Evidence map›Paper›PMID 39991136›Full record

ArticleFrontiers in bioengineering and biotechnology2025

Assessing low-back loading during lifting using personalized electromyography-driven trunk models and NIOSH-based risk levels.

Mohamed Irfan Refai, Tiwana Varrecchia, Giorgia Chini, Alberto Ranavolo, Massimo Sartori

Abstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Work-related movement and lumbar spine direction: a systematic review of lifting, posture, and pain risk.International journal of occupational medicine and environmental health · 2026
    Pooled it
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Mohamed Irfan Refai *Department of Biomechanical Engineering, University of Twente, Enschede, Netherlands.
Tiwana Varrecchia *Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, National Institute for Insurance Against Accidents at Work, Rome, Italy.
Giorgia ChiniDepartment of Occupational and Environmental Medicine, Epidemiology and Hygiene, National Institute for Insurance Against Accidents at Work, Rome, Italy.
Alberto RanavoloDepartment of Occupational and Environmental Medicine, Epidemiology and Hygiene, National Institute for Insurance Against Accidents at Work, Rome, Italy.
Massimo SartoriDepartment of Biomechanical Engineering, University of Twente, Enschede, Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Workplace injury risk due to physically demanding tasks (e.g., repeated lifting) is currently assessed using ergonomic guidelines. The Revised NIOSH Lifting Equation (RNLE) is a commonly used approach that assesses risk of low-back loading during different lifting tasks. Advances in musculoskeletal models have enabled the estimation of physiologically valid person-specific musculoskeletal models (pEMS) driven by surface electromyography and joint angle information. These models offer realistic estimates of objective parameters such as moments and compressive and shear loads at the lumbosacral joint. In this study, we applied both techniques (RNLE and pEMS) to assess risk and low-back loading in seven healthy participants performing lifting tasks at different risk levels. We found that the pEMS estimated objective parameters of low-back loading in line with the different risk levels proposed by RNLE. However, the low-back compressive and shear loads were higher than the limits proposed by the RNLE. Moreover, we show that the lumbosacral compressive loads can be a better parameter to demarcate risk levels. We recommend performing this assessment on a larger and diverse population for evaluation of personalized risk levels across lifting tasks in the industry. These approaches can be implemented with wearable sensorized garments to monitor personalized musculoskeletal health unobtrusively in the workplace providing us a better insight into possibility of individual risk.

Indexed as

electromyographyliftingmusculoskeletal modellingNIOSHworkplace musculoskeletal disorder

Identifiers

PMID39991136
PMCPMC11842350

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.