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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
Published on: July 24, 2019
Beyond factor analysis: Multidimensionality and the Parkinson's Disease Sleep Scale-Revised
Maria E Pushpanathan1, Andrea M Loftus2, Natalie Gasson2
1School of Psychological Sciences, The University of Western Australia, Crawley, Western Australia, Australia.
Plos One
|February 13, 2018
Summary
Parkinson's disease sleep scales like the PDSS-R are multidimensional, not unidimensional. A bifactor model best fits data, revealing sleep symptoms influenced by mood and personality, improving scale sensitivity.
Area of Science:
- Neurology
- Psychometrics
- Sleep Medicine
Background:
- Sleep disturbances are common in Parkinson's disease (PD), impacting cognition.
- Existing scales like the Parkinson's Disease Sleep Scale (PDSS) and its variants (PDSS-R, PDSS-2) use few items to assess sleep symptoms.
- Concerns exist regarding the conceptual breadth and overlap of items within these scales, potentially affecting data interpretation.
Purpose of the Study:
- To test if a multidimensional measurement model (bifactor model) is more appropriate than traditional factor analysis for PDSS-R data.
- To investigate the underlying structure of the PDSS-R, assessing its suitability for measuring sleep disturbances in Parkinson's disease.
- To determine if a bifactor model improves the accuracy of modeling variance compared to unidimensional or traditional factor models.
Main Methods:
- Compared three measurement models (unidimensional, 3-factor, and bifactor) using data from 166 participants with idiopathic Parkinson's disease.
- Utilized data from the Parkinson's Disease Sleep Scale-Revised (PDSS-R) as an exemplar.
- Assessed model fit to determine the most appropriate structure for the PDSS-R data.
Main Results:
- The confirmatory bifactor model demonstrated satisfactory model fit, unlike the unidimensional and 3-factor models.
- This suggests that PDSS-R data are multidimensional, with a general sleep factor and specific sub-factors.
- Differential associations between factor scores and patient characteristics indicated that some items are influenced by mood and personality beyond sleep symptoms.
Conclusions:
- A bifactor model is superior for analyzing data from the PDSS-R, indicating its multidimensional nature.
- Multidimensional measurement models may enhance the sensitivity and accuracy of sleep assessment tools in Parkinson's disease.
- These findings support the use of multidimensional models for PDSS and PDSS-2 scales to better understand sleep disturbances in PD.
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