Musculoskeletal Disorders: Definition, Causes, Control Methods

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Date publications:

August 24, 2020

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Musculoskeletal Disorders (MSDs) are, as the names suggests, any issues relating to the (musculo) muscular (skeletal) skeleton system of our bodies. There are many different types of muscles in the body but MSDs concern only what are called the skeletal or voluntary muscles as these are the ones that are connected to the bones. These muscles can be controlled at our will and are responsible for the movement of our body parts.

Disorders associated with the musculoskeletal system can include anything that not only affect the muscles but also the associated:

  • Nerves: fibres in the body that transmit information of pain or sensation to the brain.
  • Tendons: fibres that connect the muscle to the bone.
  • Ligaments: fibres that connect cartilages or bone to the bone, stabilises joints.
  • Spinal Discs: cushioning between each spinal vertebra for shock absorbing.
  • Blood Vessels: veins, arteries or capillaries that transport blood cells around the body.
  • Types of MSDs can be anything from a torn muscle, ligament or tendon, a damaged spinal disc, nerve damage, hernia, pain, numbness, swelling or any sprain or strain, connective tissue diseases or disorders such as arthritis.

According to the U.S. Bureau of Labour Statistics in 2018, MSD cases accounted for 30% of all worker injury and illness cases. ‘Work related’ MSDs (WRMDs) are conditions of which the work environment or work task contributes to the condition or is made worse due to work circumstances. The U.S. Bureau of Labour Statistics determine these as:

  • The event or exposure leading to the injury or illness is overexertion and bodily reaction
  • Unspecified; overexertion involving outside sources
  • Repetitive motion involving microtasks
  • Other and multiple exertions or bodily reactions
  • Rubbed, abraded, or jarred by vibration

Specific Risk factors associated with work related MSDs

MSDs have multiple risk factors and can be both occupational and non-occupational. Due to the multifactorial nature of any potential injuries, it is important to address all areas of possible causes or prevention strategies will contain gaps. Conducting comprehensive preventative approaches, assessing the health of the employee, their personal risk factors as well as their work-related risks is akin to achieving a longer, healthier working life cycles and guides interventions.

Individual Risks

According to a study conducted over a 12-year period published by the International Journal of Occupational and Environmental Health, there is evidence to suggest that in addition to work demands, there are individual risk factors that may contribute to WRMSDs, these include:

  • Current physical health (Underlying disease e.g. arthritis, gout, lupus and diabetes)
  • Mental health
  • Age
  • Gender
  • Stress
  • Fatigue
  • Psychosocial work factors: the interaction between design and management of work
  • Nutrition/hydration/fitness (weight, smoking, muscle strength, cardio fitness)
  • Rest/recovery cycles
  • Experience

Work Related Risks

Lifting or carrying loads, awkward static posture, frequent bending and twisting and repetition are proven to be the physical load risk factors consistently associated with work-related back disorders. Lifting and bending is said to account for 33% to 60% of all work-related Lower Back Pain (LBP). More specifically, lifting while twisting or bent sideways is a significant risk factor.

There are three primary risk factors:

  1. Repetition
  2. Long Awkward Static Posture
  3. Force: Bending, Twisting or Lifting


Continuous and repeated force exertions over a significant period of time may have impact and cause tissue changes which decrease stability and increase risk of injury.

Long Awkward Static Posture

When the body holds a single position for an extended period or shifts position but fails to allow the muscles to return to a neutral position, comfort and performance are impaired. Static postures increase the load on muscles and tendons compared to dynamic postures. These static positions may reduce blood flow to the muscles, thus preventing the body from engaging in the natural process of restoration and repair.

Force: Bending, Twisting or lifting

Bending, twisting or lifting loads when associated with poor technique, repetition or long awkward static posture can contribute to MSDs.

Lifting with poor technique can be described as making end range bending movements while performing working tasks. End range bending places muscles at full stretch, which leads to temporary muscle weakness or ‘deactivation’. As a result, the spine is not adequately protected by its muscles, potentially making it unstable.

The force of a movement – the movement may be defined as high risk when for different reasons, the person finds it physically difficult to perform. In many cases it contributes to working with the high load. If a large load is carried, even a small amount of bending can lead to injury, since fibers of the discs are much less tolerant to load at this position. Jerky and fast movements are also defined as high risk. Fast extension movements create a larger window during which the spine is exposed to instability and injury because of lack of muscle forces.

When looking at the work-related risk pertaining to force, it is easy to see that individual risk factors come into play. Any of the factors listed above such as fatigue, stress, current mental state or disease can dramatically lessen the weight that can be lifted safely, and this may also be measured as high risk. But how can this be controlled on a macro level?

Controlling Both Work-Related and Individual Risks

Creating controls that cover all aspects of both individual and work-related risk factors is not an easy feat however, technology these days by use of wearables and data, does offers an ability to be able to cover both factors, limiting exposure. Measuring and monitoring each individual’s musculoskeletal injury risk in the workplace is challenging, however due to recent advances in machine learning, workers can be materially advantaged by the use of objective data, vibro-tactile feedback and self-tracking. Machine learning algorithms allow filtering of the complex data noise and provide a clear picture to really see what’s going on. This gives the unique ability to predict movement patterns that impact long term physical health and allows intervention with personalized coaching.

If repetition, static posture and high force loads are the common work related risk factors that cause MSDs but these movements also may have underlying individual risk factors determining the likelihood of injury, then one dimensional strategies for control are not going to cover all bases nor create an broader opportunity for decreasing causes.

Traditional standards specify what ‘typical’ humans can withstand. In the case of ergonomics and manual handling risk, these usually estimate how many higher-risk movements a person should make within a time period and how much weight or force a person should be able to safely withstand. Manual handling standards are based on statistical averages and anthropometric data which do not withstand the immense population variety observed in today’s society. Providing an opportunity for the individual to self-monitor their own movement creates and encourages a level of comfort and safety to the employee, giving them autonomy.

With the ability to not only collect individual movement data but to analyze patterns and areas of risk, there is avenue to make a material difference for employees. No longer simply looking at the bottom line but looking at the far wider social and personal implications of businesses, having the ability to predict injuries before they happen.

Ergonomics is about fitting the task to the worker and with the use of monitoring devices all factors from personal to environmental can be altered to gain best results. When considering both risk factors, it is fair to say that one size doesn’t fit all. By gaining real, data-driven and objective insight into the way a worker moves, the biomechanical obstacles they need to strain to overcome can be identified. Identifying this can be relayed to the worker in the form of training to optimize the worker’s technique within their own capability, but also fed back to the organization to identify areas for improvement in the way the task is fit to the worker.

Objective Data

Using valid qualitative objective data combines all exposures and assists organizations with the ability to also track many psychosocial factors in the workplace. Data user reports identify:

  • Safety champions
  • Workers resistant to change -an informational key in the promotion of safety culture in the workplace
  • Displays of work and task intensity
  • Timelines for hazard fluctuations throughout shifts or weeks – which provides data on organizational work factors and targets work-rest cycles or job rotation
  • Repetitive hazardous movements around common tasks – identified and reviewed for implementation of task modification, substitution, or requirement for introduction of tools and aids

At Soter Analytics, we’ve built a system that self-learns and gives personalized feedback to each individual worker, helping them avoid the movements that might increase their own risk of injury. Soter has conceptualized & built an intensity model that measures how difficult movements are for individuals. This is a step-change improvement from traditional standards that only estimate weights that people could safely move but completely ignore a person’s inherent strength & fatigue. With technology, organizations can now access and create controls that cover a much wider base of risk factors with one tool which are not resource intensive, reduce administrative costs and have far wider potential to keep their workers safe.

About Soter Analytics

Soter Analytics is a global safety science company producing AI-supported wearable solutions that reduce the risk of ergonomic injuries in the workplace. Soter wearables are widely used in logistics, manufacturing, healthcare and other industries, helping leading companies to prevent up to 55% of back & shoulder musculoskeletal injuries.

To see how Soter Analytics can help you improve safety behaviour, engage employees to self-manage their training and prevent workplace ergonomic injuries, simply TRY SOTER today.


  1. Graveling, R. (2019). Ergonomics and Musculoskeletal Disorders (MSDs) in the Workplace: A Forensic and Epidemiological Analysis. In Ergonomics and Musculoskeletal Disorders (MSDs) in the Workplace (1st ed.). CRC Press.
  2. Bureau of Labor Statistics, U.S. Department of Labor. (2019). Workplace Safety Indices by industry: insights and methodology. Retrieved from
  3. Bureau of Labor Statistics, U.S. Department of Labor. Injuries, Illness, and fatalities. Occupational Safety and Health Definitions.
  4. Punnett, L., & Wegman, D. (2004). Work-related musculoskeletal disorders: the epidemiologic evidence and the debate. Journal of Electromyography and Kinesiology, 14(1), 13–23.
  5. Dong, X., Wang, X., & Largay, J. (2015). Occupational and non-occupational factors associated with work-related injuries among construction workers in the USA. International Journal of Occupational and Environmental Health, 21(2), 142–150.
  6. Burdorf A1, Sorock G. Positive and negative evidence of risk factors for back disorders. Scand J Work Environ Health. 1997 Aug;23(4):243-56.
  7. Arjmand, N & Shirazi-Adl, A. (2005). Biomechanics of changes in lumbar posture in static lifting. Spine, 30, 23, 2637-2648.
  8. Cole, M & Grimshaw, P (2003). Low back pain and lifting- a review of epidemiology and aetiology. Work, 21, 2, 173-184
  9. Choi, S (2012). A study of trade-specific occupational ergonomics considerations in the U.S. construction industry. Work, 42, 215-222.

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