tlv for hal
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2021 ◽  
Vol 92 ◽  
pp. 103310
Author(s):  
David H. Seidel ◽  
Kai Heinrich ◽  
Ingo Hermanns-Truxius ◽  
Rolf P. Ellegast ◽  
Lope H. Barrero ◽  
...  
Keyword(s):  

2020 ◽  
Vol 10 (2) ◽  
pp. 6329
Author(s):  
Sara Patricia Angulo Martínez ◽  
Yensi Johana Valencia Quintero ◽  
Liliana Marcela Rivera Huertas ◽  
Lessby Gómez Salazar

Introducción: Debido a los avances en tecnología y a la preocupación por mejorar las condiciones laborales de los trabajadores, se han desarrollado métodos de evaluación observacionales (MEO) para la evaluación del riesgo biomecánico (ERB) asociado a desordenes musculoesqueléticos (DME), que han sido empleados en investigaciones publicadas durante los años 2014 - 2019. Sin embargo, la existencia de nuevos métodos, así como la frecuencia y forma de uso de los ya reconocidos, es una información que está diseminada y no es de fácil acceso para los profesionales interesados en el tema. Esta investigación busca compilar lo reportado sobre esta temática para facilitar su consulta y evidenciar las tendencias y vacíos en torno a los MEO.Objetivo: Realizar una revisión bibliográfica de la literatura científica publicada entre 2014-2019, donde se evidencie el uso de los MEO para ERB de DME en miembros superiores (MMSS) en trabajadores.Métodos: Se realizó una búsqueda de artículos que evidenciaran el uso de MEO, publicados en idioma inglés en Medline, ScienceDirect y Scopus desde el 08/03/19 hasta el 15/06/19.Resultados: Aplicados los criterios de inclusión y exclusión, se encontró el uso de ocho MEO para el estudio de DME de MMSS, de los cuales RULA, STRAIN INDEX, TLV for HAL y Check list OCRA, fueron los más reportados.Conclusión: Los MEO continúan siendo altamente reportados siendo pocos los nuevos métodos encontrados. Se evidencia la necesidad de mayores publicaciones en Colombia y América latina.


2019 ◽  
Vol 76 (Suppl 1) ◽  
pp. A76.3-A77
Author(s):  
Carisa Harris-Adamson ◽  
Ellen Eisen ◽  
David Rempel ◽  
Fred Gerr ◽  
Stephen Bao ◽  
...  

Carpal tunnel syndrome (CTS) is a potentially disabling occupational illness with high incidence rates in certain occupations. Prospective workplace studies have identified associations between personal, psychosocial and biomechanical factors and incident CTS. However, biased estimates of association due to the healthy worker survivor effect (HWSE) have not been fully investigated. The HWSE results in left truncation bias when affected workers are lost from the population of more highly exposed workers before study participants are sampled. Right truncation bias occurs when affected workers are lost disproportionately from higher exposure strata after enrollment in a prospective cohort study. This study evaluated the effects of right truncation bias on risk estimates for biomechanical factors.Between 2001 and 2010 five research groups in the US conducted coordinated prospective studies of incident CTS among workers from various industries. Hazard ratios (HR) were estimated using Cox proportional regression analyses with adjustment for personal and workplace psychosocial factors. Inverse probability censoring weight (IPCW) values were created using a cross-validated approach analogous to the lasso method for variable selection and parameter regularization. The weighting model predicted the probability of exit from the cohort in the next 3 months.The final models included 2520 workers and 166 incident CTS cases. IPCW models with adjustment for right truncation bias were used to predict incident CTS. Effect size estimates for the workplace biomechanical factors of Peak Force, HAL Scale, Forceful Repetition Rate and the ACGIH TLV for HAL increased by 11% to 71% in comparison to models without adjustment for right truncation bias. After adjusting for right truncation bias the predicted incidence of CTS in highly exposed workers increased up to 3-fold.Bias from symptomatic workers disproportionately leaving the workplace was identified in this cohort. Adjusting for this bias has likely produced more accurate dose-response relationships than those previously reported.


2017 ◽  
Vol 15 (2) ◽  
pp. 157-166
Author(s):  
Jay M. Kapellusch ◽  
Barbara A. Silverstein ◽  
Stephen S. Bao ◽  
Mathew S. Thiese ◽  
Andrew S. Merryweather ◽  
...  

2017 ◽  
Vol 14 (12) ◽  
pp. 1011-1019 ◽  
Author(s):  
Jay M. Kapellusch ◽  
Stephen S. Bao ◽  
Barbara A. Silverstein ◽  
Andrew S. Merryweather ◽  
Mathew S. Thiese ◽  
...  

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Matthew S. Thiese ◽  
Kurt T. Hegmann ◽  
Jay Kapellusch ◽  
Andrew Merryweather ◽  
Stephen Bao ◽  
...  

Introduction. There is an increasing body of literature relating musculoskeletal diseases to both job physical exposures and psychosocial outcomes. Relationships between job physical exposure measures and psychosocial factors have not been well examined or quantified. These exploratory analyses evaluate relationships between quantified exposures and psychosocial outcomes.Methods. Individualized quantification of duration, repetition, and force and composite scores of the Strain Index (SI) and the Threshold Limit Value for Hand Activity Level (TLV for HAL) were compared to 10 psychosocial measures. Relationships and predicted probabilities were assessed using ordered logistic regression. Analyses were adjusted for age, BMI, and gender.Results and Discussion. Among 1834 study participants there were multiple statistically significant relationships. In general, as duration, repetition, and force increased, psychosocial factors worsened. However, general health and mental exhaustion improved with increasing job exposures. Depression was most strongly associated with increased repetition, while physical exhaustion was most strongly associated with increased force. SI and TLV for HAL were significantly related to multiple psychosocial factors. These relationships persisted after adjustment for strong confounders.Conclusion. This study quantified multiple associations between job physical exposures and occupational and nonoccupational psychosocial factors. Further research is needed to quantify the impacts on occupational health outcomes.


2013 ◽  
Vol 57 (3) ◽  
pp. 286-302 ◽  
Author(s):  
Arun Garg ◽  
Jay M. Kapellusch ◽  
Kurt T. Hegmann ◽  
Matthew S. Thiese ◽  
Andrew S. Merryweather ◽  
...  

Author(s):  
Jay M. Kapellusch ◽  
Arun Garg ◽  
Kurt T. Hegmann ◽  
Matthew S. Thiese ◽  
Elizabeth J. Malloy
Keyword(s):  

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