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Association between performance on the Glittre ADL-test and the functional capacity of patients with HF: A cross-sectional study.

Jéssica LeiteBruna T S AraújoSimone Cristina SoaresBrandãoVanessa Regiane ResquetiFilipe PinheiroBeatriz MonteiroSilvia Marinho MartinsThainá de Gomes FigueiredoMaria Do Amparo AndradeRafael MaiaMaria Inês Remígio de AguiarArmele de Fátima Dornelas de AndradeDaniella Cunha Brandão
Published in: Physiotherapy theory and practice (2020)
Background: The Weber classification based on peak VO2 is a well-established method for categorizing patients with heart failure (HF) regarding severity. However, other submaximal tests such as the Glittre ADL-Test have been gaining prominence in practice due to a coherent and more comprehensive correlation with limitations for performing activities of daily living in patients with heart failure.Objective: To investigate the correlation between the time required to perform the Glittre ADL-Test and the peak VO2 in patients with HF.Methods: A cross-sectional study conducted with 40 adult individuals (21 to 65 years) diagnosed with HF of all etiologies, with LVEF<50% and NYHA II and III.Results: The average time for performing the Glittre ADL-Test was 284.9 seconds, and a significant difference was found between Weber classification classes A and C (p = .01). Significant correlations with peak VO2 were also found (r = -0.424 - p < .01). Thirty (30) patients performed a second test, and the ICC found in the reproducibility analysis was 0.75 (95% CI 0.14-0.91) and p < .01.Conclusion: The Glittre ADL-Test was able to reflect the functional performance of individuals with HF, suggesting that it represents an evaluation tool which can be safely used in clinical practice.
Keyphrases
  • machine learning
  • clinical practice
  • acute heart failure
  • healthcare
  • deep learning
  • physical activity
  • primary care
  • risk factors
  • newly diagnosed
  • quality improvement
  • patient reported outcomes
  • patient reported