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Musculoskeletal Disorders and Workplace Factors: A Critical Review of Epidemiologic Evidence for Work-Related Musculoskeletal Disorders of the Neck, Upper Extremity, and Low Back

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... These findings align with previous research highlighting the importance of ergonomic interventions in reducing workrelated musculoskeletal disorders (MSDs). Bernard and Putz-Anderson (1997) and Soares et al. (2019) reported that tasks involving high repetition, excessive force, and nonergonomic postures significantly contribute to MSDs. The introduction of ergonomic tools, such as work tables, has been shown to significantly decrease the occurrence of MSD complaints by reducing repetitive strain and maintaining better body posture during work activities. ...
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UD Harapan Kita, a well-established furniture manufacturing company with a strong presence in both domestic and international markets, faces significant challenges in its packaging processes due to the large dimensions and heavy weight of its products, which increase the risk of damage during shipment. Current observations reveal inefficiencies in the packaging process, primarily attributed to nonergonomic working conditions, leading to operator discomfort and musculoskeletal disorder (MSD) complaints. Operators frequently adopt awkward postures, such as bending and stretching for extended periods, resulting in physical discomfort and a high incidence of MSDs. These conditions not only compromise the health and well-being of the operators but also reduce productivity and increase the likelihood of errors, ultimately affecting the quality of the packaged products. This study aims to address these issues by implementing ergonomically designed packing tables tailored to the operator’s anthropometric data. The study also evaluates the impact of this intervention on key performance metrics, including operator exposure levels, processing time, and the frequency and severity of MSD complaints. The packing tables were developed based on the anthropometric measurements of packing operators at the partner company. Exposure levels were assessed using the Quick Exposure Check (QEC) method, while MSD complaints were measured through the GOTRAK questionnaire. The study results indicate a significant reduction in average postural scores by 22.80%, a decrease in average processing time by 16.81%, and a reduction in average MSD complaint scores by 22.73%. These findings demonstrate that ergonomic interventions can significantly reduce MSD risks and enhance overall efficiency in the packaging process.
... (7,8,9) Actualmente, se reconoce que el mecanismo de aparición de las lesiones musculoesqueléticas es de naturaleza biomecánica; cuatro teorías explican el mecanismo de aparición: la teoría de la interacción multivariante (factores genéticos, morfológicos, psicosociales y biomecánicos), la teoría diferencial de la fatiga (desequilibrio cinético y cinemático), la teoría cumulativa de la carga (repetición) y finalmente la teoría del esfuerzo excesivo (fuerza). (10,11,12,13) En todas estas teorías se identifica a los factores de riesgo biomecánicos como los factores que incrementar la probabilidad de desarrollar un trastorno musculoesquelético, ya sea por estar presente de manera desfavorable o debido a que haya presencia simultánea con otros factores de riesgo. (14) Además, estos factores parecen estar determinados por las exigencias de las tareas o actividades laborales, ya que los síntomas musculoesqueléticos son más frecuentes en los empleados expuestos a labores físicas que le exigen al trabajador mantener posturas biomecánicamente inadecuadas. ...
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Introduction: Musculoskeletal symptoms are frequent disorders in the different activities of people, the performance of work activities under ergonomic risk conditions can lead to occupational diseases, work absenteeism and economic losses. Objective: to determine which are the ergonomic risk factors in the performance of work activities and their relationship with musculoskeletal symptoms present in administrative assistants. Methods: The research was a cross-sectional study with a random sample of fifty administrative assistants who allowed their participation by signing the informed consent form. The instruments used were the Standardized Nordic Questionnaire and the Rose Method. Results: Hundred percent reported the presence of musculoskeletal symptoms, with the neck as the most affected region. Seventy percent of the participants presented a medium to very high level of ergonomic risk, with forced postures related to the use of the chair presenting the highest average. There was a significant relationship between the specific ergonomic risk generated using the telephone and the presence of musculoskeletal symptoms in the wrist and hand, as well as between the specific ergonomic risk generated by the armrest of the chair and the presence of symptoms in the dorsal spine, shoulders, arms, elbows and forearms. Conclusions: the evaluation of ergonomic risk factors is a tool that will allow the generation of specific proposals for prevention and intervention programs, so that the necessary changes can be made to reduce occupational injuries and diseases in work activities.
... Biomechanical constraints, particularly awkward postures, repetitive tasks and applied force, collectively elevate the risk of developing MSDs [4,5]. These factors do not impact the musculoskeletal system independently but interactively. ...
... Physical activities that required the body to perform awkward or repetitive tasks in performing heavy or demanding physical tasks have been well known as the main factor for the occurrence of musculoskeletal injuries such as musculoskeletal disorders (MSDs). Risks such as awkward posture, forceful exertions were few of the significant contributors to the deterioration of workers' health, where these risks can lead to impending MSDs, and other chronic conditions due to the strain it had placed on the human body (Bernard et al., 1997;Burgess-Limerick, 2012;Franco & Fusetti, 2004). Various physical risk factors exist at workplace specifically involved in manual material handling like frequent lifting, bending, and carrying and pushing. ...
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The lack of comprehensive tool to assess both risk factors of physical and psychosocial risk factors as well as fatigue level effectively is significant in similar field research. Therefore, the aim of this study is to do the initial development and content validation of a questionnaire that identifies and measures the risk factors influencing the fatigue levels. The questionnaire was developed by implementing adapt method followed by item generation in the initial development. This questionnaire was then validated in terms of content validation by expert panel reviews. The content validity result are as follows: 64-items out of 146-items scale had I-CVI below 1, 24-items with I-CVI of 0.8, and 3-items with 0.7, and the remaining items had I-CVI of below than 0.7. The average value of CVR was 1, and the S-CVI/Ave of the questionnaire was 0.93. Items with content validity of 0.7 and above was maintained, therefore, the questionnaire ended with 87-items. Findings indicates that the questionnaire is valid for assessing the physical and psychosocial risk factors associating with the fatigue levels. This questionnaire can be made a tool in assessing workplace while promote efficiency through this comprehensive tool, contributing to a better understanding of occupational risks and potential interventions.
... Musculoskeletal disorders (MSDs) refer to impairments of the body structures, such as muscles, joints, tendons, ligaments, and nerves, which are caused or aggravated primarily by the performance of work and by the effects of the immediate environment in which work is carried out [4]. Engaging in incorrect work practices can lead to harm among silk weaving workers. ...
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Praewa silk weavers are subject to numerous occupational risk factors that contribute to musculoskeletal disorders (MSDs), primarily due to inadequate occupational safety measures, limited access to health services, and substandard working conditions. The aim of this study was to assess the prevalence rate and determinants of MSDs among Praewa silk weavers. A cross-sectional study employing a convenience sampling method was conducted, involving 198 Praewa silk weavers from four provinces in the Northeast region of Thailand. Data collection was facilitated through a two-part research tool. The first part included a questionnaire assessment using demographic information, workplace conditions, and self-reported MSDs. The second part involved an ergonomic risk assessment using rapid upper limb assessment (RULA) and hand activity level (HAL). Descriptive statistics and multiple logistic regression analyses were conducted to determine the prevalence and associated factors of MSDs among participants. The findings revealed that the prevalence rates of MSDs in the past 7 days and 12 months were 68.68% and 96.46%, respectively. The highest prevalence rates of MSDs (over 60.00%) were observed in the wrists, fingers, and neck, with similar trends reported in both the past 7 days and 12 months. Key factors associated with MSDs over the past 12 months included low hand strength test results (adjusted odds ratio (AOR)=2.09; 95%CI: 0.44– 11.05), quite low hand strength test results (AOR=2.49; 95%CI: 0.29–21.15), weaving experience of 21–30 years (AOR=1.07; 95%CI: 0.20–5.64), age between 31–40 years (AOR=2.63; 95%CI: 0.49–13.91), age above 41 years (AOR=1.13; 95%CI: 1.08–1.19), RULA level 4 (AOR=3.62; 95%CI: 0.66–19.96), and HAL score exceeding 0.78 (AOR=0.63; 95%CI: 0.80–0.98) were significantly associated with MSDs during the past 12 months. This study highlights the high prevalence of MSDs among Praewa silk weavers, attributed to occupational risk factors such as low hand strength, high hand activity level, extensive weaving experience, and poor working posture. The weaving process itself is a significant contributor to these disorders. In conclusion, ergonomics interventions aimed at preventing MSDs, including postural training, injury prevention programs, and redesigned pull-cloth devices, are recommended to mitigate these risks.
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Musculoskeletal disorders are the most prevalent occupational health problem and are often related to biomechanical risk factors. Over the last forty years, observational methods for exposure assessment have been proposed. To apply them effectively in the field, an in-depth knowledge of each methodology and a solid understanding of their actual predictive value and limitations are required. In this two-part guide, we discuss methods that have a solid scientific background, are based on expert consensus, and that do not require disproportionate technical, material, financial, and time resources. In Part 1, we focused on the Revised NIOSH Lifting Equation as a validated method for assessing manual material handling and discussed its application when dealing with task variability. In Part 2, we look at methods for the assessment of upper-limb biomechanical exposure in manual jobs. According to the above-mentioned criteria, we discuss methodologies proposed by the American Conference of Governmental Industrial Hygienists (ACGIH) and evaluate activities requiring high-speed continuous movement and the use of hand force, working with the arms above the shoulder level, to prevent localized fatigue in the upper extremities in cyclical work tasks. Finally, a preliminary proposal of a proportionate risk assessment of working duration in part-time jobs is presented.
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The American Conference of Governmental Industrial Hygienists (ACGIH) Threshold Limit Values (TLVs) for lifting provides risk zones for assessing two-handed lifting tasks. This paper describes two computational models for identifying the lifting risk zones using gyroscope information from five inertial measurement units (IMUs) attached to the lifter. Two models were developed: (1) the ratio model using body segment length ratios of the forearm, upper arm, trunk, thigh, and calf segments, and (2) the ratio + length model using actual measurements of the body segments in the ratio model. The models were evaluated using data from 360 lifting trials performed by 10 subjects (5 males and 5 females) with an average age of 51.50 (±9.83) years. The accuracy of the two models was compared against data collected by a laboratory-based motion capture system as a function of 12 ACGIH lifting risk zones and 3 grouped risk zones (low, medium, and high). Results showed that only the ratio + length model provides acceptable estimates of lifting risk with an average of 69% accuracy level for predicting one of the 3 grouped zones and a higher rate of 92% for predicting the high lifting zone.
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Objective To identify lifting actions and count the number of lifts performed in videos based on robust class prediction and a streamlined process for reliable real-time monitoring of lifting tasks. Background Traditional methods for recognizing lifting actions often rely on deep learning classifiers applied to human motion data collected from wearable sensors. Despite their high performance, these methods can be difficult to implement on systems with limited hardware resources. Method The proposed method follows a five-stage process: (1) BlazePose, a real-time pose estimation model, detects key joints of the human body. (2) These joints are preprocessed by smoothing, centering, and scaling techniques. (3) Kinematic features are extracted from the preprocessed joints. (4) Video frames are classified as lifting or nonlifting using rank-altered kinematic feature pairs. (5) A lifting counting algorithm counts the number of lifts based on the class predictions. Results Nine rank-altered kinematic feature pairs are identified as key pairs. These pairs were used to construct an ensemble classifier, which achieved 0.89 or above in classification metrics, including accuracy, precision, recall, and F1 score. This classifier showed an accuracy of 0.90 in lifting counting and a latency of 0.06 ms, which is at least 12.5 times faster than baseline classifiers. Conclusion This study demonstrates that computer vision-based kinematic features could be adopted to effectively and efficiently recognize lifting actions. Application The proposed method could be deployed on various platforms, including mobile devices and embedded systems, to monitor lifting tasks in real-time for the proactive prevention of work-related low-back injuries.
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