Determination of Waste Management Workers' Physical and Psychological Load: A Cross-Sectional Study Using Biometric Data.
Itsuki KageyamaNobuki HashiguchiJianfei CaoMakoto NiwaYeongjoo LimMasanori TsutsumiJiakan YuShintaro SengokuSoichiro OkamotoSeiji HashimotoKota KodamaPublished in: International journal of environmental research and public health (2022)
Waste management workers experience high stress and physical strain in their work environment, but very little empirical evidence supports effective health management practices for waste management workers. Hence, this study investigated the effects of worker characteristics and biometric indices on workers' physical and psychological loads during waste-handling operations. A biometric measurement system was installed in an industrial waste management facility in Japan to understand the actual working conditions of 29 workers in the facility. It comprised sensing wear for data collection and biometric sensors to measure heart rate (HR) and physical activity (PA) based on electrocardiogram signals. Multiple regression analysis was performed to evaluate significant relationships between the parameters. Although stress level is indicated by the ratio of low frequency (LF) to high frequency (HF) or high LF power in HR, the results showed that compared with workers who did not handle waste, those who did had lower PA and body surface temperature, higher stress, and lower HR variability parameters associated with higher psychological load. There were no significant differences in HR, heart rate interval (RRI), and workload. The psychological load of workers dealing directly with waste was high, regardless of their PA, whereas others had a low psychological load even with high PA. These findings suggest the need to promote sustainable work relationships and a quantitative understanding of harsh working conditions to improve work quality and reduce health hazards.
Keyphrases
- heart rate
- physical activity
- heavy metals
- high frequency
- sewage sludge
- heart rate variability
- municipal solid waste
- mental health
- healthcare
- blood pressure
- sleep quality
- public health
- life cycle
- body mass index
- risk assessment
- health information
- machine learning
- primary care
- high resolution
- big data
- deep learning
- heat stress
- data analysis
- climate change