Evidence map›Paper›PMID 27660316›Full record

ArticleBMJ open2016

Evaluation of 'I-Preventive': a digital preventive tool for musculoskeletal disorders in computer workers-a pilot cluster randomised trial.

C Lanhers, B Pereira, G Garde, C Maublant, F Dutheil, E Coudeyre

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in BMJ open, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02350244 (Active Prevention of MSDs), which is not on this map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 pooled it
4.0field-weighted citation impact, top 7% 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.

NCT02350244 phase1completednot on this map

Active Prevention of MSDs (Musculoskeletal Disorders, Upper and Spine Members) in the Context of Computer Screen Work

TypeinterventionalSponsorUniversity Hospital, Clermont-FerrandRan2014 to 2015Enrolled200ConditionsSymptomatic Musculoskeletal Diseases, Latent Musculoskeletal DiseasesArmsComputer program
3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.

  1. Pooled it
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  9. Exploring the Link between Work Addiction Risk and Health-Related Outcomes Using Job-Demand-Control Model.International journal of environmental research and public health · 2020
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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

6 authors at 4 institutions in 1 country.

C LanhersDepartment of Preventive and Occupational Medicine, University Hospital of Clermont-Ferrand (CHU), Clermont-Ferrand, France Department of Physical Medicine and Rehabilitation, University Hospital of Clermont-Ferrand (CHU), Clermont-Ferrand, France Department of Psychological & Psychosocial Stress, CNRS UMR 6024, University of Clermont Auvergne, Clermont-Ferrand, France Clermont Auvergne University, France.
B PereiraUniversity Hospital of Clermont-Ferrand (CHU), Clinical Research and Innovation Direction, Clermont-Ferrand, France.
G GardeMichelin, Clermont-Ferrand, France.
C MaublantMichelin, Clermont-Ferrand, France.
F DutheilDepartment of Preventive and Occupational Medicine, University Hospital of Clermont-Ferrand (CHU), Clermont-Ferrand, France Department of Psychological & Psychosocial Stress, CNRS UMR 6024, University of Clermont Auvergne, Clermont-Ferrand, France Faculty of Health, Australian Catholic University, School of Exercise Science, Melbourne, Victoria, Australia Laboratory of Metabolic Adaptations to Exercise in Physiological and Pathological Conditions (AME2P, EA3533), University of Clermont Auvergne, Clermont-Ferrand, France.ORCID http://orcid.org/0000-0002-1468-6029
E CoudeyreDepartment of Physical Medicine and Rehabilitation, University Hospital of Clermont-Ferrand (CHU), Clermont-Ferrand, France INRA, Human Nutrition Unit UMR1019, Clermont-Ferrand, France.
Centre National de la Recherche Scientifique · FRMichelin (France) · FRCentre Hospitalier Universitaire de Clermont-Ferrand · FRUniversité Clermont Auvergne · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesI-Preventive is a digital preventive tool for musculoskeletal disorders (MSDs) in computer workers. We sought to determine its impact on pain in computer workers with upper limb MSDs and visual discomfort.

methodsWe conducted a pilot cluster randomised trial in 2 different sites of a tyre factory in France. We randomised 200 employees to either an intervention group (I-Preventive) or control group, each comprising symptomatic and asymptomatic employees. The workers were followed up for 5 months. The main outcome was overall recovery from symptoms following 1 month's intervention based on Nordic-style and eyestrain questionnaires.

resultsWe included 185/200 workers: 96 in the intervention group (mean age 41.8±1.4 years; 88.5% males) and 79 in the control group (mean age 42.9±12.0 years; 94.5% males). The most painful areas (numerical scale ≥2) were the neck (40.0%), upper back (18.8%) and shoulders (15.7%). For the most painful anatomical area, the Nordic score significantly decreased after 1 month in the intervention group (p=0.038); no change was observed in the control group (p=0.59). After 1 month's use, the intervention group reported less pain in the painful area and less visual discomfort symptoms (p=0.02). Adherence to the I-Preventive program was 60%.

conclusionsI-Preventive is effective in the short term on musculoskeletal symptoms and visual discomfort by promoting active breaks and eyestrain treatment. This easy-to-use digital tool allows each worker to focus on areas of their choice via personalised, easy exercises that can be performed in the workplace. TRIAL REGISTRATION NUMBER: NCT02350244; Pre-results.

Indexed as

PREVENTIVE MEDICINESPORTS MEDICINE

Identifiers

PMID27660316
PMCPMC5051336
OpenAlexW2522465725

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

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.