Standing work and low back pain: An experimental analysis of risk factors

Abstract

Background

Standing work is common across a wide range of industries and service sectors and has been associated with the development of low back pain.

Objective

To analyze changes in low back pain across eight experimental scenarios combining exposure to risk factors associated with standing work.

Methodology

A controlled factorial experiment (2³) comprising eight experimental scenarios and 32 simulations. Thirty volunteers (16 men and 14 women) participated in the study. Each participant completed an experimental scenario involving light manual activity in a standing position for 120 min, varying posture (dynamic or static), surface (hard or soft), and the use of insoles (with or without inserts). Every 30 min, low back pain intensity was recorded using a Visual Analog Scale (0 mm = no pain; 100 mm = maximum pain). Results: Time had a significant effect on increases in low back pain relative to baseline (T0 = 1.71, SE = 0.34). The estimated increases in low back pain from baseline were 1.41 at (T1), 2.38 at (T2), 3.07 at (T3), and 3.66 at (T4), all of which were statistically significant (p < 0.001).

Conclusions

Low back pain intensity increased significantly and similarly across all experimental scenarios, regardless of the combination of factors evaluated (posture, surface, or insole use). The results indicate that low back pain, as the starting point of clinical pain perception (≥ 9 mm VAS), manifests itself after 60 min of standing, regardless of the combined study factors.

Keywords

ergonomics musculoskeletal disorders occupational health physical demands/Exposure analysis safety and health work analysis and design

Introduction

Standing work is common across a wide range of activities in both industrial and service sectors. It has been associated with reports of lower extremity and low back pain, increased leg volume, and adverse vascular effects.^1,2 Key factors to consider when characterizing standing work include low-mobility standing postures,^1,3–5 duration of standing work,^1,2,6–8 type of footwear used,^1,9–12 and type of surface,^10,13–18 among other factors.

Moreover, scientific evidence supports the onset of low back pain symptoms among workers performing standing work. Coenen et al. (2017) in a systematic review, concluded that standing work is associated with low back pain.² Additionally, low back pain has been reported to occur within two hours of experimental exposure during both morning and evening sessions.¹⁹ Waters and Dick (2015) compiled evidence from multiple studies indicating that prolonged standing at work affects the lower back. They also identified several factors, such as footwear, insoles, and anti-fatigue mats, that may influence the onset or absence of low back pain.¹

In Colombia, studies have reported assessments of low back pain and other body regions among healthcare workers,²⁰ flower growers,²¹ and manufacturing workers.²² To date, a characterization of standing work in Colombian companies has been conducted by a group of researchers in collaboration with the Surveillance and Control Directorate of the Colombian Ministry of Labor. This study found that 60% of companies have workers standing in confined spaces with limited mobility for prolonged periods, with hard surfaces being the most common.²³ Despite this characterization and the available studies in specific economic sectors, no formal experimental studies have been conducted to examine the influence of different risk factors on the onset of low back pain during standing work.

The objective of this study is to analyze changes in low back pain across eight experimental scenarios combining exposure to risk factors associated with standing work. To achieve this objective, low back pain was evaluated using a randomized, controlled, factorial experimental design during a 120-min simulation.

Materials and methods

Experimental design

The study employed a randomized 2³ factorial design (three factors, each with two levels), as illustrated in Figure 1. This design yielded eight distinct experimental scenarios, each evaluated during a 120-min continuous standing simulation. All trials were conducted at the Biomechanics and Rehabilitation Laboratory of Institución Universitaria-ITM.

Figure 1.

Schematic diagram of the experimental design.

Participants in the experiment

Thirty-two participants volunteered; however, only 30 students of legal age (>18 years) from the ITM University took part in the study, comprising 16 men and 14 women (mean [SD] age = 21.83 [2.13] years; height = 167.98 [8.48] cm; weight = 67.72 [8.24] kg). The dominant foot among the participants was the right foot (87.5%). Potential participants with a history of pain in the lower extremities and/or low back were excluded if they had required previous medical intervention (e.g., care from a doctor, physical therapist, or occupational therapist); performed routine tasks involving prolonged standing; or had a functional disability that compromised their ability to stand continuously for 120 min. After confirming that they met the study inclusion criteria, participants provided written informed consent to the study protocol by signing the consent form, which was endorsed and approved by the Institución Universitaria ITM Research Ethics Committee, as approved on October 18, 2023.

Experimental procedure

Eight experimental scenarios were simulated according to guidelines for factorial experiments (2³) (Figure 1). Each experimental scenario was repeated four times, for a total of 32 simulations. Each simulation was coded, and the order of the simulations was randomized using a spreadsheet. Each participant was assigned a single simulation; there were no washout periods because each simulation had a different participant. Study participants were required to report to the laboratory at the scheduled time for the simulation. The participants knew the research objective, but no participant knew in advance which experimental scenario they would be assigned to. For the assignment to an experimental scenario, simple manual randomization was used, with participants randomly selecting the code for their simulation from a bag.

After selecting the simulation, participants’ height and weight were measured using a calibrated scale and an anthropometer. Participants then had to rest for 30 min in a seated position in a chair before the simulation began. During this time, each participant was informed about the experimental protocol, the tasks they had to perform, and how to fill out the body map form, and the footwear — either with or without inserts — according to the assigned scenario. Additionally, participants could drink water at any time during the 120-min simulation.

The simulation began with the baseline (T0) recording. After this, time was controlled with a stopwatch that had an alarm to record the pain intensity at each time point. At each pain assessment, participants stopped the task, stood upright, recorded their perception on the body map form, and then resumed the task. The simulation ended with the final T4 recording. After the 120-min simulation, participants had to rest in a chair for 30 min. During this time, it was verified that the simulation had not caused any adverse effects on their health.

Factors considered in the experiment

Posture: Two postures were defined in the experiment: static and dynamic. Static posture was defined according to guidelines issued by the German Occupational Safety and Health Committee.²⁴ The guideline indicates that standing statically means being unable to move forward, backward, or sideways within a radius greater than 20 cm. This space was marked with adhesive tape on the floor or mat (depending on the scenario). Participants assigned to the static posture were instructed not to step outside this area (Figure 2 B). For the dynamic posture, participants were instructed to move from site 1 to site 2 at least every 5 min while standing and performing the experimental tasks (Figure 2A). The transfer consisted of walking a distance greater than 20 cm (2.7 m) between each site.

Figure 2.

Factors considered in the experiment. A: Dynamic posture. B: Static posture. C: Anti-fatigue mat. D: Insert insoles. E: Safety boots.

Support surface: Both hard and soft support surfaces were used. The laboratory floor was used as the hard support surface. The laboratory floor had porcelain tiles (Figure 2 B). For the soft surface, an anti-fatigue commercial mat (model no. 5500 modular classic) was chosen. The mat material was styrene-butadiene polymer (SBR) and its dimensions were 90 cm x 90 cm (Figure 2 C). The thickness or height of the mat was 1.58 cm. The mat had medium hardness, with a Shore A rating of 50 ± 5, providing a balance between comfort and support. The tensile strength was 500 PSI (3.45 MPa). The maximum elongation percentage was 400, and the temperature range in which the anti-fatigue mat properties were maintained was −25–80 °C. Both support surfaces were marked with adhesive tape to ensure each of the scenarios.

Insoles for footwear: Footwear was used both without insoles and with insoles inserted. The footwear used was commercial industrial safety boots with unstable soles and metal toe caps. One pair of boots was available for each shoe size from 37 to 42 (Colombian sizes) (Figure 2E). This was done to ensure participant variability and limit the exclusion of participants from the study due to their shoe size. Each participant freely chose the shoe size that best fit them. Commercial insoles for safety boots were used. The insole material was polyurethane and gel (Figure 2D). The insole model was cgel/L, and the insole material was 100% polyester lining, 93% rubber, and 7% textile. There was one pair of insoles between shoe sizes 37 and 42 (Colombian sizes).

Experiment tasks

Two light tasks were simulated, requiring participants to remain standing for a 120-min evaluation period. In the first task, participants had to assemble and disassemble boxes for packing hamburgers. The place where they had to assemble and disassemble the boxes was always adjusted to the height of the participants, preventing them from leaning forward and allowing them to keep their backs straight at all times. Participants were instructed not to bend or lean forward at any time. Participants were not required to maintain a pace of assembling and disassembling boxes, nor a frequency or number of boxes in a given time; participants could perform this light task at their own pace until they decided to start the second task; nevertheless, regarding the workload associated with assembling and disassembling boxes, workload estimates, derived from direct observation of participants and the analysis of selected video recordings, suggested that participants could assemble an average of 10 boxes per minute and disassemble an average of 16 boxes per minute. The second task was optional. It was used especially when participants felt exhausted from assembling and disassembling boxes. The task consisted of assembling a Tetris game with wooden pieces; it could also be alternated with assembling and disassembling puzzles. These activities were carried out during the 120 min that the simulation lasted.

Recording pain intensity

To record the perceived intensity of lower back pain, a body map similar to that proposed in ISO/TS 20646:2018²⁵ was used with the Visual Analog Scale (VAS) (0 mm: no pain; 100 mm: maximum pain). Pain was recorded at five times points every 30 min. Pain intensity was recorded at the start of the experiment (T0), at minute 30 (T1), at minute 60 (T2), at minute 90 (T3), and at minute 120 (T4). Participants were asked to draw a vertical line on the VAS with a red pen. Subsequently, a member of the research team recorded pain intensity using the same ruler for all recordings. To reduce participant bias in pain reporting, a different body map format was used at each time point, so participants did not have the pain intensity from the previous assessment as a reference.

Statistical analysis

R statistical software version 4.5.0²⁶ was used. Descriptive graphs were generated for each scenario, with the mean pain intensity at each assessment moment, expressed on the normal scale and transformed to the square root. Linear mixed-effects models were fitted using the lme4 package, as the initial onset of pain is an individual effect. To improve the mixed linear model with a random effect, a square-root transformation was applied to the outcome variable. Power analyses, marginal estimates, and effect size measures were performed for each of the factors analyzed in the experiment. Statistical hypothesis tests were applied to verify the model's validity.

Results

Post-hoc analysis of the data

A post hoc analysis was conducted using Monte Carlo simulation to assess the statistical power of the factors. The analysis was based on the original linear mixed-effects model with random effects derived from the experimental data. Only the time factor showed statistical power above 80% (100%), with a large effect size (Cohen's f² = 1.07; partial η² = 53%). The remaining factors exhibited medium-low, low, or negligible power in the model. The overall marginal R² of the original model was 33%, indicating that fixed effects explained approximately one-third of the model's variability. The other results can be seen in Table 1. The final version of the original model was tested using only the time effect; however, it was discarded because it did not meet the statistical assumptions.

Table 1.

Results of the post-hoc analysis (power, CI, partial η², and Cohen's f²) in the original model.

Factor/Interaction	Power (%)	95% CI	Partial η²	Cohen's f²
Time	100.0	[96.4–100.0]	0.53	1.07
Posture	8.0	[3.5–15.2]	0.005	0.07
Mat	8.0	[3.5–15.2]	0.02	0.15
Insole	7.0	[2.9–13.9]	0.01	0.11
Gender	14.0	[7.9–22.4]	0.04	0.21
Dominant foot	6.0	[2.2–12.6]	0.007	0.08
Height	6.0	[2.2–12.6]	0.0007	0.03
Weight	36.0	[26.6–46.2]	0.10	0.33
Age	14.0	[7.9–22.4]	0.02	0.15
Time × Posture	8.5	[3.0–15.8]	0.02	0.15
Time × Mat	7.5	[2.5–14.0]	0.008	0.09
Posture × Mat	8.0	[3.4–15.0]	0.004	0.07
Time × Insole	6.5	[2.2–13.0]	0.004	0.07
Posture × Insole	7.0	[2.9–14.0]	0.006	0.08
Mat × Insole	6.5	[2.3–12.5]	0.12	0.36
Time × Posture × Mat	5.5	[2.0–11.0]	0.01	0.10
Time × Posture × Insole	8.0	[3.0–15.0]	0.04	0.2
Time × Mat × Insole	8.0	[3.0–15.0]	0.08	0.29
Posture × Mat × Insole	8.0	[3.0–15.0]	0.06	0.25
Time × Posture × Mat × Insole	5.0	[2.0–11.0]	0.03	0.17

Additionally, a linear mixed-effects model with a square root transformation applied to the response variable was tested. The square root transformation was applied to the original values to reduce positive asymmetry and stabilize variance across levels of the time factor. This transformation is commonly used in continuous data with a skewed distribution, as it improves the approximation to normality without altering the relationship between variables. Overall, the linear mixed-effects model with a square root transformation provided a more robust fit for the relationship between time and the pain variable, controlling for each participant's variability and meeting the statistical assumptions. The time factor had a power above 80% (100%), with a significant effect (Cohen's f² 1.31, partial η² 63%), improving on the original model; the remaining factors studied had medium-low, low, or no power in the model. The overall marginal R² of the transformed model was 37.5%, indicating that the model is more stable than the original, as evidenced by its lower variance. The other results are shown in Table 2. This model was tested only with the time factor, complying with the statistical assumptions presented in section 3.3.

Table 2.

Results of the post-hoc analysis (power, CI, partial η², and Cohen's f²) in the mixed linear model with square root transformation in the pain variable.

Factor/Interaction	Power (%)	95% CI	Partial η²	Cohen's f²
Time	100.0	[96.4–100.0]	0.63	1.31
Posture	8.0	[3.5–15.2]	0.007	0.08
Mat	13.0	[7.1–21.2]	0.05	0.23
Insole	4.0	[1.1–9.9]	0.002	0.04
Gender	10.0	[4.9–17.6]	0.02	0.15
Dominant foot	8.0	[3.5–15.2]	0.01	0.12
Height	7.0	[2.9–13.9]	0.001	0.04
Weight	32.0	[23.0–42.1]	0.09	0.32
Age	16.0	[9.4–24.7]	0.03	0.17
Time × Posture	9.0	[3.5–16.5]	0.03	0.18
Time × Mat	9.0	[3.2–16.8]	0.02	0.14
Posture × Mat	8.0	[3.0–15.0]	0.01	0.11
Time × Insole	7.0	[2.5–13.5]	0.004	0.06
Posture × Insole	7.0	[2.8–13.7]	0.006	0.08
Mat × Insole	8.0	[3.0–15.0]	0.08	0.29
Time × Posture × Mat	7.0	[2.8–13.5]	0.006	0.07
Time × Posture × Insole	8.0	[3.0–15.0]	0.04	0.20
Time × Mat × Insole	8.0	[3.0–15.0]	0.06	0.25
Posture × Mat × Insole	8.0	[3.0–15.0]	0.05	0.24
Time × Posture × Mat × Insole	7.0	[2.8–13.5]	0.03	0.16

Distribution of perceived pain in the original model and in the transformed square root model

Perceived low back pain increased from baseline (T0) across all eight experimental scenarios, as shown in Figures 3 and 4. The static posture showed the greatest increase in perceived pain. However, no significant differences were found among the eight experimental scenarios analyzed in either the original or the square root transformation model. Therefore, participants’ pain perception increased similarly across scenarios. The following covariates were also considered in the analysis: sex, age, height, weight, dominant foot, and two-way and three-way interactions; however, none significantly affected pain increase due to their low or negligible statistical power (Tables 1 and 2).

Figure 3.

Result of the average perceived pain in the lower back in each of the experimental scenarios at each of the times grouped by posture on the scale of the original model.

Figure 4.

Result of the average perceived lower back pain in each of the experimental scenarios at each of the times grouped by posture on the scale of the square root transformed model.

Analysis of the mixed linear model with square root transformation

The linear mixed-effects model with a square root transformation was applied to the pain variable over time, as the most appropriate factor to explain the onset of low back pain in the experiment conducted.

The analysis revealed that the fixed effects of time were significant for all measurements after T0 (p < 0.001). This indicates that pain changed systematically across all time points, not randomly. The estimated intercept was 1.71 (SD = 0.34), reflecting the average level of pain transformed from T0. The estimated increases in pain relative to baseline were 1.41 (T1), 2.38 (T2), 3.07 (T3), and 3.66 (T4), all statistically significant (p < 0.001), indicating a progressive increase in pain over the duration of the experimental scenarios. The other results are shown in Table 3.

Table 3.

Results of the mixed linear model with random effect and square root transformation.

Effect	Estimate	Standard error	t	p-value	Variance	SD	CI 2.5-97.5%	Significance
Fixed Effects
Intercept (T0)	1.71	0.34	5.03	< 0.001	—	—	[1.04 – 2.38]	***
T1	1.41	0.30	4.63	< 0.001	—	—	[0.81 – 1.99]	***
T2	2.38	0.30	7.84	< 0.001	—	—	[1.79 – 2.97]	***
T3	3.07	0.30	10.12	< 0.001	—	—	[2.48 – 3.66]	***
T4	3.66	0.30	12.05	< 0.001	—	—	[3.07 – 4.25]	***
Random Effects
Random intercept (Subject)	—	—	—	—	2.11	1.45	[1.08 –1.93]	—
Residual	—	—	—	—	1.39	1.17	[1.02 – 1.32]	—

Regarding the random-effects structure, the model identified a participant-specific intercept standard deviation of 1.45, suggesting interindividual heterogeneity in baseline pain levels, which explains differences in baseline pain levels across participants. The residual standard deviation was 1.17. The residuals showed an approximately symmetrical distribution with no extreme outliers (range: −2.41 to 2.13), supporting the adequacy of the proposed model.

Model assumptions were assessed using Shapiro—Wilk tests for residual normality (p = 0.734), Breusch—Pagan tests for homoscedasticity (p = 0.191), Durbin—Watson tests for independence of residuals (value = 1.97, close to 2), and multicollinearity was ruled out because only one predictor was included. The final mixed linear model with a random effect and a square root transformation, adjusted to the real scale, based on the analysis data, is shown in Eq. 1. This final model was squared because the pain variable underwent a square root transformation to meet the statistical assumptions. Therefore, when presenting results on the original pain scale, it is necessary to apply the inverse transformation (squaring) to the model predictions to obtain values in the original units of the variable. This is why this model explains and can predict the time of onset of low back pain under conditions similar to those studied.

Pai n_{ij} = (1.7144 + 1.4077 T 1_{ij} + 2.3823 T 2_{ij} + 3.0734 T 3_{ij} + 3.6621 T 4_{ij} + b 0 j + ε_{ij})^{2}

Eq 1.

The variable Pain_ij represents the estimated perceived pain for observation i of subject j on the original scale; the variables T1_ij, T2_ij, T3_ij, T4_ij are dummy indicators for time levels, with T0 as the reference; b_oj ∼ N(0, 2.105) represents the random intercept for subject j; and ɛ_ij ∼ N(0, 1.385) represents the residual error.

Discussion

This study analyzed changes in low back pain across eight experimental scenarios integrating different risk factors associated with standing work. This is one of the first experimental studies to evaluate the onset of low back pain considering three risk factors in a Latin American population. The findings confirm that performing light tasks while standing increases perceived low back pain.

This study also confirms that time is the primary risk factor to which workers are exposed during standing work. In this study, significant changes in pain were already observed at T1 (30 min), with pain increasing over time (30–60–90–120 min) as a result of exposure to standing (T0; t = 5.03, p < 0.001). None of the eight experimental scenarios showed significant differences that could be part of the explanatory model of lower back pain (P > 0.05). This suggests that, across the study factors, low back pain increases similarly in response to all of them, and that only time has a direct influence on the onset of lower back pain.

Although none of the eight scenarios produced statistically significant differences, experimental scenario 5 showed the lowest mean low back pain intensity at the end of the simulation (Figure 3: T4, Pain intensity < 20 mm), whereas experimental scenario 8 showed the highest mean low back pain intensity at the end of the simulation (Figure 3: T4, Pain intensity > 40 mm). These results suggest that incorporating inserts for safety boots, maintaining dynamic postures, and using anti-fatigue rubber mats, in different combinations, may help reduce low back pain, even though the analytical results of this study did not show one scenario to be better or worse than another.

In seven experimental scenarios (1, 2, 3, 4, 5, 6, and 8), mean low back pain at T2 reached or exceeded 10 mm. Interpretation of pain on the visual analog scale (VAS) has shown high variability across studies due to differences in population, evaluation context, and exposure duration. In the field of low back pain, minimally clinically important differences (MCID) of approximately 35 mm for acute cases and 20 mm for subacute or chronic cases have been established and widely adopted in clinical contexts.²⁷ However, a systematic review of acute pain reported a much wider range, from 8 to 40 mm, depending on baseline pain level and the statistical method used, suggesting that MCIDs should be defined contextually.²⁸

Consistent with the above, a systematic review of laboratory studies on occupational exposure to standing work proposed an operational cutoff of ≥ 9 mm on the VAS to identify clinically relevant pain levels, reaching an average of 71 min of standing (or an average of 42 min in so-called “pain developers”).² Other studies used a more conservative threshold of ≥ 20 mm VAS as clinically significant for prolonged standing tasks, consistent with previous clinical references.^29,30 Considering this heterogeneity, the present study adopted a VAS score ≥ 9 mm as the threshold for clinical perception of low back pain, recognizing that a VAS score ≥ 20 mm indicates a clinically relevant change.

According to the criteria adopted in this study, the initial threshold for the onset of low back pain associated with any of the evaluated factors is approximately 60 min (T2). The results of this study are consistent with Dutch guidelines, which indicate a risk when working continuously on one's feet for 1 h or a total of 4 h during the working day.^1,7 A meta-analysis synthesizing experimental studies evaluating standing posture (similar to this study) concluded that low back pain occurs after 42 min of standing.² This time may depend on the study factors analyzed in this research, since in scenario 7, the intensity of lower back pain in T1 was already ≥9 mm before 30 min. Therefore, further investigation of the factors addressed in this study is warranted, including evaluating different types of inserts for safety boots, anti-fatigue mats, and the performance of both light and demanding tasks under static and dynamic posture conditions. In this way, it will be possible to identify the most favorable scenario for reducing pain in the lumbar region.

Study limitations

The results of this study are limited to the experimental conditions and sample analyzed, so its conclusions should be interpreted within that specific framework. However, the results provide evidence on the interaction of the risk factors analyzed and their effect on the onset of lower back pain, observed after approximately 60 min of exposure. While these results could guide the future design of ergonomic interventions for standing work, any extrapolation to real occupational settings should be considered a hypothesis requiring empirical verification in field studies with working populations.

The nature of the tasks performed by participants limits the extrapolation of these findings to work contexts with greater physical demands. Although the selected activities (assembling boxes, putting together puzzles, and playing Tetris) represent light manual tasks that can be performed while standing, their physical demands are substantially lower than those of many real occupational activities. Consequently, the ecological validity of the experimental protocol is limited, as it does not incorporate components characteristic of standing work, such as load handling, repetitive movements, or exposure to psychosocial stressors. It is recommended that future studies include a detailed quantification of physical demands (e.g., movement frequency, upper-limb involvement, or perceived effort level) or replicate the experimental protocol with tasks that more realistically reproduce occupational scenarios in industry and services.

The relatively small sample size may have contributed to the absence of statistically significant differences between some of the factors evaluated, due to the analysis's limited power relative to the actual effect sizes. However, the results provide valuable insights into trends and possible relationships between experimental conditions and the onset of low back pain in a standing position. Future studies with larger participant numbers will confirm and expand these findings, strengthening the understanding of the occupational factors that modulate this type of pain.

Conclusions

Low back pain increased significantly across all experimental scenarios, regardless of whether a static or dynamic posture was adopted, insoles were used or not, or an anti-fatigue mat was present. According to the results, the onset of low back pain—defined as the threshold for clinical pain perception (pain intensity ≥ 9 mm on the VAS)—occurs after approximately 60 min of standing, regardless of the combination of factors studied.

The generalizability of these findings is constrained by the specific experimental conditions and the reliance on a young university student sample, therefore, caution is warranted when extrapolating the results to occupational settings. Further research in real-world occupational environments is needed to validate whether the 60-min continuous standing threshold and the optimal condition identified (dynamic posture, anti-fatigue mat, and no insoles) yield comparable outcomes.

Footnotes

Acknowledgements

The authors would like to thank the volunteer participants in the study, who were students at the Institución Universitaria—ITM. We would also like to thank the Biomechanics and Rehabilitation Laboratory at the Institución Universitaria - ITM for its willingness to support this research.

ORCID iDs

Jonathan Osorio-Vasco

Jessica Rojas-Mora

Carlos Barrera-Causil

Yordán Rodríguez

Ethical approval and informed consent statements

This study was approved by the Research Ethics Committee of the Institución Universitaria ITM, as communicated on October 18, 2023, and was classified as minimal risk in accordance with Resolution 8430 of 1993 of the Ministerio de Salud de Colombia. All participants provided written informed consent prior to participation, acknowledging the experimental protocol.

Informed consent

Participants provided written informed consent to voluntarily participate in the study's research protocol.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by Institución Universitaria ITM and the Facultad Nacional de Salud Pública of the Universidad de Antioquia. This article forms part of the project entitled “Standing work: evaluation of the effects and risk factors on the lower limbs and low back,” funded through the internal call for proposals for the creation of the 2022 Bank of Eligible Projects for Science, Technology, Innovation, and Creation. It also contributes to the doctoral thesis entitled “A model for predicting symptoms in the lower extremities and low back based on exposure to risk factors associated with standing work.”

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The data supporting the findings of this study are available upon reasonable request from the corresponding author but are not publicly available due to ethical and legal restrictions. Public data sharing could violate Colombian Law 1581 of 2012 on personal data protection and the conditions established by the ethical approval granted by the Research Ethics Committee of Institución Universitaria ITM. If necessary for verification purposes, anonymized data may be shared directly with reviewers or editors, provided that such sharing complies with ethical approval and applicable legal requirements.

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