Reader TTS

ABSTRACT The aim of this study is to investigate the impact of personal factors, habitual factors, and work-related factors on work-related musculoskeletal disorders (WRMSDs) among dumper

operators. In total, 248 dumper operators working in an iron ore mine were considered for this study. A questionnaire was developed and administered to collect dumper operators'

personal, habitual, and work-related data. The reliability of the questionnaire was cross-checked by Cronbach alpha and the test–retest method. The values of Cronbach alpha for all latent

variables were above 0.7, and the correlation coefficient of the questionnaire items at Time 1 and Time 2 was above 0.82. After verifying the validity (i.e., convergent and divergent) of the

questionnaire data, the relationship between the factors under consideration was examined by structural equation modeling (SEM). The SEM demonstrated a moderate fit, with \(\frac{{\upchi

}^{2}}{df}\) value of 1.386, comparative fit index (CFI) of 0.86, goodness-of-fit index (GFI) of 0.72, adjusted goodness-of-fit index (AGFI) of 0.69, Tucker-Lewis Index (TLI) of 0.83, normed

fit index (NFI) of 0.71 and root mean square error of approximation (RMSEA) of 0.051. The SEM analysis revealed a positive relationship between WRMSDs and personal factors (with path

coefficient = 0.313 and p < 0.05) as well as work-related factors (with path coefficient = 0.296 and p < 0.05). However, the relationship between WRMSDs and habitual factors was not

statistically significant (with path coefficient = 0.142 and p > 0.05). Overall, this study provides valuable insights into the factors that influence the prevalence of WRMSDs among

dumper operators. The findings highlight the significance of personal and work-related factors by which one can make a positive decision to prevent and reduce the incidence of WRMSDs among

dumper operators. SIMILAR CONTENT BEING VIEWED BY OTHERS MUSCULOSKELETAL DISORDERS AND ITS ASSOCIATED FACTORS AMONG HOSPITAL CLEANERS IN ADDIS ABABA, ETHIOPIA Article Open access 05 February

2024 PREVALENCE AND ASSOCIATED FACTORS OF WORK-RELATED MUSCULOSKELETAL DISORDER SYMPTOMS AMONGST EMERGENCY MEDICAL SERVICE WORKERS Article Open access 05 June 2025 OCCUPATIONAL STRESS AND

MUSCULOSKELETAL DISORDERS IN FIREFIGHTERS: THE MEDIATING EFFECT OF DEPRESSION AND JOB BURNOUT Article Open access 26 February 2024 INTRODUCTION The demand for the raw resources, such as

minerals, has risen globally due to urbanization, increasing demand for finished goods and technological innovations1. To meet this demand, the mining industry relies heavily on Heavy Earth

Moving Machinery (HEMM), such as drills, excavators, transport equipments, and auxiliary equipments. These machines are essential for the efficient extraction and transportation of minerals.

In surface mines, dumpers are commonly used to transport waste and minerals. Dumper operators face several challenges in their daily work, such as—need to sit for extended period2, exposure

to whole-body vibration3,4, and adopting poor posture while driving5. These factors contribute to the development of Work-Related Musculoskeletal Disorders (WRMSDs) among dumper operators6.

WRMSDs is a serious problem for both the operators and the mining industry. The study conducted by the Indian Council of Medical Research (ICMR) showed that 65% of the workers in the mining

industry suffers from WRMSDs7. The economic burden of WRMSDs is significant, with direct and indirect costs estimated to be billions of dollars annually8. In addition, WRMSDs constitute a

significant cause of disability among workers and loss in productivity in many industries worldwide9. Dumper operators, in particular, are at risk of developing WRMSDs due to the physically

demanding nature of their work10. They drive and maneuver heavy vehicles on rough terrain for loading and unloading material repetitively throughout the day11. The prevalence of WRMSDs among

dumper operators in Indian surface metal mines is poorly understood, and further research is needed to identify the risk factors associated with WRMSDs. Several risk factors may contribute

to the development of WRMSDs among dumper operators, such as individual characteristics—age and body mass index12,13, work-related factors—job demand and job control14, the physical

environment of the mine, the ergonomics design of the dumper truck, and management practices13. Recent studies have identified the risk factors associated with WRMSDs among mine

workers15,16,17,18. These studies have adopted various study designs, were multiple dependent variables (such as lower back pain, neck pain, etc.) are combined to form a new and single

dependent variable for the analysis19,20. However, the complex relationships between multiple risk factors and multiple outcomes cannot be studied using this aproach. Advance statistical

techniques, such as structural equation modeling (SEM) is useful in understanding complex relationships between exposures and health outcomes21. In addition, SEM can model the latent

constructs that may not be directly measurable22. Therfore, in this study, SEM technique was used to analyse the relationship between personal, habitual, and work-related factors with

respect to WRMSDs among dumper operators working in Indian surface iron ore mines. RESULTS RELIABILITY OF THE QUESTIONNAIRE The reliability of the custom questionnaire was determined using

the Cronbach alpha and test–retest methods. The Cronbach alpha test determines the internal consistency of the latent variable, whereas the test–retest method determines whether the data

collected is independent of time. CRONBACH ALPHA TEST The internal consistency of the latent variables, including personal, habitual, and work-related factors, was assessed using

Cronbach's alpha test. For each latent variable, the variance of the scores for each item was calculated to determine the variability in responses. Additionally, the variance of the

total scores across all items for each latent variable was also computed. These variances were used in calculation of Cronbach's alpha coefficient using the standard formula. The result

indicated alpha coefficients of 0.82, 0.76, and 0.77 for personal, habitual, and work-related factors. The high coefficient of 0.82 for personal factors shows that the responses to the

items related to personal factors are highly correlated and it measures the same underlying construct. Though the coefficient of habitual factors is less than that of personal factors, it is

considered acceptable23. Similarly, the coefficient of 0.77 for work-related factors is also considered acceptable. Therefore, the questionnaire developed in this study has adequate

reliability and can be considered for the study. TEST–RETEST METHOD The stability and consistency of the custom questionnaire over time were assessed by re-administering it to a subset of

the sample after a 9-month interval. A total of 20% of the participants were selected to participate in the retest. The questionnaire response collected at Time 1 was compared with the

response collected at Time 2. The result indicated a strong (ranging from 0.82 to 0.91) and statistically significant correlation between the responses to the items of the custom

questionnaire at Time 1 and Time 2. VALIDITY OF THE QUESTIONNAIRE CONVERGENT VALIDITY Table 1 shows the correlation matrix that demonstrates the relationship between the observed variables.

The results indicated in Table 1 reveals a moderate but significant correlation among the variables belonging to personal factors (i.e., age, experience in mines, number of childrens,

eduction and marriage status). Similar trend was also observed among the variables belonging to the habitual (i.e., tobacco chewing, medicine intake, smoking cigarettes, alcohol consumption)

and work-related factors (i.e., awkward posture, repetitive work, job demand, work design). DISCRIMINANT VALIDITY The discriminant validity of the latent variables such as personal,

habitual, and work-related factors, was found using correlation coefficient. The results revealed that all independent latent variables had correlation coefficients less than 0.2 and were

statistically insignificant (_p_ > 0.05). This suggested that there is minimal or no relationship between the latent variables. FACTOR ANALYSIS The exploratory factor analysis (EFA) was

done to explore the underlying factor structure of the custom questionnaire. The factor structure obtained from the EFA was in line with the proposed factor structure developed using the

theoretical background. The confirmatory factor analysis (CFA) was then performed to confirm the fit of the observed data with the hypothesized model. The CFA showed that the fit of the

model is moderate23 with \(\frac{{\upchi }^{2}}{df}\) value of 1.386, comparative fit index (CFI) of 0.86, goodness-of-fit index (GFI) of 0.72, adjusted goodness-of-fit index (AGFI) of 0.69,

Tucker-Lewis Index (TLI) of 0.83, normed fit index (NFI) of 0.72 and root mean square error of approximation (RMSEA) of 0.056. It is worth to mention here that there was a significant

increase in the fit of the model when the indicator variables, such as “smoking cigarettes” and “hand pain” were removed from the model. However, the evaluation of the SEM is more objective

while retaining these two indicator variables. Hence, these indicator variables were retained and the final model comprises of four latent variables and ninteen indicator variables as shown

in Fig. 1. STRUCTURAL EQUATION MODELLING The relationship between the variables were examined by structural equation modelling (SEM) using the generalized least squares method for parameter

estimation. The final SEM (as shown in Fig. 2), demonstrated a moderate fit, with \(\frac{{\upchi }^{2}}{df}\) value of 1.386, CFI of 0.86, GFI of 0.72, AGFI of 0.69, TLI of 0.83, NFI of

0.71 and RMSEA of 0.051. The SEM analysis revealed the relationship between the latent independent variables (i.e., personal factors, habitual factors, and work-related factors) and

dependent variables (i.e., WRMSDs). The path coefficient between the “personal factors” and ‘age’ was 0.78 (_p_ < 0.001), indicating a strong and positive correlation. A strong and

positive relationship was also found between “personal factors” and “experience in driving” (path coefficient = 0.83 with _p_ < 0.001). A similar pattern was noticed for the indicator

variables such as "number of children" (path coefficient = 0.43, p < 0.001) and "marriage status" (path coefficient = 0.30, p < 0.001). However, the path coeffcient

between “personal factors” and ‘education’ was weak and negative (path coefficient = − 0.22 with _p_ < 0.05). Similarly, “habitual factors” had a strong and positive relation with

indicator variables such as, “alcohol consumption” (path coefficient = 0.687 with _p_ < 0.001), ‘medicine’ (path coefficient = 0.739 with _p_ < 0.001), and “tobacco chewing” (path

coefficient = 0.767 with _p_ < 0.001). In addition, “work-related factors” had a moderate and positive relation with the “awkward posture” (path coefficient = 0.551 with _p_ < 0.001),

“job demand” (path coefficient = 0.703 with _p_ < 0.001), and “repetitive work” (path coefficient = 0.528 with _p_ < 0.001). However, present study showed a positive and moderate

relation between the “work-related factors” and “work design” (path coefficient = 0.393 with _p_ < 0.001). The path coefficients between ‘WRMSDs’ and five indicator variables such as,

“knee pain”, “lower leg pain”, “mid-back pain”, “neck pain”, and “shoulder pain” were found significant (with _p_ < 0.001) with the path coefficients of 0.632, 0.496, 0.541, 0.452, and

0.695, respectively. Further, when the path coefficient between the latent variables were compared, it was found that ‘WRMSDs’ and “personal factors” had a moderate and positive correlation

(path coefficient = 0.313 with _p_ < 0.05). In contrast, a weak correlation was found between ‘WRMSDs’ and “work-related factors” (path coefficient = 0.296 with _p_ < 0.05). However,

the relationship between ‘WRMSDs’ and “habitual factors” was not found to be statistically significant (path coefficient = 0.142 with _p_ > 0.05). The findings of this study partially

supported the hypothesized model. The influence of personal and work-related factors on WRMSDs were significant, with _p_ < 0.05. This indicates that both these latent factors do affect

the prevalence of WRMSDs in dumper operator’s population. On the other hand, present study showed that the WRMSDs was not influenced by habitual factors of the dumper operators, as _p_ > 

0.05. DISCUSSION This study aims to evaluate the association between personal, habitual, and work-related factors with the WRMSDs among dumper operators. The authors used a custom-made

self-reported questionnaire to collect the data on dumper operators. The reliability of the questionnaire was cross-checked by Cronbach alpha and the test–retest method. The values of

Cronbach alpha coefficent for all latent variables were above 0.7, and the correlation coefficient of questionnaire items at Time 1 and Time 2 was above 0.82. The CFA was then conducted to

assess the fit of the model. Initially, the model had a moderate fit. There was significant increase in the fit of the model after removing the indicator variables, such as “smoking

cigarettes” and “hand pain”. However, these indicator variables were retained in the final model because they made the evaluation of the SEM model more objective. The final model comprised

four latent and ninteen indicator variables that derived an moderate fit. This rigorous assessment of the questionnaire ensured that the conclusions drawn from the analysis were reliable and

valid. The SEM was then used to examine the relationship between the latent variables and the WRMSDs. The path coefficients were computed for all the latent variables. The coefficient

values showed that the personal and work-related factors significantly and positively influence WRMSDs, while habitual factors have poor influence. This study's findings partially align

with the previous studies performed on various occupational groups. For example, previous study24 showed that personal factors, such as age, gender, and BMI, were significantly associated

with the prevalence of WRMSDs. Similarly, one more study25 found that work-related factors, such as job demand, job control, and work-related stress, were significantly related to the

prevalence of WRMSDs among dumper operators. However, the findings of this study revealed that the habitual factors will not significantly influence the prevalence of WRMSDs among dumper

operators, which is not corroborated with the results of previous studies. For example, previous study26 found that unhealthy habits, such as smoking, alcohol consumption, and sedentary

behavior significantly influence the risk of injuries among mine workers. Overall, this study provides valuable insights into the factors that influence the prevalence of WRMSDs among dumper

operators. The findings highlight the significance of personal and work-related factors, by which one can take a positive decision to prevent and reduce the incidence of WRMSDs among dumper

operators. In the future, the findings of this research can be further strengthened by considering a more diverse sample from different mine sites and incorporating additional measures of

reliability and validity to evaluate the results. METHODS BACKGROUND OF THE MINE The iron ore mine under study covers an area of 62 Ha and had a production capacity of 6 Mt per year. The

mine was working in two shifts of 8 h each. The ore was transported from the pit to the dumping point using dumpers of 30 tons capacity. The distance between the loading and unloading point

was about 1 km. The dumper operators were performing an average of 11 to 12 cycles per shift (one cycle comprises loaded truck traveling from the loading point to the unloading point and

empty truck back to the loading point). DATA COLLECTION The case study mine employed 262 dumper operators. The inclusion criteria for selecting the study sample were that the operators must

have a minimum age of 18 and a maximum age of 56. In addition, operators must have a minimum of 6 months of professional driving experience. Similarly, operators with a history of injuries

were excluded. In total, 248 dumper operators were selected, and the information about their characteristics (i.e., personal, habitual, and work-related) was collected using developed

questionnaire. Similarly, the WRMSDs data of the study sample were collected using a standardized Nordic questionnaire27. Table 2 gives the consolidated values of the collected data. DATA

ANALYSIS The data collected from the self-reported questionnaire was analyzed by SEM, which was coded using semopy python library28. The parameter estimate of the SEM model was determined by

the least square estimation method. The goodness of fit of the SEM model was assessed by seven types of fit indices: chi-square statistic, GFI, AGFI, NFI, TLI, CFI, and RMSEA. The SEM is

considered to have an acceptable fit if the chi-square statistic is not significant (i.e., _p_ > 0.05), the RMSEA is less than 0.08, and othet fit indices such as GFI, AGFI, NFI, TLI, and

CFI are greater than 0.9023. ETHICAL CONSIDERATIONS Ethical approval for this study was obtained from the institutional review board (committee set by National Institute of Technology

Karnataka). All methods were performed in accordance with the relevant guidelines and regulations set by the institutional review board. The participants were informed about this study, and

consent was obtained from them. Confidentiality of the participant’s personal and medical information was ensured. DATA AVAILABILITY The data sets analysed during this study are not publicly

available due to specific terms and conditions set by the mine under study. However, upon reasonable request, the data can be made available from the corresponding author in

normalized/standardized form. REFERENCES * McGuffin-Cawley, J. D. _Trends in Raw Materials Usage in the Production of Infrastructure and Engineering Devices. Encyclopedia of Sustainable

Technologies_ Vol. 2 (Elsevier, 2017). Google Scholar  * Jeripotula, S. K., Mangalpady, A. & Mandela, G. R. Ergonomic assessment of musculoskeletal disorders among surface mine workers

in India. _Min. Metall. Explor._ 38, 1041–1046 (2021). Google Scholar  * Eger, T. R. _et al._ Why vehicle design matters: Exploring the link between line-of-sight, driving posture and risk

factors for injury. _Work_ 35, 27–37 (2010). Article  CAS  PubMed  Google Scholar  * Grenier, S. G., Eger, T. R. & Dickey, J. P. Predicting discomfort scores reported by LHD operators

using whole-body vibration exposure values and musculoskeletal pain scores. _Work_ 35, 49–62 (2010). Article  CAS  PubMed  Google Scholar  * Upadhyay, R., Jaiswal, V., Bhattacherjee, A.

& Patra, A. K. Role of whole-body vibration exposure and posture of dumper operators in musculoskeletal disorders: A case study in metalliferous mines. _Int. J. Occup. Saf. Ergon._ 28,

1711–1721 (2022). Article  CAS  PubMed  Google Scholar  * Murtoja Shaikh, A., Bhusan Mandal, B. & Mangani Mangalavalli, S. Causative and risk factors of musculoskeletal disorders among

mine workers: A systematic review and meta-analysis. _Saf. Sci._ 155, 105868 (2022). Article  Google Scholar  * Murtoja, A., Bhusan, B. & Mangalavalli, S. M. Causative and risk factors

of musculoskeletal disorders among mine workers: A systematic review and meta-analysis. _Saf. Sci._ 155, 105868 (2022). Article  Google Scholar  * Bhattacharya, A. Costs of occupational

musculoskeletal disorders (MSDs) in the United States. _Int. J. Ind. Ergon._ 44, 448–454 (2014). Article  Google Scholar  * Ribeiro, T., Serranheira, F. & Loureiro, H. Work related

musculoskeletal disorders in primary health care nurses. _Appl. Nurs. Res._ 33, 72–77 (2017). Article  PubMed  Google Scholar  * Krishna, O. B., Maiti, J., Ray, P. K. & Mandal, S.

Assessment of risk of musculoskeletal disorders among crane operators in a steel plant: A data mining-based analysis. _Hum. Factors Ergon. Manuf._ 25, 559–572 (2015). Article  Google Scholar

  * Mandal, B. B. & Srivastava, A. K. Musculoskeletal disorders in dumper operators exposed to whole body vibration at Indian mines. _Recl. Env._ 24, 233–243 (2010). Google Scholar  *

Soares, C. O. _et al._ Preventive factors against work-related musculoskeletal disorders: Narrative review. _Revista Brasileira de Medicina do Trabalho_ 17, 415–430 (2019). Article  PubMed 

Google Scholar  * Ziaei, M., Choobineh, A., Abdoli-Eramaki, M. & Ghaem, H. Individual, physical, and organizational risk factors for musculoskeletal disorders among municipality solid

waste collectors in Shiraz. _Iran. Ind. Health_ 56, 308–319 (2018). Article  PubMed  Google Scholar  * Wolfgang, R. & Burgess-Limerick, R. Whole-body vibration exposure of haul truck

drivers at a surface coal mine. _Appl. Ergon._ 45, 1700–1704 (2014). Article  PubMed  Google Scholar  * Bhattacherjee, A., Kunar, B. M., Baumann, M. & Chau, N. The role of occupational

activities and work environment in occupational injury and interplay of personal factors in various age groups among Indian and French coalminers. _Int. J. Occup. Med. Environ. Health_ 26,

910–929 (2013). Article  PubMed  Google Scholar  * Chau, N., Bhattacherjee, A. & Kunar, B. M. Relationship between job, lifestyle, age and occupational injuries. _Occup. Med._ 59,

114–119 (2009). Article  CAS  Google Scholar  * Kunar, B. M., Bhattacherjee, A., Samanta, B. & Mitra, A. Relationship of individual and work related factors with obstructive type lung

function disorder of underground coal miners: A spirometry study. _J. Geol. Min. Res._ 6, 57–63 (2014). Google Scholar  * Njaka, S., Mohd Yusoff, D., Anua, S. M., Kueh, Y. C. & Edeogu,

C. O. Musculoskeletal disorders (MSDs) and their associated factors among quarry workers in Nigeria: A cross-sectional study. _Heliyon_ 7, e06130 (2021). Article  PubMed  PubMed Central 

Google Scholar  * Macdonald, K. & King, D. Work-related musculoskeletal disorders in veterinary echocardiographers: A cross-sectional study on prevalence and risk factors. _J. Vet.

Cardiol._ 16, 27–37 (2014). Article  PubMed  Google Scholar  * Yizengaw, M. A., Mustofa, S. Y., Ashagrie, H. E. & Zeleke, T. G. Prevalence and factors associated with work-related

musculoskeletal disorder among health care providers working in the operation room. _Ann. Med. Surg._ 72, 102989 (2021). Article  Google Scholar  * Tripathy, J. P., Thakur, J. S., Jeet, G.

& Jain, S. Structural equation modeling to identify the risk factors of diabetes in the adult population of North India. _Trop. Med. Health_ 46, 1–10 (2018). Article  Google Scholar  *

Christ, S. L., Lee, D. J., Lam, B. L. & Zheng, D. D. Structural equation modeling: A framework for ocular and other medical sciences research. _Physiol. Behav._ 176, 139–148 (2017).

Google Scholar  * Yildiz, E. & Ülker-Demirel, E. Measuring the effects of brand authenticity dimensions on word-of-mouth marketing via brand image using structural equation modeling.

_Int. J. Bus. Soc. Sci._ 8, 121–130 (2017). Google Scholar  * He, Z., Wu, Q., Wen, L. & Fu, G. A process mining approach to improve emergency rescue processes of fatal gas explosion

accidents in Chinese coal mines. _Saf. Sci._ 111, 154–166 (2019). Article  Google Scholar  * Sharma, R. & Singh, R. Work-related musculoskeletal disorders, job stressors and gender

responses in foundry industry. _Int. J. Occup. Saf. Ergon._ 20, 363–373 (2014). Article  PubMed  Google Scholar  * Kunar, B. M., Bhattacherjee, A. & Chau, N. Relationships of job

hazards, lack of knowledge, alcohol use, health status and risk taking behavior to work injury of coal miners: A case-control study in India. _J. Occup. Health_ 50, 236–244 (2008). Article 

PubMed  Google Scholar  * Kuorinka, I. _et al._ Standardised Nordic questionnaires for the analysis of musculoskeletal symptoms. _Appl. Ergon._ 18, 233–237 (1987). Article  CAS  PubMed 

Google Scholar  * Meshcheryakov, G., Igolkina, A. A. & Samsonova, M. G. _semopy 2: A Structural Equation Modeling Package with Random Effects in Python_ (2021). Download references

ACKNOWLEDGEMENTS The authors wish to acknowledge the support of the Science and Engineering Research Board, India (Grant number: CRG/2019/001940), for funding the research work. AUTHOR

INFORMATION AUTHORS AND AFFILIATIONS * Department of Mining Engineering, NITK Surathkal, Mangaluru, India Mohith Bekal Kar, Mangalpady Aruna & Bijay Mihir Kunar Authors * Mohith Bekal

Kar View author publications You can also search for this author inPubMed Google Scholar * Mangalpady Aruna View author publications You can also search for this author inPubMed Google

Scholar * Bijay Mihir Kunar View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS All authors have equally contributed for this study.

CORRESPONDING AUTHOR Correspondence to Mohith Bekal Kar. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER'S NOTE

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. RIGHTS AND PERMISSIONS OPEN ACCESS This article is licensed under a

Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit

to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are

included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons

licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of

this licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Kar, M.B., Aruna, M. & Kunar, B.M. Structural equation

modelling of work related musculoskeletal disorders among dumper operators. _Sci Rep_ 13, 14055 (2023). https://doi.org/10.1038/s41598-023-40507-9 Download citation * Received: 02 June 2023

* Accepted: 11 August 2023 * Published: 28 August 2023 * DOI: https://doi.org/10.1038/s41598-023-40507-9 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this

content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative