaningful discrimination between occupations.

3.3. Final Regression Model: Risk Amplifiers and Mitigators

The final regression model identified seven risk amplifiers and three risk mitigators, all statistically significant at p < 0.05 (Table 3). Together, these variables explained 48.9% of the variance in MSD event counts across construction occupations.

Table 3. MSD Predictor Regression Model.

Ref.	Variable	Estimate (x)	SE	t Value	Pr > |t|	Exp (x)
MSD Risk Amplifiers
b.	disease	0.0633	0.0135	4.67	<0.0001	0.0653
f.	noise	0.0386	0.0103	3.77	<0.001	0.0394
h.	sitting	0.0306	0.0068	4.51	<0.0001	0.0311
i.	special_attire	0.0303	0.0066	4.57	<0.0001	0.0307
e.	minor_bcbs	0.0289	0.0089	3.26	<0.01	0.0293
j.	time_pressure	0.0249	0.0104	2.39	<0.05	0.0252
d.	haz_conditions	0.0220	0.0098	2.24	<0.05	0.0223
MSD Risk Mitigators
c.	duration_wk	−0.1151	0.0123	−9.33	<0.0001	−0.1087
a.	cramped	−0.0772	0.0107	−7.19	<0.0001	−0.0743
g.	radiation	−0.0595	0.0121	−4.91	<0.0001	−0.0577
MSD Event Moderators
Jobzone (Adjusted: 0–4)		0.9197	0.2366	3.89	<0.001	1.51
MSD_outcome		0.8420	0.1524	5.53	<0.0001	1.32
MSD_symptoms		0.3894	0.1524	2.56	<0.05	0.48
MSD_outcome + MSD_symptoms		1.2314				2.43
Intercept		6.8163	1.0324	6.60	<0.0001	912.62
Adjusted R-square		0.4894

Note: Letters a–j in the reference (Ref) column are used to identify predictors in narrative.

3.3.1. Risk Amplifiers (Positive Predictors)

The following occupational descriptors were significantly and positively associated with MSD frequency, meaning that higher exposure corresponds to higher MSD counts. Several statistically significant predictors identified in this study likely function as contextual or proxy indicators of hazardous and complex construction work environments rather than direct causal agents of musculoskeletal injury. The variables are presented in descending order based on the magnitude of their estimated effect.

• Exposure to Disease or Infections (Estimate = 0.0633, p < 0.0001): This was the strongest amplifier. It reflects occupations with extensive contact with wastewater, drainage lines, or contaminated materials (e.g., plumbers, pipefitters). Such environments can exacerbate fatigue, inflammation, or injury susceptibility.
• Distracting/Uncomfortable Noise (Estimate = 0.0386, p < 0.001): High noise exposure often coincides with heavy machinery operation and power tool use. Noise can impair communication and situational awareness, heightening the chance of awkward or forceful movements that increase MSD risk.
• Time Spent Sitting (Estimate = 0.0306, p < 0.0001): In construction, sitting primarily represents equipment and vehicle operation, not sedentary clerical work. Operators experience prolonged static loading and whole-body vibration, well-known contributors to low-back and trunk MSDs.
• Special Protective Attire (Estimate = 0.0303, p < 0.0001): Full-body protective suits, respirators, fall-arrest harnesses, and arc-flash gear can restrict mobility, add weight, and alter postural mechanics—amplifying physical strain during task execution.
• Exposure to Minor Burns, Cuts, Bites, or Stings (Estimate = 0.0289, p < 0.01): This variable does not imply that minor injuries directly cause musculoskeletal disorders. Rather, it likely reflects high-risk, multifaceted construction work environments characterized by frequent interaction with sharp tools, abrasive materials, or hazardous conditions. Its significance suggests that this descriptor may function as a proxy indicator of overall workplace hazard intensity, where complex task demands and cumulative physical strain increase MSD risk. Further research is warranted to clarify the direct and indirect mechanisms linking these environments to musculoskeletal outcomes.
• Time Pressure (Estimate = 0.0249, p < 0.05): Time pressure directly reflects production-driven schedules and compressed timelines—conditions that encourage workers to cut micro-breaks, lift more rapidly, or assume awkward positions to maintain workflow.
• Exposure to Hazardous Conditions (Estimate = 0.0220, p < 0.05): This includes tasks near energized systems, moving vehicles, unstable surfaces, and confined excavation areas. Such high-hazard contexts require reactive, forceful movements that increase MSD risk.

3.3.2. Risk Mitigators (Negative Predictors)

The following occupational descriptors were significantly and negatively associated with MSD frequency, indicating potentially protective conditions or tasks with inherently lower biomechanical loading. The variables are presented in descending order based on the magnitude of their estimated negative effect.

• Duration of Typical Work Week (Estimate = −0.1151, p < 0.0001): This variable showed the strongest protective association. In construction, longer work weeks often correspond to supervisory or technical positions rather than direct manual labor. These roles primarily involve planning, oversight, or inspection tasks with substantially lower biomechanical loading.
• Cramped Workspace Exposure (Estimate = −0.0772, p < 0.0001): Although this finding appears counterintuitive, cramped-space tasks often occur in controlled environments like mechanical rooms or during interior builds. These contexts may impose a slower work pace and stable surfaces, encouraging workers to engage in more deliberate posture adjustment and controlled movements, thus mitigating MSD risk.
• Radiation Exposure (Estimate = −0.0595, p < 0.0001): This variable typically applies to specialized inspection roles (e.g., non-destructive testing) that operate under strict procedural controls. These roles are often subject to mandated rest periods and highly regulated ergonomic environments designed to minimize exposure, which may inadvertently reduce overall physical strain.

3.3.3. Moderator Effects of MSD Outcome, Symptom Category, and Job Zone

The analysis revealed significant moderating effects based on how the MSD was classified (by outcome, symptom, or job preparation level), suggesting these factors influence the magnitude of the observed MSD counts.

• Job Zone (Estimate = 0.9197, p < 0.001): This variable showed the strongest moderating effect. Higher preparation-level occupations (e.g., heavy equipment operators, inspectors) experienced significantly more MSD events than lower preparation-level roles. This finding is consistent with elevated exposure to specific risk factors in these roles, such as vibration, prolonged static postures, or precision-based tasks.
• MSD Outcome (Estimate = 0.8420, p < 0.0001): MSD injuries resulting in days away from work were significantly more common than those leading to restricted duty. This effect reflects the limited availability of light-duty options in many construction trades, leading to less severe injuries resulting in full time off.
• MSD Symptoms (Estimate = 0.3894, p < 0.05): Sprains, strains, and tears (representing 62.8% of all events) generated significantly more MSD counts than general soreness or pain. This pattern is consistent with the force-intensive and dynamic nature of typical construction tasks.

3.4. MSD Distribution by Job Zone and Injury Characteristics

Table 4 illustrates that:

Table 4. MSD Events by Outcome and Symptoms of per Occupation Job Zone.

MSD Outcome:	Job Transfer/Restrictions				Away from Work				Job Zone Totals (n = 86)
MSD Symptom:	Sore or Pain		Sprains… or Tears		Sore or Pain		Sprains… or Tears
Job Zone	n	%	n	%	n	%	n	%	N	%
No HS or GED (n = 2)	1990	2.5	5260	2.5	5610	2.6	5390	1.9	18,250	2.3
	10.9		28.8		30.7		29.5		100
HS/GED (n = 70)	59,540	75.3	153,980	73.1	162,610	75.1	210,650	73.2	586,780	73.9
	10.1		26.2		27.7		35.9		100
AA/AS (n = 14)	17,490	22.1	51,460	24.4	48,220	22.3	71,830	25.0	189,000	23.8
	9.3		27.2		25.5		38.0		100
MSD Event Totals (N)	79,020	100	210,700	100	216,440	100	287,870	100	794,030	100
	10.0		26.5		27.2		36.3		100

Note: HS/GED = High School/General Educational Development (High School Equivalent diploma); AA/AS: Associate of Arts/Associate of Science (2-year degree).

• High school–level occupations accounted for 73.9% of all MSD events, consistent with their numerical dominance and higher physical demands.
• Sprains, strains, and tears represented 62.8% of all construction MSDs.
• These severe injuries resulted in:
  • 36.3% days away from work.
  • 26.5% restricted duty events.

4. Discussion

The present study provides a focused examination of MSD determinants within U.S. construction-sector occupations, revealing a risk profile distinct from that observed in the broader workforce. Rather than traditional ergonomic predictors such as bending, kneeling, or repetitive motions—which did not discriminate MSD incidence among construction occupations—this analysis found that environmental hazards, constrained body postures associated with equipment operation, and production-driven organizational pressures were the primary factors elevating MSD events in SOC 47. These findings underscore the need to rethink MSD mitigation strategies in construction by extending attention from general biomechanical guidelines to the specific contextual and operational realities of construction worksites.

4.1. Environmental and Contextual Hazards as Key MSD Drivers

Environmental exposures—including noise, hazardous conditions, and potential disease contact—emerged as some of the most influential predictors of MSD frequency [7,44,45]. This contrasts with findings from national-level analyses, where physical repetition or sustained awkward postures often dominate MSD models. Construction workers frequently perform tasks amid high noise and mechanical vibration, in proximity to energized systems, unstable structures, partially enclosed spaces, or contaminated materials. These conditions not only create acute physical strain but also impair workers’ ability to maintain safe posture or coordinate movement, amplifying the risk of sudden, forceful, or compensatory motions that contribute to sprains, strains, or tears.

From a practical standpoint, these findings highlight the need for integrated hazard management that goes beyond PPE and task protocols. Strategies such as early detection of environmental hazards, improved communication systems in noisy zones, and real-time field monitoring technologies (e.g., noise dosimeters or environmental sensors) could significantly reduce MSD risk by allowing supervisors and workers to adjust task methods before fatigue or distraction trigger injury events.

4.2. Equipment Operation, Seating Constraints, and Static Loading

Another strong amplifier—time spent sitting—provides valuable insight into MSD risk among heavy equipment operators, crane operators, and vehicle-based trades. Unlike sedentary office work, seated construction tasks involve vibration exposure, limited lumbar movement, and constrained posture. These conditions create continuous loading on the lower back and hips, elevating MSD occurrence [7]. This suggests that MSD prevention in construction cannot rely solely on general lifting guidelines or stretching programs; ergonomic interventions must include equipment design improvements, vibration damping technology, and rotation schedules for seated operators.

4.3. Organizational Pressures and Musculoskeletal Risk

Time pressure, a significant positive predictor, reflects the deeply embedded production-driven nature of construction projects. Compressed timelines, interdependent trade sequencing, and contractual penalties often push workers to complete tasks faster, reduce micro-breaks, or lift and maneuver materials with less caution. These pressures can override even well-established safety protocols. This finding aligns with research documenting the impact of psychosocial stressors on injury risk across high-demand industries [46,47].

To address this, contractors and project managers may benefit from adopting capacity-based scheduling, improved task planning, and realistic productivity allowances that do not incentivize unsafe acceleration of physical tasks. Such organizational changes could generate meaningful reductions in MSD rates without requiring specialized equipment.

4.4. Why Some Common Biomechanical Predictors Were Not Significant

The absence of traditional ergonomic predictors (e.g., bending, kneeling, vibration) from the significant subset of variables should not be interpreted as suggesting that these exposures are safe or non-contributory. Instead, their non-significance likely reflects their uniformity across construction roles. Because nearly all construction occupations involve manual exertion, these variables do not distinguish high-risk from low-risk occupations. This is an important nuance for practitioners:

MSD prevention strategies cannot rely solely on reducing common biomechanical exposures—they must target differential exposures such as environmental hazards, constrained operator postures, and production speed.

4.5. Protective Factors and Their Practical Implications

The finding that cramped workspace exposure reduced MSD risk appears counterintuitive but likely reflects the nature of certain finishing or mechanical tasks performed in controlled environments with stable surfaces and slower task pace. These contexts inherently limit rapid or forceful movements. Similarly, radiation exposure—associated with highly regulated inspection tasks—predicts lower MSD frequency due to strict procedural oversight and mandatory recovery periods.

These mitigators suggest that task organization and procedural control, rather than job physicality alone, can play a meaningful protective role. Incorporating more regulated workflows, even for non-radiation tasks, could yield ergonomic benefits.

4.6. The Role of Job Preparation and Skill Level

The positive relationship between Job Zone and MSD events challenges assumptions that higher-skilled roles are safer. Instead, occupations requiring more technical training—such as equipment operation, inspection, or specialized installation—often involve static loading, constrained seating, or high concentration in awkward positions. These insights highlight the need to expand ergonomic surveillance beyond manual trades and recognize that skilled technical workers face their own unique ergonomic risks, which should be incorporated into training and safety planning.

5. Implications for Construction Safety Management and Policy

This study provides actionable guidance for policymakers, safety engineers, and construction managers:

• Prioritize environmental hazard mitigation (noise control, hazard isolation) as much as biomechanical training.
• Redesign operator cabins and seating interfaces to reduce vibration and static loading.
• Adopt scheduling practices that minimize time pressure, especially during high-risk phases of a project.
• Enhance PPE procurement policies, ensuring protective attire reduces, rather than adds, ergonomic burden.
• Train supervisors to recognize high-risk combinations of environmental exposure and organizational stress.
• Introduce trade-specific task rotation systems—especially between equipment operation and manual tasks.
• Apply procedural rigor from regulated environments (e.g., radiation-handling tasks) to other high-risk activities.

From a policy standpoint, integrating O*NET descriptors with SOII outcomes—as demonstrated in this study—offers a scalable method for prioritizing ergonomic interventions across construction trades, supporting data-driven approaches to injury prevention.

6. Limitations

While this study provides important insights into MSD risk determinants within the construction sector, limitations should be acknowledged. The analysis relies on publicly available datasets (SOII and O*NET), which, although authoritative, are subject to inherent limitations. SOII data may underreport injuries due to industry reporting practices, and O*NET descriptors, while standardized, represent averaged occupational characteristics rather than task-specific exposures. This may introduce measurement imprecision when describing conditions at specific worksites. This study operates at the occupation level rather than the task or worker level. Construction tasks are highly variable, even within a single occupation. As a result, the model captures broad exposure patterns but cannot account for individual-level variation, daily work sequencing, crew practices, or unobserved psychosocial factors. Construction occupations span only three Job Zones (0–2), with no representation of bachelor’s-level or graduate-level occupations. This limits the generalizability of Job Zone–related findings and may underestimate MSD risks in specialized supervisory or hybrid technical-management roles not captured in SOC 47. This study is cross-sectional and does not allow for causal inferences. The observed associations indicate relationships between job descriptors and MSD outcomes but cannot confirm whether specific exposures directly cause injuries. Longitudinal methods would be required to confirm causal pathways.

7. Conclusions

This study provides sector-specific evidence on the determinants of musculoskeletal disorders (MSDs) across U.S. construction occupations, revealing a risk profile shaped primarily by environmental hazards, equipment-induced posture constraints, and production-oriented work organization. Unlike broader occupational analyses, which often emphasize repetitive movements or static postures, the present findings demonstrate that MSD risk in construction is driven by a set of exposures that reflect the dynamic, high-variability nature of construction worksites. Predictors such as noise intensity, hazardous working conditions, specialized protective gear, and constrained operator seating were among the strongest risk amplifiers, while mitigators tended to reflect structured, controlled work environments.

These insights underscore the need for a multidimensional approach to injury prevention in construction that integrates ergonomic design, worksite hazard control, and organizational strategies. Interventions aimed at reducing noise exposure, minimizing equipment vibration, optimizing PPE design, and moderating time pressure could produce substantial reductions in MSD burden. Moreover, task planning and scheduling practices that allow for controlled pacing and rotation between high- and low-strain tasks may reduce the frequency of severe sprains, strains, and tears that dominate construction injury statistics.

Overall, this study contributes an occupation-centered, data-driven framework for understanding MSD risk in construction. By identifying specific job attributes that amplify or mitigate risk, the results offer a roadmap for contractors, safety managers, and policymakers seeking to design more targeted, effective ergonomic interventions. These findings reinforce the importance of tailoring safety strategies to the unique operational conditions of construction rather than relying on generalized ergonomic guidelines.