Evidence map›Paper›PMID 29117932›Full record

ArticleJMIR research protocols2017

Patient Transfers and Risk of Back Injury: Protocol for a Prospective Cohort Study With Technical Measurements of Exposure.

Jonas Vinstrup, Pascal Madeleine, Markus Due Jakobsen, Kenneth Jay, Lars Louis Andersen

Open access · goldAbstract read
In one paragraph

Article in JMIR research protocols, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.1field-weighted citation impact, top 22% of its field
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

7 citing papers in PubMed, 11 citations in OpenAlex.

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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 at 3 institutions in 2 countries.

Jonas VinstrupDepartment of Musculoskeletal Disorders, National Research Centre for the Working Environment, Copenhagen, Denmark.ORCID http://orcid.org/0000-0001-8430-0139
Pascal MadeleineDepartment of Health Science and Technology, Physical Activity and Human Performance Group, SMI, Aalborg University, Aalborg, Denmark.ORCID http://orcid.org/0000-0002-2164-234X
Markus Due JakobsenDepartment of Musculoskeletal Disorders, National Research Centre for the Working Environment, Copenhagen, Denmark.ORCID http://orcid.org/0000-0003-1165-968X
Kenneth JayThe Carrick Institute for Graduate Studies, Institute of Clinical Neuroscience and Rehabilitation, Florida, FL, United States.ORCID http://orcid.org/0000-0003-4431-6042
Lars Louis AndersenDepartment of Musculoskeletal Disorders, National Research Centre for the Working Environment, Copenhagen, Denmark.ORCID http://orcid.org/0000-0003-2777-8085
National Research Centre for the Working Environment · DKAalborg University · DKCarrick Institute · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMore than one third of nurses experience musculoskeletal pain several times during a normal work week. Consistent use of assistive devices during patient transfers is associated with a lower risk of occupational back injuries and low back pain (LBP). While uncertainties exist regarding which type of assistive devices most efficiently prevent LBP, exposure assessments using technological advancements allow for quantification of muscle load and body positions during common work tasks.

objectiveThe main objectives of this study are (1) to quantify low back and neck/shoulder muscle load in Danish nurses during patient transfers performed with different types of assistive devices, and (2) to combine the exposure profile for each type of assistive device with fortnightly questionnaires to identify the importance of muscle load (intensity and frequency of transfers) and body position (degree of back inclination and frequency) on LBP intensity and risk of back injury during a patient transfer.

methodsA combination of technical measurements (n=50) and a prospective study design (n=2000) will be applied on a cohort of female nurses in Danish hospitals. The technical measurements will be comprised of surface electromyography and accelerometers, with the aim of quantifying muscle load and body positions during various patient transfers, including different types of assistive devices throughout a workday. The study will thereby gather measurements during real-life working conditions. The prospective cohort study will consist of questionnaires at baseline and 1-year follow-up, as well as follow-up via email every other week for one year on questions regarding the frequency of patient transfers, use of assistive devices, intensity of LBP, and back injuries related to patient transfers. The objective measurements on muscle load and body positions during patient handlings will be applied to the fortnightly replies regarding frequency of patient transfer and use of different assistive devices, in order to identify risk factors for back injuries related to patient transfers and intensity of LBP.

resultsData collection is scheduled to commence during the winter of 2017.

conclusionsThe design of this study is novel in its combination of technical measurements applied on a prospective cohort, and the results will provide important information about which assistive devices are associated with intensity of LBP and risk of back injury related to patient transfers. Furthermore, this study will shed light on the dose-response relationship between intensity, duration, and frequency of patient transfers and the intensity of LPB in Danish nurses, and will thereby help to guide and improve electronic health practices among this population.

Indexed as

assistive deviceselectromyographyhospitallow back painnursepain

Identifiers

PMID29117932
PMCPMC5700406
OpenAlexW2767576216

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.