Dataset Information

A Neurodegenerative Disease Sleep Questionnaire: principal component analysis in Parkinson's disease.

ABSTRACT: Sleep disturbances are common in many neurodegenerative diseases and may include altered sleep duration, fragmented sleep, nocturia, excessive daytime sleepiness, and vivid dreaming experiences, with occasional parasomnias. Although representing the "gold standard," polysomnography is not always cost-effective or available for measuring sleep disturbance, particularly for screening. Although numerous sleep-related questionnaires exist, many focus on a specific sleep disturbance (e.g., restless legs, REM Behavior Disorder) and do not capture efficiently the variety of sleep issues experienced by such patients. We administered the 12-item Neurodegenerative Disease Sleep Questionnaire (NDSQ) and the Epworth Sleepiness Scale to 145 idiopathic Parkinson's disease patients. Principal component analysis using eigenvalues greater than 1 suggested five separate components: sleep quality (e.g., sleep fragmentation), nocturia, vivid dreams/nightmares, restless legs symptoms, and sleep-disordered breathing. These results demonstrate construct validity of our sleep questionnaire and suggest that the NDSQ may be a useful screening tool for sleep disturbances in at least some types of neurodegenerative disorders.

SUBMITTER: Scullin MK

PROVIDER: S-EPMC3947083 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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A Neurodegenerative Disease Sleep Questionnaire: principal component analysis in Parkinson's disease.

Scullin Michael K MK Harrison Tyler L TL Factor Stewart A SA Bliwise Donald L DL

Journal of the neurological sciences 20130912 1-2

Sleep disturbances are common in many neurodegenerative diseases and may include altered sleep duration, fragmented sleep, nocturia, excessive daytime sleepiness, and vivid dreaming experiences, with occasional parasomnias. Although representing the "gold standard," polysomnography is not always cost-effective or available for measuring sleep disturbance, particularly for screening. Although numerous sleep-related questionnaires exist, many focus on a specific sleep disturbance (e.g., restless l ...[more]

PMID: 24074551

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Project description:BACKGROUND:To develop patient-reported outcome instruments, statistical techniques (e.g., principal components analysis; PCA) are used to examine correlations among items and identify interrelated item subsets (empirical factors). However, interpretation and labelling of empirical factors is a subjective process, lacking precision or conceptual basis. We report a novel and reproducible method for mapping between theoretical and empirical factor structures. We illustrate the method using the pilot Aberdeen Glaucoma Questionnaire (AGQ), a new measure of glaucoma-related disability developed using the International Classification of Functioning and Disability (ICF) as a theoretical framework and tested in a sample representing the spectrum of glaucoma severity. METHODS:We used the ICF to code AGQ item content before mailing the AGQ to a UK sample (N = 1349) selected to represent people with a risk factor for glaucoma and people with glaucoma across a range of severity. Reflecting uncertainty in the theoretical framework (items with multiple ICF codes), an exploratory PCA was conducted. The theoretical structure informed our interpretation of the empirical structure and guided the selection of theoretically-derived factor labels. We also explored the discrimination of the AGQ across glaucoma severity groups. RESULTS:656 (49%) completed questionnaires were returned. The data yielded a 7-factor solution with a simple structure (using cut-off point of a loading of 0.5) that together accounted for 63% of variance in the scores. The mapping process resulted in allocation of the following theoretically-derived factor labels: 1) Seeing Functions: Participation; 2) Moving Around & Communication; 3) Emotional Functions; 4) Walking Around Obstacles; 5) Light; 6) Seeing Functions: Domestic & Social Life; 7) Mobility. Using the seven factor scores as independent variables in a discriminant function analysis, the AGQ scores resulted in correct glaucoma severity grading of 32.5% of participants (p < 0.001). CONCLUSIONS:This paper addresses a methodological gap in the application of classical test theory (CTT) techniques, such as PCA, in instrument development. Labels for empirically-derived factors are often selected intuitively whereas they can inform existing bodies of knowledge if selected on the basis of theoretical construct labels, which are more explicitly defined and which relate to each other in ways that are evidence based.

Project description:BackgroundPrincipal component analysis is used to summarize matrix data, such as found in transcriptome, proteome or metabolome and medical examinations, into fewer dimensions by fitting the matrix to orthogonal axes. Although this methodology is frequently used in multivariate analyses, it has disadvantages when applied to experimental data. First, the identified principal components have poor generality; since the size and directions of the components are dependent on the particular data set, the components are valid only within the data set. Second, the method is sensitive to experimental noise and bias between sample groups. It cannot reflect the experimental design that is planned to manage the noise and bias; rather, it estimates the same weight and independence to all the samples in the matrix. Third, the resulting components are often difficult to interpret. To address these issues, several options were introduced to the methodology. First, the principal axes were identified using training data sets and shared across experiments. These training data reflect the design of experiments, and their preparation allows noise to be reduced and group bias to be removed. Second, the center of the rotation was determined in accordance with the experimental design. Third, the resulting components were scaled to unify their size unit.ResultsThe effects of these options were observed in microarray experiments, and showed an improvement in the separation of groups and robustness to noise. The range of scaled scores was unaffected by the number of items. Additionally, unknown samples were appropriately classified using pre-arranged axes. Furthermore, these axes well reflected the characteristics of groups in the experiments. As was observed, the scaling of the components and sharing of axes enabled comparisons of the components beyond experiments. The use of training data reduced the effects of noise and bias in the data, facilitating the physical interpretation of the principal axes.ConclusionsTogether, these introduced options result in improved generality and objectivity of the analytical results. The methodology has thus become more like a set of multiple regression analyses that find independent models that specify each of the axes.

Dataset Information

A Neurodegenerative Disease Sleep Questionnaire: principal component analysis in Parkinson's disease.

Publications

A Neurodegenerative Disease Sleep Questionnaire: principal component analysis in Parkinson's disease.

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