The effect of clinically elevated body mass index on physiological stress during manual lifting activities.
Sergio A LemusMallory VolzEduard TiozzoArlette PerryThomas M BestFrancesco TravascioPublished in: PloS one (2022)
Individuals with a body mass index (BMI) classified as obesity constitute 27.7% of U.S. workers. These individuals are more likely to experience work-related injuries. However, ergonomists still design work tasks based on the general population and normal body weight. This is particularly true for manual lifting tasks and the calculation of recommended weight limits (RWL) as per National Institute of Occupational Safety & Health (NIOSH) guidelines. This study investigates the effects of BMI on indicators of physiological stress. It was hypothesized that, for clinically elevated BMI individuals, repeated manual lifting at RWL would produce physiological stress above safety limits. A repetitive box lifting task was designed to measure metabolic parameters: volume of carbon dioxide (VCO2) and oxygen (VO2), respiratory exchange ratio (RER), heart rate (HR), and energy expenditure rate (EER). A two-way ANOVA compared metabolic variables with BMI classification and gender, and linear regressions investigated BMI correlations. Results showed that BMI classification represented a significant effect for four parameters: VCO2 (p < 0.001), VO2 (p < 0.001), HR (p = 0.012), and EER (p < 0.001). In contrast, gender only had a significant effect on VO2 (p = 0.014) and EER (p = 0.017). Furthermore, significant positive relationships were found between BMI and VCO2 (R2 = 59.65%, p < 0.001), VO2 (R2 = 45.01%, p < 0.001), HR (R2 = 21.86%, p = 0.009), and EER (R2 = 50.83%, p < 0.001). Importantly, 80% of obese subjects exceeded the EER safety limit of 4.7 kcal/min indicated by NIOSH. Indicators of physiological stress are increased in clinically elevated BMI groups and appear capable of putting these individuals at increased risk for workplace injury.
Keyphrases
- body mass index
- weight gain
- heart rate
- body weight
- physical activity
- machine learning
- carbon dioxide
- healthcare
- blood pressure
- weight loss
- metabolic syndrome
- type diabetes
- heart rate variability
- deep learning
- magnetic resonance
- transcription factor
- insulin resistance
- mass spectrometry
- computed tomography
- atomic force microscopy
- climate change
- risk assessment
- health information