Evidence map›Paper›PMID 39860816›Full record

ArticleSensors (Basel, Switzerland)2025

Framework Using Multicriteria Analysis for Evaluating the Risk of Musculoskeletal Disorders.

Karolis Senvaitis, Aušra Adomavičienė, Kristina Daunoravičienė

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Karolis SenvaitisDepartment of Biomechanical Engineering, Vilnius Gediminas Technical University, LT-10223 Vilnius, Lithuania.
Aušra AdomavičienėDepartment of Rehabilitation, Physical and Sports Medicine, Vilnius University, LT-01131 Vilnius, Lithuania.
Kristina DaunoravičienėDepartment of Biomechanical Engineering, Vilnius Gediminas Technical University, LT-10223 Vilnius, Lithuania.ORCID 0000-0003-0898-4860

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study includes musculoskeletal disorder (MSD) risk evaluation based on the IMU sensor data gathered from patient-lifting movement performed by healthcare specialists. This is a continuation of previous research focusing on a novel multicriteria statistical model integrating experimental and large-scale statistical datasets. The proposed model estimates MSD probabilities over 5, 10, and 15 years for the neck (0.537 ± 0.156), shoulder (0.449 ± 0.084), and elbows (0.277 ± 0.221). The model enables individual risk profiling, influenced by dynamic parameters that can reduce the long-term risk by up to 70.49%. The model is in its early development stages, i.e., it is the proof of concept that offers a new approach to assessing MSD risk at work using motion tracking data in combination with statistics. Further studies with larger sample sizes and validated criterion weights are needed to refine and validate this approach.

Indexed as

Musculoskeletal DiseasesHumansMaleNeckRisk AssessmentShoulderbiomechanicslifting motionmusculoskeletal disorderrisk modelstatistics

Identifiers

PMID39860816
PMCPMC11769208

What OpenQuestion holds

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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.