Effects of a Multi-Component Ergonomic Intervention on Absenteeism and Biomechanical Risk: A Case Study in a Peruvian Textile SME

2.1 Ergonomic foundations and occupational health in biomechanical and environmental risk management

Ergonomics is the scientific discipline that studies the interactions between people and the components of a work system, with the purpose of optimizing worker well-being without compromising process performance [12, 13]. In the field of occupational health, its application goes beyond correcting unsafe conditions, as it guides workstation design toward matching the task's physical, cognitive, and environmental demands with operators' functional capacities, thereby reducing exposure to biomechanical risk factors [14].

Among the components of physical ergonomics, ambient illuminance, expressed in lux, directly influences visual comfort and the execution of tasks requiring precision [15]. This relationship is especially relevant in the textile industry, where activities such as cutting and sewing demand high visual acuity. When lighting levels are insufficient, ocular fatigue increases and the likelihood of errors during the production process rises; at the same time, workers adopt compensatory postures that increase muscular load and favor the onset of musculoskeletal discomfort [16-18].

Quantifying these risk factors requires standardized assessment procedures that allow for objective characterization of worker exposure [19]. Tools such as Rapid Entire Body Assessment (REBA) and Occupational Repetitive Actions (OCRA) estimate the level of risk associated with forced postures, repetitive movements, force application, and recovery times [20]. The incorporation of these methodologies, complemented by software for angular biomechanical analysis, strengthens ergonomic diagnosis and provides technical evidence to support workstation redesign, workflow reorganization, and the reduction of postural overload in manufacturing systems [21].

2.2 Musculoskeletal disorders, absenteeism, and the effectiveness of comprehensive ergonomic models in small and medium-sized enterprises

MSDs comprise a set of inflammatory and degenerative conditions affecting tendons, muscles, joints, and nerve structures, whose onset or aggravation is associated with prolonged exposure to repetitive movements, static postures, and high physical effort [22]. In the textile industry, the coexistence of workstations with limited adjustability, intensive production rates, and highly repetitive tasks increases the incidence of these conditions, limiting workers' functional capacity [23, 24]. Their consequences extend beyond the clinical domain, translating into higher levels of work absenteeism, disruptions to operational continuity, and increased costs stemming from replacements, unproductive time, and efficiency losses [2, 3].

Recent evidence supports the implementation of comprehensive ergonomic models grounded in a systemic approach, whose efficacy exceeds that achieved through isolated interventions [25]. These models combine actions on the biomechanical design of workstations with improvements in environmental conditions and organizational adjustments, incorporating measures such as lighting optimization, thermal comfort control, scheduled active breaks, and task rotation [26, 27]. Reported outcomes show reductions of between 40% and 70% in both ergonomic risk levels and sick days associated with MSDs, magnitudes that are seldom achieved through interventions focused on a single risk factor [4].

However, the transfer of these models to textile SMEs located in emerging economies faces constraints that the literature does not always address in sufficient depth. Budgetary limitations, infrastructure rigidity, and reduced investment capacity hinder the adoption of technologically complex solutions [28]. Consequently, the most viable proposals are those that integrate low-cost operational modifications with staff-participation mechanisms and occupational-health awareness programs, thereby improving ergonomic conditions without requiring structural transformations incompatible with these organizations' financial reality [11, 29].

The results obtained correspond to the empirical validation of the formulated ergonomic model and are presented according to the main intervention axes derived from its methodological phases. Accordingly, the findings are grouped around the assessment of ergonomic risks, the analysis of causes, the implementation of improvements, and the observed effects on work absenteeism indicators.

4.1 Initial ergonomic assessment

The initial ergonomic assessment aimed to identify the level of exposure to biomechanical and postural risks across the different processes of the production line. For this purpose, the REBA and OCRA methods were selected, as both allow a comprehensive assessment of ergonomic conditions in work environments where manual, repetitive tasks with frequent postural variations predominate.

To ensure the reliability and accuracy of the ergonomic assessments, a hybrid data-collection protocol was implemented. Tasks were recorded through direct on-site observation and complemented with video recordings to allow a detailed analysis of micro-movements and work cycles.

The subsequent analysis of postures and the precise measurement of the joint angles required by the REBA and OCRA methods were performed using the Ergoniza software, which eliminated subjectivity in angular estimation and ensured data consistency.

As shown in Table 1, the results of the initial assessments revealed high and very high-risk levels in the cutting and sewing processes, while embroidery and packaging activities showed medium levels. Considering the complete sample of 11 operators, the overall average score was 7.27 points on REBA and 7.68 points on OCRA, corresponding to medium-to-uncertain risk levels, evidencing significant exposure to forced postures, repetitive movements, and constant manual handling.

Table 1. Risk levels before the interventions

Note: Rapid Entire Body Assessment (REBA); Occupational Repetitive Actions (OCRA).

Likewise, the most frequently reported symptoms among workers corresponded to lower-back, upper-back, and neck pain, associated with prolonged static postures, repetitive tasks, and inadequate lighting in the work area, factors that contributed to the increase in absenteeism rates.

These results are consistent with the records obtained in the company's documentary review, which showed that during the 2023-II reporting period (August–December 2023) an average of 13 absences attributable to MSDs were recorded per operator, with an average duration of 230 minutes per absence, representing a mean cumulative absence time of 4,140.27 minutes per operator - equivalent to approximately 69 hours, or eight working days, per operator - and a combined total of 45,543 minutes across the 11 operators. However, these average values do not accurately reflect the organizational context, in which staff from critical areas, such as cutting and sewing, reported up to 20 absences during that period, with an average duration of 300 minutes per event, practically equivalent to a full working day.

For the purposes of this study, an absence event was operationally defined as any instance of lost working time attributed to musculoskeletal pain, whether corresponding to a full-shift absence, a partial-shift interruption, or time away for a medical consultation or company-granted permit related to musculoskeletal symptoms. Attribution to musculoskeletal causes was established through documentary review of the company's absence and permit registry, maintained by the person responsible for personnel management; each entry considered in this study explicitly recorded musculoskeletal pain as the stated reason for the absence or permit granted. The complete operator-level absenteeism data (frequency, duration, and cumulative time, before and after the intervention, for each of the 11 operators) are presented in Table A1 to allow full verification of the aggregate values reported in this study.

4.2 Root cause identification and intervention design

The root-cause analysis, carried out using the Ishikawa technique (Figure 1), made it possible to group the main risk factors into three dimensions:

(1) Physical, related to forced postures, repetitive movements, and lack of lumbar support.

(2) Environmental, mainly due to inadequate illuminance levels.

(3) Organizational, related to the absence of active breaks and limited task rotation.

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Figure 1. Ishikawa diagram

The first line of action was the establishment of an active-break program designed to reduce muscle fatigue and prevent MSDs. This implementation was based on an analysis of the workday to identify critical moments involving static postures or repetitive movements. Based on this diagnosis, a specific break schedule was established for each process, varying in frequency and duration according to the physical demand of the task. The result is shown in Table 2.

Table 2. Breaks assigned by process

Simultaneously, the specific exercise protocol shown in Table 3 was developed, focused on the most affected body areas.

Table 3. Exercises for active breaks

Notably, passive breaks (without activity) were ruled out, favoring muscle recovery through brief, controlled physical activation. The intervention was preceded by awareness-raising sessions and weekly follow-up.

In addition to the physical-activity intervention, work methods and workstations were redesigned to correct forced postures. In the cutting process, the intervention was twofold: first, the fabric-laying method was changed to a two-operator collaborative system to reduce the frequency and amplitude of trunk flexion; and second, the height mismatch between operators and work tables (which generated uncomfortable postures) was mitigated through the acquisition of modular platforms.

For the sewing process, an ergonomic chair design was developed based on the task requirements. Key design criteria included a pneumatic height-adjustment system, a backrest with lumbar support adapted to the curvature of the spine, and armrests designed to maintain a 90° elbow angle with a depth that does not interfere with the machinery. Footrests were not considered, given that the feet are in constant interaction with the pedals of the sewing machines.

Finally, the third intervention axis addressed the reported visual fatigue caused by inadequate lighting.

The objective was to bring the mean illuminance (E_m) levels into compliance with the standards set by Technical Standard EM.010 of the National Building Regulations, which stipulates 500 lux for dyeing and cutting, and 750 lux for sewing.

MASTER TL-D Secura 58W/840 luminaires (luminous flux of 5,240 lm) were selected, and a lighting calculation was performed for each area (Eqs. (1) and (2)) to determine the number of luminaires and their distribution. The results are shown in Table 4.

$\Phi_T=\frac{E_m * S}{C_u * C_m}$   (1)

where,

Φ_T = Total luminous flux

E_m = Desired average illuminance

S = Work-plane surface area

C_u = Utilization coefficient

C_m = Maintenance coefficient

$N=\frac{\Phi_T}{\Phi_L * n}$   (2)

where,

N = Number of luminaires required

Φ_L = Local luminous flux

n = Number of lamps per luminaire. In this installation, single-lamp luminaires were used (n = 1).

For all three areas, a uniform 4 × 4 luminaire grid (16 units) was installed rather than the strict photometric minimum calculated for each area. This was necessary because, in the dyeing area, the calculated minimum (13.52, rounded up to 14 luminaires) could not be arranged as a uniform grid: a 4 × 3 layout yields only 12 luminaires, below the required minimum, while an irregular 4 × 3-plus-2 arrangement would have produced non-uniform illumination across the work area. Adopting the same 4 × 4 grid in all three areas therefore ensured both compliance with the photometric minimum and consistent, uniform light distribution; for cutting and sewing, this grid already matches their respective calculated minimums (16 luminaires each).

Table 4. Calculation of the number of luminaires

After installation, it was verified that the resulting mean illuminance (E_m) in each area met or exceeded the legal standard: Dyeing (591.82 lux > 500), Cutting (533.08 lux > 500), and Sewing (750.27 lux > 750). This intervention ensured regulatory compliance and mitigation of visual-fatigue risk.

4.3 Effect of the interventions on work absenteeism

Following the implementation of the interventions, risk scores showed a significant reduction.

Considering all 11 operators, the overall REBA average decreased from 7.27 (medium risk) to 4.09 (medium risk), a 43.8% reduction, while the OCRA average decreased from 7.68 (uncertain level) to 4.82 (optimal level), a 37.3% reduction, as shown in Table 5. Both reductions were confirmed as statistically significant through the Wilcoxon signed-rank test (REBA: W = 0, p = 0.001; OCRA: W = 0, p = 0.004), as detailed in Table 6, evidencing the effectiveness of the model in mitigating physical and postural factors.

Work absenteeism indicators also showed a substantial improvement. The average number of absences fell from 13.00 to 5.82 events per operator, the average duration per event fell from 230.00 to 147.82 minutes, and the mean cumulative absence time fell from 4,140.27 to 1,188.36 minutes per operator (a combined-group total of 45,543 to 13,072 minutes), a 71.3% reduction. This mean cumulative time was calculated as the product of frequency and duration recorded for each operator, averaged across the 11 operators; this differs from, and is not equivalent to, the product of the group's mean frequency and mean duration, since these two variables are correlated at the individual level.

To statistically validate these reductions, the Shapiro-Wilk test was first applied to the paired pre-post differences of each variable. Normality was rejected for two of the five variables (REBA, p(SW) = 0.010; cumulative absence time, p(SW) = 0.042), while the remaining three did not show a significant departure from normality (OCRA, p(SW) = 0.482; absence frequency, p(SW) = 0.326; absence duration, p(SW) = 0.081). Given this mixed result and the small sample size (n = 11), the non-parametric Wilcoxon signed-rank test (Minitab 2019) was applied uniformly to all five variables for methodological consistency. Table 6 presents the mean values before and after the intervention, the percentage reduction, and the Wilcoxon test statistics (W and p-value) for REBA, OCRA, absence frequency, duration, and cumulative time. All five variables showed statistically significant reductions (p < 0.01), confirming that the improvements are not the product of chance but consistent with the interventions implemented among the group of 11 operators comprising the T-shirt assembly process in the textile SME.

Effect sizes (matched-pairs rank-biserial correlation r, Table 6) ranged from 0.871 to 0.884, indicating a uniformly large effect across all variables.

Table 5. Risk levels after the interventions

Note: Rapid Entire Body Assessment (REBA); Occupational Repetitive Actions (OCRA).

Table 6. Statistical validation of results (Wilcoxon signed-rank test, n = 11)

Note: Δ% = percentage reduction; Mdn Δ = median difference (post - pre); p(SW) = Shapiro-Wilk normality p-value on paired differences; W, Z, p = Wilcoxon signed-rank statistic, normal-approximation Z (continuity-corrected), and exact two-sided p-value; r = matched-pairs rank-biserial correlation (effect size).

The results obtained provide preliminary evidence that the proposed ergonomic model can serve as an effective alternative for reducing exposure to musculoskeletal risk factors and lowering work absenteeism in small textile-sector enterprises. The integration of interventions addressing the workstation, the physical environment, and task organization made it possible to act simultaneously on the main determinants of biomechanical load, generating quantifiable improvements in working conditions in this case study.

The post-intervention assessments showed significant reductions in the REBA and OCRA scores, confirming the effectiveness of workstation redesign, work-method optimization, and improved lighting conditions. Together, these actions decreased exposure to forced postures, repetitive movements, and muscular strain associated with an inadequate work environment.

The incorporation of structured active breaks and training activities strengthened the organizational dimension of the model, favoring the adoption of preventive practices and contributing to the reduction of absenteeism. The results show that ergonomic improvements produce effects that go beyond reducing physical risk, also being reflected in operational indicators linked to the continuity of the production process.

This study corresponds to a one-group pre-experimental case study conducted in a single small enterprise with 11 operators; consequently, its results should be interpreted as preliminary evidence rather than as generalizable proof of causal effectiveness, sustainability, or applicability to other SMEs. The design did not include a control group, a long-term follow-up beyond the immediate post-intervention measurement, a cost-effectiveness analysis, an assessment of potential adverse effects, or adherence data, all of which would be necessary before recommending the model as a validated occupational-health policy tool. Notwithstanding these limitations, the model developed offers favorable conditions for implementation in other SMEs with similar operational characteristics. Its structure, based on continuous-improvement principles and low-cost interventions, facilitates its adaptation to organizations with budgetary constraints, where the incorporation of technologically complex ergonomic solutions is typically limited.

The observations made during implementation also identify opportunities for future research. It would be pertinent to develop longitudinal studies assessing the influence of compensation systems on adherence to active breaks and the adoption of safe working postures. Likewise, it would be worthwhile to compare interventions focused exclusively on ergonomic improvements with others incorporating occupational safety and health awareness strategies, in order to determine the specific contribution of organizational culture to the sustainability of ergonomic improvements in small and medium-sized enterprises.