ABSTRACT
OBJECTIVE: The purpose of the study was to validate the Chinese (Singapore) version of the Parkinson's Disease Questionnaire (PDQ-39CSV) and its briefer version (PDQ-8CSV). METHODS: A convenience sample of Chinese-speaking Singaporeans with Parkinson's disease (PD) (n = 63) completed a questionnaire containing the PDQ-39CSV and the Chinese (Singapore) EQ-5D. A subgroup also participated in a retest and/or a focus group discussion. A priori hypotheses were tested by examining correlations between PDQ-39CSV, PDQ-8CSV and EQ-5D scores and using principal component factor analysis. Reliability was assessed using Cronbach's alpha and intra-class correlation coefficients (ICC). RESULTS: Thirty-two PDQ-39CSV items correlated satisfactorily with their hypothesized dimensions (Spearman's p > or = 0.4). Factor analysis yielded a component on which all 8 PDQ-39CSV dimensions were substantially loaded (loading range: 0.53-0.89). As hypothesized, the PDQ-39CSV and PDQ-8CSV summary indices were highly correlated (Pearson's r:0.95, ICC:0.94); correlations between related PDQ and EQ-5D scores were generally strong (Spearman's p: 0.38-0.76, p < 0.001 for all). Cronbach's alpha values ranged from 0.64 to 0.90 and ICC values from 0.66 to 0.86. CONCLUSION: This study provides preliminary evidence supporting validity and reliability of both the PDQ-39CSV and its briefer version.
Subject(s)
Parkinson Disease/physiopathology , Quality of Life , Surveys and Questionnaires , Adult , Aged , Aged, 80 and over , China , Female , Humans , Male , Middle Aged , Parkinson Disease/psychologyABSTRACT
This paper defines the restricted growing concept (RGC) fur object separation and provides an algorithmic analysis of its implementations. Our concept decomposes the problem of object separation into two stages. First, separation is achieved by shrinking the objects to their cores while keeping track of their originals as masks. Then the core is grown within the masks obeying the guidelines of a restricted growing algorithm. In this paper, we apply RGC to the remote sensing domain, particularly the synthetic aperture radar (SAR) sea ice images.
