Using Body Composition Analysis to Manage Health and Safety at Work
Obesity has always been associated with several health conditions that may affect the body’s functioning and longevity. Over the past few years, the rising obesity epidemic worldwide has raised concerns amongst health experts globally. Studies have also shown that obesity prevalence in Australian adults rose from 55% to 60% from 2006 to 2019. The percentage is expected to go up.(1)
While the incidence of Obesity started rising, so did the awareness about Body Composition. Body Composition Analysis effectively evaluates an individual’s fat mass, muscle mass, and body fat percentage. There are various body composition analysis tools – one of the most popular and reliable method being Bioelectrical Impedance (BIA).
What is the Importance of Body Composition Analysis?
Body Composition Analysis allows an individual to know the ratio of fats and muscle mass in their body and what interventions they need to improve their overall health. This assessment method has gained attention, particularly after the rising trend of Obesity worldwide amongst office-going individuals in every field.
Body Composition Analysis allows an individual insight into their nutritional needs and the functional status of their body. It may be used to prevent different diseases associated with unhealthy body composition and assess one’s progress after therapeutic interventions. (2)
By educating one on their fat mass, muscle mass, and body fat percentage, body composition analysis will enable a person to compare their results to healthy individuals. They can also compare their measurements to the ideal measurements that have been predetermined with the help of population data. Hence, this comparison motivates to reduce the composition of body components that are above normal levels and increase those below normal levels. An example of this may be lowering total body fat and increasing muscle mass in overweight individuals through increased physical activity and dietary changes.
Important Parameters Included in Body Composition Analysis
SMI stands for Skeletal Muscle Index and has become a popular body composition analysis parameter. It helps identify muscle mass and the risk of frailty. (3) This is particularly important for individuals who are at risk for Sarcopenia, as it is a condition that is associated with a loss of skeletal muscle mass and function (4) Skeletal Muscle Index is calculated by dividing the skeletal muscle mass into the legs and arms to the individual’s height. Hence, it can be effectively used to diagnose conditions like Sarcopenia and monitor the individual’s nutritional status.
Visceral Fat Level
Commonly assessed in medicine with percentage of body fat, is Visceral Fat present in the body. This type of fat is found around the organs found inside the abdomen. It may be linked to numerous metabolic disorders, including insulin resistance, glucose intolerance, hyperinsulinemia, and even type 2 diabetes. Hence, understanding the body fat percentage and muscle allows opportunity for intervention to prevent metabolic and cardiovascular disorders.
Waist To Hip Circumference
The Circumference of an individual’s waist measures their body’s central fat. This fat is usually associated with an altered lipid profile or increased chances of Insulin resistance. The Waist to Hip Ratio is a popular body composition analysis parameter for predicting the disease risk in an individual. (5)
When this ratio is more than 0.85, it represents a more central body fat distribution. Moreover, experts believe that women with a waist to hip ratio of 0.85 and a mean of 1 are at an increased risk for cancer, cardiovascular disorders, and Diabetes. (6)
Total Body Water
The total amount of water present in the body may be divided into two types known as extracellular water and intracellular water. As the name suggests, while the extracellular water is found outside the cells, the intracellular is present inside the cells. Since Total body water is essential for maintaining body health, this parameter allows individuals to assess their health status and plan required interventions.
Skeletal Muscle Mass
Skeletal Muscle Mass helps analyze the mass of lean muscles present at every body segment. Since these muscles are essential for the mobility and posture maintenance of the body, an imbalance of this parameter may indicate reduced health status and the need for increased physical activity.
Fat-Free mass comprises all the tissues in the body, except for fat-based ones. It includes skeletal muscles, parenchymal tissues, and bones. This parameter helps an individual determine their total energy expenditure over 24 hours. An increased fat-free mass is a cause of increased energy expenditure in overweight and obese individuals. (7)
How Body Composition Analysis can help Companies Manage their Health and Safety
Unhealthy body composition is a common cause of poor health amongst people worldwide. Poor health not only affects social and private lives, but also has a deleterious effect on work performance. Poor health and wellbeing may cause our teams to slow in productivity, miss out on work and have a negative impact on overall office operations. Data from Integrated Benefits Institute shows that a loss of more than $576 billion is seen every year due to the productivity loss and missed workdays by employees. Therefore, experts suggest that management and employers encourage keeping the number of unexpected loss of workdays to a minimum, by educating and encouraging staff on healthy interventions and how to maintain their health continuously.
The increased working hours and prolonged sitting positions in an office setup increase musculoskeletal problems and Obesity. Studies have shown that nurses are more likely to suffer from musculoskeletal overload. In one study, 61.7% of the nurses complained of lower back pain, 41. In addition, 5% complained of shoulder pain, while 48.9% complained of neck pain. (8) This further affirms the importance of health assessment tools like Body Composition Analysis to allow the employees to check their health and the changes they need to make to improve their health.
Moreover, other studies have shown Obesity in office workers is associated with an increased risk for sick leave due to its harmful effect on various body systems. (9) This can be a significant burden on a country’s economy and national health setup. (10)
Introduction of Body Composition Analysis tools at the workplace will encourage staff to know what is happening inside their body and what changes they need to prevent diseases and maintain their health. The benefit of such health and wellness-promoting interventions amongst the office workers was seen in a study conducted by a Ferrari Company in Italy. The employees who agreed to the health recommendations and followed the nutritional advice thrice a week had a much lower risk for cardiovascular disease than those who did not. (11)
- Keramat SA, Alam K, Ahinkorah BO, et al. Obesity, Disability and Self-Perceived Health Outcomes in Australian Adults: A Longitudinal Analysis Using 14 Annual Waves of the HILDA Cohort. Clinicoecon Outcomes Res. 2021;13:777-788. Published 2021 Sep 7. doi:10.2147/CEOR.S318094
- Thibault, R., Genton, L., & Pichard, C. (2012). Body composition: why, when and for who?. Clinical nutrition (Edinburgh, Scotland), 31(4), 435–447. https://doi.org/10.1016/j.clnu.2011.12.011
- Kim KM, Jang HC, Lim S. Differences among skeletal muscle mass indices derived from height-, weight-, and body mass index-adjusted models in assessing Sarcopenia. Korean J Intern Med. 2016;31(4):643-650. doi:10.3904/kjim.2016.015
- Fielding RA, Vellas B, Evans WJ, Bhasin S, Morley JE, Newman AB, Abellan van Kan G, Andrieu S, Bauer J, Breuille D, Cederholm T, Chandler J, De Meynard C, Donini L, Harris T, Kannt A, Keime Guibert F, Onder G, Papanicolaou D, Rolland Y, Rooks D, Sieber C, Souhami E, Verlaan S, Zamboni M. Sarcopenia: an undiagnosed condition in older adults. Current consensus definition: prevalence, etiology, and consequences. International working group on sarcopenia. J Am Med Dir Assoc. 2011 May;12(4):249-56. doi: 10.1016/j.jamda.2011.01.003. Epub 2011 Mar 4. PMID: 21527165; PMCID: PMC3377163.
- Rimm, A. A., Hartz, A. J., & Fischer, M. E. (1988). A weight shape index for assessing risk of disease in 44,820 women. Journal of clinical epidemiology, 41(5), 459–465. https://doi.org/10.1016/0895-4356(88)90047-9
- Seidell, J. C., Oosterlee, A., Thijssen, M. A., Burema, J., Deurenberg, P., Hautvast, J. G., & Ruijs, J. H. (1987). Assessment of intra-abdominal and subcutaneous abdominal fat: relation between anthropometry and computed tomography. The American journal of clinical nutrition, 45(1), 7–13. https://doi.org/10.1093/ajcn/45.1.7
- Ravussin, E., Burnand, B., Schutz, Y., & Jéquier, E. (1982). Twenty-four-hour energy expenditure and resting metabolic rate in obese, moderately obese, and control subjects. The American journal of clinical nutrition, 35(3), 566–573. https://doi.org/10.1093/ajcn/35.3.566
- Anna Kołcz, Martyna Baran, Karolina Walewicz, Małgorzata Paprocka-Borowicz, Joanna Rosińczuk, “Analysis of Selected Body Composition Parameters and Ergonomic Safety among Professionally Active Nurses in Poland: A Preliminary Prospective Monocentric and Observational Study”, BioMed Research International, vol. 2020, Article ID 9212587, 9 pages, 2020. https://doi.org/10.1155/2020/9212587
- Neovius, K., Johansson, K., Rössner, S., & Neovius, M. (2008). Disability pension, employment and obesity status: a systematic review. Obesity reviews : an official journal of the International Association for the Study of Obesity, 9(6), 572–581. https://doi.org/10.1111/j.1467-789X.2008.00502.x
- Lehnert, T., Sonntag, D., Konnopka, A., Riedel-Heller, S., & König, H. H. (2013). Economic costs of overweight and Obesity. Best practice & research. Clinical endocrinology & metabolism, 27(2), 105–115. https://doi.org/10.1016/j.beem.2013.01.002
- Biffi, A., Fernando, F., Adami, P.E. et al. Ferrari Corporate Wellness Program: Results of a Pilot Analysis and the “Drag” Impact in the Workplace. High Blood Press Cardiovasc Prev 25, 261–266 (2018). https://doi.org/10.1007/s40292-018-0266-z