Evidence map›Paper›PMID 37965093›Full record

ArticleFrontiers in rehabilitation sciences2023

Hospital admission is associated with disability and late musculoskeletal pain in individuals with long COVID.

Ricardo Bezerra Duarte Neto, Luis Felipe Fonseca Reis, Arthur de Sá Ferreira, Dângelo José de Andrade Alexandre, Renato Santos de Almeida

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Article in Frontiers in rehabilitation sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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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.

Ricardo Bezerra Duarte NetoPost Graduation Program in Rehabilitation Sciences, Augusto Motta University Center (UNISUAM), Rio de Janeiro, Brazil.
Luis Felipe Fonseca ReisPost Graduation Program in Rehabilitation Sciences, Augusto Motta University Center (UNISUAM), Rio de Janeiro, Brazil.
Arthur de Sá FerreiraPost Graduation Program in Rehabilitation Sciences, Augusto Motta University Center (UNISUAM), Rio de Janeiro, Brazil.
Dângelo José de Andrade AlexandrePhysiotherapy Department. Rio de Janeiro Ortophaedic Trauma Institute (INTO), Rio de Janeiro, Brazil.
Renato Santos de AlmeidaPost Graduation Program in Rehabilitation Sciences, Augusto Motta University Center (UNISUAM), Rio de Janeiro, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The acute clinical repercussions of SARS-CoV-2 infection have been widely studied. However, the possible late repercussions of long COVID have not yet been well defined in the literature. Objectives: To identify the presence of pain and musculoskeletal disability in patients with Long COVID and also to identify predictive factors for pain intensity in this population. Methods: In this cross-sectional and retrospective observational study individuals with Long COVID symptoms were included. It was collected musculoskeletal disability measures, data from patient-related outcome measures and variables from a COVID-19 outpatient service database. Associations and sub-group analyses were performed considering the variables pain, disability and hospitalization. Linear regression was performed to identify predictive factors for pain intensity in Long COVID patients. Results: We evaluated 195 patients and most of them (57%) presented musculoskeletal pain in one area of the body. Pain sub-group presented worse disability indices and worse clinical course during hospitalization. Hospitalized patients presented worse disability indices comparing to non-hospitalized. Significant correlations were found between pain and days of non-invasive oxygen support ( Conclusion: Individuals with Long COVID presented musculoskeletal pain and disability. Hospitalized patients showed a greater chance of having musculoskeletal pain. Days in intensive care unit and days in invasive mechanical ventilation were predictors of late musculoskeletal pain intensity.

Indexed as

disabilityhospitalizationlong-COVIDmusculoskeletal painphysical therapy

Identifiers

PMID37965093
PMCPMC10641772

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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.