Reproducibility of the multidimensional assessment questionnaire and factors associated for individuals with Parkinson disease
Year: 2025
Type: article
Study design: Observational Study
Abstract: Objectives: To investigate the reproducibility of the Multidimensional Assessment Questionnaire for Individuals with Parkinson's Disease (MAQPD), applied remotely, based on an analysis of test-retest reliability, standard error of measurement (SEM) and minimum detectable change (MDC), and to investigate the associated factors. Methods: The MAQPD was applied and re-applied via Google Forms to individuals with Parkinson. Personal and clinical variables (age, sex, number of associated diseases, number of medications, skin color, and sleep quality - number of times waking up at night) also was registered. Test-retest reliability for each section was determined by the intraclass correlation coefficient (ICC). With these values, the SEM (SEM%) and MDC were calculated. Spearman's correlation coefficient was used to assess the correlations between each section of the MAQPD and the associated factors. Results: Sixty-one individuals were included. All three sections showed high reliability (ICC≥0.90), and a SEM% lower than 15%. The MDC for the ADL section was 13.52, for cognition was 7.98, and for pain was 11.72. There was a significant correlation between: the ADL section and age (ρ=-0.371,p=0.004) and skin color (ρ=-0.324,p=0.011); the cognition section and the number of medications (ρ=-0.295,p=0.022); and the pain section and age (ρ=0.286,p=0.027) and sleep quality (ρ=-0.292,p=0.024). Conclusion: The MAQPD is reproducible and capable of generating reliable measures in the self-assessment of this population. Furthermore, older and black individuals have worse performance in ADLs, individuals taking a higher number of medications have a worse cognitive state and, finally, younger individuals with a higher number of nocturnal awakenings have more pain.
Source: Revista brasileira de neurologia
Authors: Brenda Fernandes, Thaís Silva, Lívia Muniz, Maria Luiza Oliveira, Mariana Gomes, Patrick Avelino, Kênia Menezes
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4409538064