Prevention Of Work-related Musculoskeletal Disorders Research Articles

The Role of Physical Exercise in the Prevention of Musculoskeletal Disorders in Manual Workers: A Systematic Review and Meta-Analysis.

Work-related musculoskeletal disorders (WMSDs) are the most common occupational health problem in the European Union. Physical exercise interventions have been investigated in the prevention of WMSDs in many sectors. Therefore, our aim was to assess the effect of physical exercise in manual workers for the primary and secondary prevention of WMSDs. We conducted a systematic search of the literature and papers were included if: the participants were adult employees exclusively engaged in manual labor tasks; non-acute physical exercise intervention; pain, disability, physical functioning, or health-related quality of life outcome, with pre-post intervention measurements. We retrieved 10419 unique records and included 23 studies. A random effect meta-analysis was conducted on the studies with a control group design, using a three level model to estimate the pooled effect for pain outcomes (g = 0.4339, 95% CI : 0.1267 - 0.7412, p < 0.01), and a two-level model for disability outcomes (g = 0.6279, 95% CI : 0.3983 - 0.8575, p < 0.0001). Subset analysis revealed a moderate-to-large effect on the VAS outcome (g = 0.5866, 95% CI: 0.3102 - 0.8630, p < 0.0001). Meta-regression on pain outcomes revealed a significant effect for sex, age, study quality, and body segments tested. The analyses on all outcomes except VAS showed substantial heterogeneity (I2pain = 93%, of which 72% at the study level, I2disability = 78%, and I2vas = 56%, of which 44% at the study level). Physical exercise programs seem to have a positive effect on pain and disability stemming from WRMSDs in manual workers.

Spatial relationship-aware rapid entire body fuzzy assessment method for prevention of work-related musculoskeletal disorders

In the advent of Industry 5.0, advances in human-centered smart manufacturing (HSM) accentuate the role of humans in human–machine collaboration. This development has catapulted human health in human-machine systems to the forefront of the conversation. Although various tools have emerged to mitigate work-related musculoskeletal disorders (WMSDs), combining biomechanics with human morphology, the extant methods primarily hinge on expert scoring. Such methods display a step-wise change between risk levels, yielding inadequate assessment accuracy and posing challenges to human health assurance in HSM. To address these issues, this study proposes a spatial relationship-aware rapid entire body fuzzy assessment technique. The proposed method enhances the rapid entire body assessment (REBA) by enacting a dynamic evaluation of WMSD-related risk via a deep learning-based 3D pose reconstruction. Contrary to the step-wise transitions between REBA's different risk levels, the proposed method actualizes a fuzzy assessment of WMSD risk by introducing weights between these levels. This innovation allows for a more accurate risk assessment for workers engaged in HSM. Validation through experiments conducted on data from an automobile production line demonstrates that the proposed method can achieve a precision rate of 99.31%. Demo videos and code are available at https://github.com/giim-hf-lab/REBA-PLUS.

Enhancing Understanding Among Intensive Care Unit Nurses of Lower Back Musculoskeletal Disorders and Associated Risks

Work-related musculoskeletal disorders (WMSDs) have received the most attention worldwide of the various diseases addressed by the field of occupational medicine. In intensive care units (ICUs), patients with critical illness typically rely heavily on assistance provided by nurses to engage in daily life and rehabilitation activities. This dependence increases the risk of nurses experiencing WMSDs. An injury screening revealed that 56.4% of the nurses working in the ICU of the case hospital faced a mild risk of lower back musculoskeletal disorders and that the main contributor to this risk was lack of understanding among these nurses of lower-back-related WMSDs. This project was designed to enhance understanding of lower back WMSDs among the ICU nurses and to reduce the percentage of nurses facing a mild risk of contracting WMSDs. 1. Organize integrated courses to introduce human-induced hazards and enhance nurses' understanding and prevention of WMSDs. 2. Design slogans, posters, and teaching videos to promote awareness of patient turning tips and procedures to prevent nurses from experiencing WMSDs due to incorrect force application. 3. Design illustrations highlighting risky postures commonly performed by nurses in ICUs that may cause lower back WMSDs to prevent the occurrence of human-induced injuries. The rate of correct understanding of lower back WMSDs in the target nurse population improved from 73.8% to 96.2%. In addition, the percentage of those assessed with a mild risk of contracting lower back musculoskeletal injuries decreased from 56.4% to 25.5%. This project promoted multifaceted improvement measures based on the WMSD screening and risk classification and management processes stipulated by Taiwan's Ministry of Labor to increase understanding of lower back WMSDs among ICU nurses and reduce the percentage of those facing a mild risk of contracting WMSDs.

텍스트마이닝을 활용한 작업관련성 근골격계질환 연구동향 분석

Domestic musculoskeletal disorders have been on the rise since the 1997 IMF crisis, as rapid changes in work environments and practices, such as mechanization, automation, and specialization, have been implemented by various companies to enhance productivity. Furthermore, with increasing awareness and concern among workers, the government has been conducting harmful factor and causal investigations related to musculoskeletal disorders caused by burdensome work tasks through the amendment of Article 39 of the Industrial Safety and Health Act since 2003.&#x0D; To ensure the proactive prevention of work-related musculoskeletal disorders and minimize work-related losses for workers' safety, there is a continuous need for systematic and comprehensive research. To address this, the present study conducted a trend analysis to explore new perspectives for preventing industrial accidents.&#x0D; The literature collection comprised a total of 653 domestic theses and academic papers from 2003 to 2022 that included the keyword 'musculoskeletal disorder,' gathered through search engines such as the Korean Citation Index (KCI) and the Research Information Sharing Service (RISS). The data were categorized into two periods: the first period from 2003 to 2012 and the second period from 2013 to 2022. Text mining was employed to analyze the frequency (TF) and network connections, which were subsequently visualized.&#x0D; The results revealed that the top keywords in work-related musculoskeletal disorder research throughout the entire period (2003 to 2022) were 'work,' 'musculoskeletal disorder,' and 'symptoms,' consistently recognized as essential research topics in both the first and second periods. In the first period, the most frequently discussed factors influencing workers' musculoskeletal disorders included 'posture,' 'physical condition,' 'environment,' 'job stress,' 'time,' 'age,' and the term 'survey' was prominent among the top keywords. In the second period, keywords like 'comparison,' 'development,' 'measurement,' 'method,' and 'investigation' emerged.&#x0D; N-Gram analysis indicated that the simultaneous occurrence of the 'musculoskeletal disorder-symptoms' keyword was the most frequent throughout the entire period, followed by high frequencies of 'musculoskeletal disorder,' 'pain,' 'awareness,' and 'burden.' In the first period, body part-related keywords like 'hand-wrist,' 'shoulder-back,' 'wrist-finger,' and 'back-leg' were prominent. In the second period, keywords such as 'workload,' 'safety-health,' 'factor-investigation,' 'risk-assessment,' and 'occupational accident-insurance' were observed.&#x0D; This study attempted the first analysis of theses and academic papers on work-related musculoskeletal disorders using text mining, a big data analysis method. The findings contribute to suggesting future research directions on work-related musculoskeletal disorders, holding significant implications for the field.

Correlations between Ratings and Technical Measurements in Hand-Intensive Work.

An accurate rating of hand activity and force is essential in risk assessment and for the effective prevention of work-related musculoskeletal disorders. However, it is unclear whether the subjective ratings of workers and observers correlate to corresponding objective technical measures of exposure. Fifty-nine workers were video recorded while performing a hand-intensive work task at their workplace. Self-ratings of hand activity level (HAL) and force (Borg CR10) using the Hand Activity Threshold Limit Value® were assessed. Four ergonomist observers, in two pairs, also rated the hand activity and force level for each worker from video recordings. Wrist angular velocity was measured using inertial movement units. Muscle activity in the forearm muscles flexor carpi radialis (FCR) and extensor carpi radialis (ECR) was measured with electromyography root mean square values (RMS) and normalized to maximal voluntary electrical activation (MVE). Kendall's tau-b correlations were statistically significant between self-rated hand activity and wrist angular velocity at the 10th, 50th, and 90th percentiles (0.26, 0.31, and 0.23) and for the ratings of observers (0.32, 0.41, and 0.34). Significant correlations for force measures were found only for observer-ratings in five of eight measures (FCR 50th percentile 0.29, time > 10%MVE 0.43, time > 30%MVE 0.44, time < 5% -0.47) and ECR (time > 30%MVE 0.26). The higher magnitude of correlation for observer-ratings suggests that they may be preferred to the self-ratings of workers. When possible, objective technical measures of wrist angular velocity and muscle activity should be preferred to subjective ratings when assessing risks of work-related musculoskeletal disorders.

Wearable Motion Capture Devices for the Prevention of Work-Related Musculoskeletal Disorders in Ergonomics-An Overview of Current Applications, Challenges, and Future Opportunities.

Work-related musculoskeletal disorders (WMSDs) are a major contributor to disability worldwide and substantial societal costs. The use of wearable motion capture instruments has a role in preventing WMSDs by contributing to improvements in exposure and risk assessment and potentially improved effectiveness in work technique training. Given the versatile potential for wearables, this article aims to provide an overview of their application related to the prevention of WMSDs of the trunk and upper limbs and discusses challenges for the technology to support prevention measures and future opportunities, including future research needs. The relevant literature was identified from a screening of recent systematic literature reviews and overviews, and more recent studies were identified by a literature search using the Web of Science platform. Wearable technology enables continuous measurements of multiple body segments of superior accuracy and precision compared to observational tools. The technology also enables real-time visualization of exposures, automatic analyses, and real-time feedback to the user. While miniaturization and improved usability and wearability can expand the use also to more occupational settings and increase use among occupational safety and health practitioners, several fundamental challenges remain to be resolved. The future opportunities of increased usage of wearable motion capture devices for the prevention of work-related musculoskeletal disorders may require more international collaborations for creating common standards for measurements, analyses, and exposure metrics, which can be related to epidemiologically based risk categories for work-related musculoskeletal disorders.

Growing pains: strategies for improving ergonomics in minimally invasive gynecologic surgery.

To evaluate factors contributing to the development of work-related musculoskeletal disorders (WMSDs) and review strategies for mitigating ergonomic strain in minimally invasive gynecologic surgery. Factors associated with increased ergonomic strain and the development of WMSDs include increasing patient body mass index (BMI), smaller surgeon hand size, noninclusive design of instruments and energy devices and improper positioning of surgical equipment. Each type of minimally invasive surgery (laparoscopic, robotic, vaginal) confers its own ergonomic risk to the surgeon. Recommendations have been published regarding optimal ergonomic surgeon and equipment positioning. Intraoperative breaks and stretching are effective in reducing surgeon discomfort. Formal training in ergonomics has not yet been widely implemented, but educational interventions have been effective in reducing surgeon discomfort and can improve surgeon recognition of suboptimal ergonomics. Considering the serious downstream effects of WMSDs on surgeons, it is imperative to implement strategies for WMSD prevention. Optimal positioning of the surgeons and operative equipment should be routine. Intraoperative breaks and stretching should be incorporated during procedures and between every case. Formal education in ergonomics should be provided to surgeons and trainees. Additionally, more inclusive instrument design by industry partners should be prioritized.

Prevention of work-related musculoskeletal disorders in vaginal surgery

Prevention of work-related musculoskeletal disorders in vaginal surgery

AIUM Practice Principles for Work-Related Musculoskeletal Disorder.

The prevalence of Work-Related Musculoskeletal Disorders (WRMSDs) among ultrasound professionals has been significant. National and international efforts to create industry standards have focused primarily on injuries in sonographers. In addition, the Centers for Disease Control and Prevention (CDC) and National Institute for Occupational Safety & Health (NIOSH) have published documents related to this occupational exposure. There has also been significant attention on equipment utilization and design to help reduce the prevalence of WRMSDs. The American Institute of Ultrasound in Medicine (AIUM) developed the AIUM Practice Principles for Work-Related Musculoskeletal Disorder in collaboration with other organizations whose members use ultrasound [see Collaborating Societies and Representatives]. This document supports the "Industry Standards for the Prevention of Work-Related Musculoskeletal Disorders in Sonography" and aims to expand on these Standards to include safety practices for all health care professionals who utilize ultrasound. These professionals include members of the scientific community, a wide variety of medical professionals, and dental professionals. These ultrasound users and operators will collectively be referred to in this document as "operator(s)" except in those instances where data addressed those holding a specific job title, such as sonographer. In addition, this document will support guidance for quality improvement specific to preventing and reducing injury rates.

An ergonomic role allocation framework for dynamic human–robot collaborative tasks

By incorporating ergonomics principles into the task allocation processes, human–robot collaboration (HRC) frameworks can favour the prevention of work-related musculoskeletal disorders (WMSDs). In this context, existing offline methodologies do not account for the variability of human actions and states; therefore, planning and dynamically assigning roles in human–robot teams remains an unaddressed challenge. This study aims to create an ergonomic role allocation framework that optimises the HRC, taking into account task features and human state measurements. The presented framework consists of two main modules: the first provides the HRC task model, exploiting AND/OR Graphs (AOG)s, which we adapted to solve the allocation problem; the second module describes the ergonomic risk assessment during task execution through a risk indicator and updates the AOG-related variables to influence future task allocation. The proposed framework can be combined with any time-varying ergonomic risk indicator that evaluates human cognitive and physical burden. In this work, we tested our framework in an assembly scenario, introducing a risk index named Kinematic Wear. The overall framework has been tested with a multi-subject experiment. The task allocation results and subjective evaluations, measured with questionnaires, show that high-risk actions are correctly recognised and not assigned to humans, reducing fatigue and frustration in collaborative tasks.

Assessment of muscular effort in workers performing an actual use-case manual material handling with and without the assistance of Bazar robot

Human-robot collaboration (HRC) technologies represent one of the new options for the prevention of work-related musculoskeletal disorders (WMDs) [1]. They share the workspace with workers, communicate with them, manage the human partner's flexibility and ergonomics and physically interact, to work together on a common goal [2]. Reconfigurable collaborative robots adapt to the workers’ intentions and task variations, offloading them from external loads and keeping them in ergonomic working circumstances, to improve efficiency and quality of the task execution. Although collaborative work is a widespread method for lowering WMDs, the scientific literature reveals conflicting findings on its efficacy, due to factors affecting team capacity, autonomy, and coordination [3]. To measure motor coordination during dynamic tasks, a common approach is muscle co-activation that may become functionally unfavorable [3]. Therefore, this research aims to evaluate upper limb muscle coactivation in workers performing an industrial use case manual material handling (MMH), proposed by the SME HANKAMP (Netherlands), by comparing a scenario where the workers are helped by a dual-arm cobot BAZAR [4] with a scenario without BAZAR. This cobot BAZAR is being assessed in the context of SOPHIA (http://www.project-sophia.eu) project funded by the European Union's Horizon 2020 (grant agreement No. 871237). Eleven participants (5 females and 6 males; 27.73±5.99 years; BMI: 23.06±3.93 kg/m2) took part in the study carried out at the University of Montpellier in accordance with the Helsinki Declaration and authorized by the local Ethics Committee (Protocol number IRB-EM 2103 A). Six bipolar electrodes (FreeEMG300 System, BTS, Italy, sampling rate at 1000 Hz) were placed on the flexors and extensors muscles of the shoulder (anterior and posterior deltoideus), elbow (biceps and triceps brachii caput longum) and wrist (flexor and extensor carpi radialis) joints. The experiments are shown in the video available at: https://youtu.be/vul8iLO0Sdw. Each participant, after being instructed, completed the task 3 times without BAZAR and 3 with BAZAR (Fig. 1A). We randomly ordered the 2 conditions for each subject. Furthermore, participants performed a specific exercise twice with each muscle to obtain the isometric maximal voluntary contractions (iMVCs) [1,3]. The envelope of each task's sEMG signals was then extracted, normalized respect to the average iMVC peak value for each muscle, time-normalized and then interpolated according to the brushing sub-task duration (400 samples) [1,3]. The time-varying multi-muscle coactivation function (TMCf) [3] was estimated and the mean and maximum values were calculated. A statistical analysis was carried out to evaluate whether the help of the cobot had determined significant changes in each parameter (p-value < 0.05). Fig. 1 B show the mean and standard deviation of mean and max values of evaluated TMCf for each condition. Significant effects of the presence of BAZAR were found for mean (p<0.001) and max (p=0.001) values of TMCf: the values significantly decrease when the task is performed with BAZAR. The results showed a reduction in upper limb co-activation when the specific MMH was performed with BAZAR. Therefore, HRC technologies, which share the workspace with workers, not only offload workers from external loads and improve the task execution efficiency and quality. They also allow a better coordination and reduce the worker’s physical effort while she/he physically interacts with the robot, and positively affect her/his physiological motor strategy.

Prevention of work-related musculoskeletal disorders among dental professionals: A scoping review.

Musculoskeletal disorders (MSDs) are highly prevalent among dental professionals. Studies examining the prevention of dental work-related MSDs have been completed globally. To identify and evaluate the available evidence regarding MSD prevention in dental practice, and to identify knowledge gaps. An electronic search was conducted across multiple scientific databases. Identified articles were assessed according to inclusion/exclusion criteria. Systematic reviews, interventions and published expert opinion were included. Five systematic reviews, 27 intervention studies and 21 expert opinion articles were included. There was overall consensus that use of ergonomic equipment is beneficial in MSD prevention. Knowledge gaps identified included: a lack of systematic reviews on the role of exercise and therapy in MSD prevention; a lack of high-quality intervention studies; a lack of research in general outside of ergonomic equipment use. Expert opinion articles have been published despite a lack of dental-specific research. Whilst more robust research is required, current evidence supports the use of optical enhancement, saddle chairs, and wide-diameter silicone-handled instruments in MSD prevention. Knowledge gaps were identified relating to the use of exercise, therapy, and education in the prevention of dental work-related MSDs. Further research is required, specifically high-quality intervention studies.

Prevalence, Risk Factors and Prevention of Work-Related Musculoskeletal Disorders in Physiotherapist According to Their Specialization - A Review

Physiotherapists are primary health care Professionals who diagnose and treat individuals of all ages, who have medical problems or other health related conditions, illness, or injuries that limit their abilities to move and perform the daily functional activities. Our aim was to review current knowledge associated with prevalence, risk factors, and preventive strategies of WMSDs among physiotherapist according to their work of specialization. Google Scholar and PubMed were searched for terms relating to WMSDs in Physiotherapists of various specialization from inception to 2021. There are many physiotherapy specialties including musculoskeletal, pediatrics, neurological, cardio- respiratory, rehabilitation, sports etc. and each one has its unique purpose and set of responsibilities. According to area of specialization in physiotherapy, occurrence of WMSDs at different body part may vary. Therefore, implementing preventive strategies for WMSDs in early stage of a physiotherapist’s career according specialization is needed. Keywords: work-related musculoskeletal disorders, Physiotherapy, Neuro-physiotherapy, Cardiopulmonary Physiotherapy, Musculoskeletal Physiotherapy, Sports Physiotherapy.

Detection of Physical Strain and Fatigue in Industrial Environments Using Visual and Non-Visual Low-Cost Sensors

The detection and prevention of workers’ body straining postures and other stressing conditions within the work environment, supports establishing occupational safety and promoting well being and sustainability at work. Developed methods towards this aim typically rely on combining highly ergonomic workplaces and expensive monitoring mechanisms including wearable devices. In this work, we demonstrate how the input from low-cost sensors, specifically, passive camera sensors installed in a real manufacturing workplace, and smartwatches used by the workers can provide useful feedback on the workers’ conditions and can yield key indicators for the prevention of work-related musculo-skeletal disorders (WMSD) and physical fatigue. To this end, we study the ability to assess the risk for physical strain of workers online during work activities based on the classification of ergonomically sub-optimal working postures using visual information, the correlation and fusion of these estimations with synchronous worker heart rate data, as well as the prediction of near-future heart rate using deep learning-based techniques. Moreover, a new multi-modal dataset of video and heart rate data captured in a real manufacturing workplace during car door assembly activities is introduced. The experimental results show the efficiency of the proposed approach that exceeds 70% of classification rate based on the F1 score measure using a set of over 300 annotated video clips of real line workers during work activities. In addition a time lagging correlation between the estimated ergonomic risks for physical strain and high heart rate was assessed using a larger dataset of synchronous visual and heart rate data sequences. The statistical analysis revealed that imposing increased strain to body parts will results in an increase to the heart rate after 100–120 s. This finding is used to improve the short term forecasting of worker’s cardiovascular activity for the next 10 to 30 s by fusing the heart rate data with the estimated ergonomic risks for physical strain and ultimately to train better predictive models for worker fatigue.

Work-related musculoskeletal disorders among dentists and their prevention through ergonomic interventions - A systematic review

Introduction: Occupational or work-related musculoskeletal disorders (WMSDs) vastly prevail among dental professionals. This review aims to summarize the prevalence of WMSDs and ergonomic interventions for the prevention of WMSDs among dental professionals. Methods: Thorough literature search was done using scientific databases. The terms musculoskeletal disorders among dentists/dental professionals/ hygienists in dentistry, occupational or work-related MSDs, ergonomics, ergonomic interventions, and several combinations were used as keywords. Articles published in the English language only were included. Abstracts, thesis work, and other languages were excluded. Results: Our findings revealed several studies showing the prevalence of WMSD among dental professionals and ergonomic interventions to prevent MSDs among dental professionals. Conclusion: Dental practice is highly challenging both in terms of physical and mental status there is a need for continuing efforts to discover innovative preventive strategies, to reduce the prevalence of WMSDs. This article guides dental professionals to incorporate the proper ergonomic methods in their early stages of day-to-day work, for long-term and healthy dental practice.

Deep learning-based networks for automated recognition and classification of awkward working postures in construction using wearable insole sensor data

Among the numerous work-related risk factors, construction workers are often exposed to awkward working postures that may lead them to develop work-related musculoskeletal disorders (WMSDs). To mitigate WMSDs among construction workers, awkward working posture recognition is the first step in proactive WMSD prevention. Several researchers have proposed wearable sensor-based systems and machine learning classifiers for awkward posture recognition. However, these wearable sensor-based systems (e.g., surface electromyography) are either intrusive or require attaching multiple sensors on workers' bodies, which may lead to workers' discomfort and systemic instability, thus, limiting their application on construction sites. In addition, machine learning classifiers are limited to human-specific shallow features which influence model performance. To address these limitations, this study proposes a novel approach by using wearable insole pressure system and recurrent neural network (RNN) models, which automate feature extraction and are widely used for sequential data classification. Therefore, the research objective is to automatically recognize and classify different types of awkward working postures in construction by using deep learning-based networks and wearable insole sensor data. The classification performance of three RNN-based deep learning models, namely: (1) long-short term memory (LSTM), (2) bidirectional LSTM (Bi-LSTM), and (3) gated recurrent units (GRU), was evaluated using plantar pressure data captured by a wearable insole system from workers on construction sites. The experimental results show that GRU model outperforms the other RNN-based deep learning models with a high accuracy of 99.01% and F1-score between 93.19% and 99.39%. These results demonstrate that GRU models can be employed to learn sequential plantar pressure patterns captured by a wearable insole system to recognize and classify different types of awkward working postures. The findings of this study contribute to wearable sensor-based posture-related recognition and classification, thus, enhancing construction workers' health and safety.

A qualitative exploration of tools used by WHS professionals for the prevention of musculoskeletal disorders

Although a number of prevention tools are available to support work health and safety (WHS) professionals in the prevention of work-related musculoskeletal disorders (WMSDs), little is known about their use in real world settings. The aim of the current study was to explore the use of tools designed for the identification and management of physical and psychosocial hazards, in a range of high-risk industry sectors, to support prevention of WMSDs. A qualitative study design was used. Twenty-nine WHS professionals from high WMSD risk industry sectors were interviewed and presented with a list of 33 tools (identified through a literature review) designed to support risk management of WMSDs, to determine, 1) their level of awareness and use of tools 2) how tools were being used, and 3) barriers to implementation of these tools. Most participants reported awareness of some of the 33 tools presented however, only 30% of participants were currently using at least one of these toolsin their practice. Reported barriers to the use of tools included a lack of tool awareness and availability, practical deficits in the tools, and organisational challenges to implementation. WMSDs are complex disorders which require identification and control of relevant physical and psychosocial workplace hazards, yet findings from the current study suggest that tools are not being effectively used to support mitigation of workplace physical and psychosocial hazards. Improved availability of practical tools and implementation guidance is required to support effective implementation of tools for workplace risk management of WMSDs. Future research into the development and implementation of new evidence-based tools would assist in improving the effectiveness of WMSD risk management in high-risk industry sector workplaces.

Prevention of Work-Related Musculoskeletal Disorders in Physiotherapist - A Review

Introduction: Healthcare professionals, especially those with in direct contact of patient are at the highest risk of work-related musculoskeletal disorders (WMSDs), physical therapists being one of them. Aim: Our aim was to review current knowledge relating to the prevalence, risk factors and prevention of WMSDs among physiotherapists. Method: Google Scholar and PubMed were searched for terms relating to WMSDs in Physiotherapists from inception to 2021. Result: The prevalence of WMSDs among Physiotherapist was high, with the lower back as the most frequently affected followed most often by the neck, upper back and shoulders. Knee joint was commonly affected joint in lower extremity. Multiple risk factors have been found responsible for WMSDs in physiotherapist and some of them are modifiable. Conclusion: Implementing preventive strategies for WMSDs in early stage of a physiotherapist’s career may help to prevent WMSDs and maintain good quality of treatment for patients. Keywords: Pain, Musculoskeletal disorders, Ergonomics, Prevention.

A primary care team approach to secondary prevention of work-related musculoskeletal disorders: Physical therapy perspectives

BACKGROUND: Among work-related conditions in the United States, musculoskeletal disorders (MSDs) account for about thirty-four percent of work absences. Primary care physicians (PCPs) play an essential role in the management of work-related MSDs. For conditions diagnosed as work-related, up to seventeen percent of cases are PCP managed; within these conditions, up to fifty-nine percent are diagnosed as musculoskeletal. Negative factors in treatment success confronting PCPs include time constraints and unfamiliarity with work-related MSDs. A multidimensional team approach to secondary prevention, where PCPs can leverage the expertise of allied health professionals, might provide a useful alternative to current PCP practices for the treatment of work-related MSDs. OBJECTIVE: Provide the structure of and rationale for an “extended care team” within primary care for the management of work-related MSDs. METHODS: A systematic literature search, combining medical subject headings and keywords, were used to examine eight peer-reviewed literature databases. Gray literature, such as government documents, were also used. RESULTS: An extended care team would likely consist of at least nine stakeholders within primary care. Among these stakeholders, advanced practice orthopedic physical therapists can offer particularly focused guidance to PCPs on the evaluation and treatment of work-related MSDs. CONCLUSIONS: A multidimensional approach has the potential to accelerate access and improve quality of work-related outcomes, while maintaining patient safety.

Musculoskeletal Disorders' Classification Proposal for Application in Occupational Medicine.

Occupational-specific classifications of musculoskeletal disorders (MSD) are scarce and do not answer specific clinical questions. Thus, a specific classification was developed and proposed, covering criteria applicable to daily clinical activity. It was considered that the disorder development process is the same across all work-related MSDs (WRMSDs). Concepts of clinical pathology were applied to the characteristics of WRMSDs pathophysiology, cellular and tissue alterations. Then, the correlation of the inflammatory mechanisms with the injury onset mode was graded into four levels (MSDs 0–3). Criteria of legal, occupational and internal medicine, semiology, physiology and orthopaedics, image medicine and diagnostics were applied. Next, the classification was analysed by experts, two occupational physicians, two physiatrists and occupational physicians and one orthopaedist. This approach will allow WRMSD prevention and improve therapeutic management, preventing injuries from becoming chronic and facilitating communication between occupational health physicians and the other specialities. The four levels tool relate aetiopathogenic, clinical, occupational and radiological concepts into a single classification. This allows for improving the ability to determine a WRMSD and understanding what preventive and therapeutic measures should be taken, avoiding chronicity. The developed tool is straightforward, easy to understand and suitable for WRMSDs, facilitating communication between occupational physicians and physicians from other specialities.