An experimental study was performed to determine the applicability and accuracy of occupational hygienist's expert judgment in occupational exposure assessment. The effect of tier 1 model application on improvement of expert judgments were also realized. Hygienists were asked to evaluate inhalation exposure intensity in seven operating units in a tile factory before and after an exposure training session. Participants' judgments were compared to air sampling data in the units; then after relative errors for judgments were calculated. Stepwise regressions were performed to investigate the defining variables. In all situations there were almost a perfect agreement (ICC >0.80) among raters. Correlations between estimated mean exposure and relative percentage error of participants before and after training were significant at 0.01 (correlation coefficients were -0.462 and -0.443, respectively). Results showed that actual concentration and experience resulted in 22.4% prediction variance for expert error as an independent variable. Exposure rating by hygienists was susceptible to error from several sources. Experienced subjects had a better ability to predict the exposures intensity. In lower concentrations, the rating error increased significantly. Leading causes of judgment error should be taken into account in epidemiological studies.
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http://dx.doi.org/10.2486/indhealth.2014-0066 | DOI Listing |
JMIR Form Res
January 2025
School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan.
Background: Origami is a popular activity among preschool children and can be used by therapists as an evaluation tool to assess children's development in clinical settings. It is easy to implement, appealing to children, and time-efficient, requiring only simple materials-pieces of paper. Furthermore, the products of origami may reflect children's ages and their visual-motor integration (VMI) development.
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January 2025
Respiratory Epidemiology & Clinical Research Unit, Centre for Outcomes Research & Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada.
Background: Pulmonary TB (PTB) predominantly affects individuals of working age. We sought to characterise the occupations of people newly diagnosed with PTB in Karachi, Pakistan, by type and physical intensity.
Design/methods: We did a secondary analysis of data from a study evaluating the diagnostic accuracy of artificial intelligence-based chest X-ray (CXR) analysis software, where individuals had been evaluated for active PTB using sputum cultures and had provided information on occupation.
Sensors (Basel)
December 2024
National Institute for Occupational Safety and Health, Cincinnati, OH 45226, USA.
The American Conference of Governmental Industrial Hygienists (ACGIH) Threshold Limit Values (TLVs) for lifting provides risk zones for assessing two-handed lifting tasks. This paper describes two computational models for identifying the lifting risk zones using gyroscope information from five inertial measurement units (IMUs) attached to the lifter. Two models were developed: (1) the ratio model using body segment length ratios of the forearm, upper arm, trunk, thigh, and calf segments, and (2) the ratio + length model using actual measurements of the body segments in the ratio model.
View Article and Find Full Text PDFDiagnostics (Basel)
January 2025
Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, 80138 Naples, Italy.
: Long-term work-related musculoskeletal disorders are predominantly influenced by factors such as the duration, intensity, and repetitive nature of load lifting. Although traditional ergonomic assessment tools can be effective, they are often challenging and complex to apply due to the absence of a streamlined, standardized framework. Recently, integrating wearable sensors with artificial intelligence has emerged as a promising approach to effectively monitor and mitigate biomechanical risks.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
Department of Family Medicine, Taichung Veterans General Hospital, Taichung 407219, Taiwan.
: The prevalence of diabetes is increasing worldwide, particularly in the Pacific Ocean island nations. Although machine learning (ML) models and data mining approaches have been applied to diabetes research, there was no study utilizing ML models to predict diabetes incidence in Taiwan. We aimed to predict the onset of diabetes in order to raise health awareness, thereby promoting any necessary lifestyle modifications and help mitigate disease burden.
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