Variable Lifting Index (VLI): a new method for evaluating variable lifting tasks
Public Domain
-
2016/08/01
-
Details
-
Personal Author:
-
Description:Objective: We seek to develop a new approach for analyzing the physical demands of highly variable lifting tasks through an adaptation of the Revised NIOSH (National Institute for Occupational Safety and Health) Lifting Equation (RNLE) into a Variable Lifting Index (VLI). Background: There are many jobs that contain individual lifts that vary from lift to lift due to the task requirements. The NIOSH Lifting Equation is not suitable in its present form to analyze variable lifting tasks. Method: In extending the prior work on the VLI, two procedures are presented to allow users to analyze variable lifting tasks. One approach involves the sampling of lifting tasks performed by a worker over a shift and the calculation of the Frequency Independent Lift Index (FILI) for each sampled lift and the aggregation of the FILI values into six categories. The Composite Lift Index (CLI) equation is used with lifting index (LI) category frequency data to calculate the VLI. The second approach employs a detailed systematic collection of lifting task data from production and/or organizational sources. The data are organized into simplified task parameter categories and further aggregated into six FILI categories, which also use the CLI equation to calculate the VLI. Results: The two procedures will allow practitioners to systematically employ the VLI method to a variety of work situations where highly variable lifting tasks are performed. Conclusions: The scientific basis for the VLI procedure is similar to that for the CLI originally presented by NIOSH; however, the VLI method remains to be validated. Application: The VLI method allows an analyst to assess highly variable manual lifting jobs in which the task characteristics vary from lift to lift during a shift. [Description provided by NIOSH]
-
Subjects:
-
Keywords:
-
ISSN:0018-7208
-
Document Type:
-
Genre:
-
Place as Subject:
-
CIO:
-
Division:
-
Topic:
-
Location:
-
Volume:58
-
Issue:5
-
NIOSHTIC Number:nn:20047245
-
Citation:Hum Factors 2016 Aug; 58(5):695-711
-
Contact Point Address:Enrico Occhipinti, c/o Clinica del Lavoro, Università degli Studi, Via S.Barnaba, 8 - 20122 Milano, Italy
-
Email:epmenrico@tiscali.it
-
Federal Fiscal Year:2016
-
Peer Reviewed:True
-
Source Full Name:Human Factors
-
Collection(s):
-
Main Document Checksum:urn:sha-512:d9480758b3103311d9e1ed8c69497d704dcb3bf8a2841d30114e1f46a69b69cf4aa834d80c0fc8411d50e129241eecd1e1257d2761fe6e10bed53a64401c1742
-
Download URL:
-
File Type:
ON THIS PAGE
CDC STACKS serves as an archival repository of CDC-published products including
scientific findings,
journal articles, guidelines, recommendations, or other public health information authored or
co-authored by CDC or funded partners.
As a repository, CDC STACKS retains documents in their original published format to ensure public access to scientific information.
As a repository, CDC STACKS retains documents in their original published format to ensure public access to scientific information.
You May Also Like