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MedRisk is one of the several available measurement tools developed to assess patient satisfaction with outpatient physical therapy services and has been adapted to different countries, including Brazil. However, the literature shows conflicting results regarding its dimensional structure. To evaluate the Brazilian version of MedRisk for semantic equivalence to the original English version, followed by an assessment of its dimensional structure. The original 18-item version was first submitted to translation and back-translation, followed by a pre-test on 55 patients (semantic equivalence phase). Another 326 patients were interviewed to assess the dimensional structure. An initial confirmatory factor analysis (CFA) was followed by exploratory structural equation modelling (ESEM). CFA was performed on the 16-item version agreed upon in the first phase, and indicated poor fit (RMSEA = 0.10, 90% CI: 0.08–0.12; CFI = 0.84; TLI = 0.79). The ensuing ESEM pointed to a four-factor structure, but five cross-loadings emerged, suggesting violation of the item factor specificity. High residual variances were also found in 3 items (≥0.70), which is indicative of poor item reliability. Although semantic equivalence was achieved, several uncovered psychometric (configural and metric) shortcomings show that MedRisk still requires refinements before recommending its use in Brazilian health services. [ABSTRACT FROM AUTHOR]

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Copyright of Revista Brasileira de Prescrição e Fisiologia do Exercício is the property of Instituto Brasileiro de Pesquisa e Ensino em Fisiologia do Exercicio and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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 ( r  = 0.21; p  = 0.003); days in intensive care unit ( r  = 0.22; p  = 0.002) and days in invasive mechanical ventilation ( r  = 0.35; p  = 0.001). Hospitalized individuals showed a higher chance of presenting late musculoskeletal pain (OR = 1.42: 95%CI 1.09-2.04). Days in intensive care unit ( β  = 0,234: P  = 0,001) and days in invasive mechanical ventilation ( β  = 0.764: P  = 0.001) were predictors of pain intensity [F(2,192) = 18.559; R 2  = 0.231; p  = 0.001]., 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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Neto, Reis, Ferreira, Alexandre and Almeida.)

Background: The assessment of painful areas through printed body charts is a simple way for clinicians to identify patients with widespread pain in primary care. However, there is a lack in the literature about a simple and automated method designed to analyze pain drawings in body charts in clinical practice., Purpose: To test the inter- and intra-rater reliabilities and concurrent validity of software (PainMAP) for quantification of pain drawings in patients with low back pain., Methods: Thirty-eight participants (16 [42.10%] female; mean age 50.24 [11.54] years; mean body mass index 27.90 [5.42] kg/m 2 ; duration of pain of 94.35 [96.11] months) with a current episode of low back pain were recruited from a pool of physiotherapy outpatients. Participants were instructed to shade all their painful areas on a body chart using a red pen. The body charts were digitized by separate raters using smartphone cameras and twice for one rater to analyze the intra-rater reliability. Both the number of pain sites and the pain area were calculated using ImageJ software (reference method). The PainMAP software used image processing methods to automatically quantify the data from the same digitized body charts., Results: The reliability analyses revealed that PainMAP has excellent inter- and intra-rater reliabilities to quantify the number of pain sites (intraclass correlation coefficient [ICC] 2,1 : 0.998 [95% confidence interval (CI) 0.996 to 0.999]; ICC 2,1 : 0.995 [95% CI 0.991 to 0.998]) and the pain area [ICC 2,1 : 0.998 (95% CI 0.995 to 0.999); ICC 2,1 : 0.975 (95% CI 0.951 to 0.987)], respectively. The standard error of the measurement was 0.22 (4%) for the number of pain sites and 0.03 cm 2 (4%) for the pain area. The Bland-Altman analyses revealed no substantive differences between the 2 methods for the pain area (mean difference = 0.007 [95% CI -0.053 to 0.067])., Conclusion: PainMAP software is reliable and valid for quantification of the number of pain sites and the pain area in patients with low back pain., (© 2020 World Institute of Pain.)