Jung Pil Park
Research Subjects
Research Interests
Jung Pil Park has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Bon Hoon Koo
Department of Psychiatry, College of Medicine, Yeungnam University, Daegu 42415, Korea
1 shared publication - Dai Seg Bai
Department of Health and Medical Counselling and Welfare, Kyungwoon University, Gumi, Korea
1 shared publication - Dong Hoon Cho
Corporate Research Institute of Clupea, Inc., Daegu 41585, Korea
1 shared publication - Gi Hwan Kim
Corp Res Inst Clupea Inc, Daegu, South Korea
1 shared publication - Hye-Geum Kim
Department of Psychiatry, Yeungnam University Hospital, Daegu, Korea
1 shared publication - Hyun Seok Jeong
Division of Clinical Psychology, Department of Psychiatry, Yeungnam University, Daegu 42415, Korea
1 shared publication
Latest Publications
- Open Access
- 26 February 2020
- Published by MDPI AG in Applied Sciences
- Vol. 10 (5) , 1579
A Study of the Effectiveness Verification of Computer-Based Dementia Assessment Contents (Co-Wis): Non-Randomized Study
Computer-based neuropsychological assessments have many advantages over traditional neuropsychological assessments. However, limited data are available on the validity and reliability of computer-based assessments. The purpose of this study was to examine the reliability and validity of computer-based dementia assessment contents (Co-Wis). This study recruited 113 participants from Yeungnam University Medical Center in Daegu from June 2019 to December 2019 and received ethical approval. Participants were evaluated using standard and objective dementia cognitive test tools such as the Korean version of the Mini-Mental State Examination (K-MMSE), the Clinical Dementia Rating Scale (CDR), and the Standardized Seoul Neuropsychological Screening Battery-II (SNSB-II). To verify the effectiveness of Co-Wis, the concurrent validity, test–retest reliability (Pearson’s correlation coefficients), construct validity (Factor analysis), and signal detection analysis (ROC curve) were used. In most of the Co-Wis subtests, the concurrent validity and test–retest reliability showed statistically significant correlations (p < 0.05, p < 0.01). The factor analysis showed that Co-Wis assessed the most major cognitive areas (Tucker–Lewis Index (TLI) = 0.876, Comparative Fit Index (CFI) = 0.897, RMSEA = 0.88). Thus, Co-Wis appears clinically applicable and with high reliability and validity. In the future, we should develop tests to evaluate both standard data and big data-based machine learning.
- 26 February 2020
- Published by MDPI AG in Applied Sciences
- Vol. 10 (5) , 1579
Open AccessA Study of the Effectiveness Verification of Computer-Based Dementia Assessment Contents (Co-Wis): Non-Randomized Study
Computer-based neuropsychological assessments have many advantages over traditional neuropsychological assessments. However, limited data are available on the validity and reliability of computer-based assessments. The purpose of this study was to examine the reliability and validity of computer-based dementia assessment contents (Co-Wis). This study recruited 113 participants from Yeungnam University Medical Center in Daegu from June 2019 to December 2019 and received ethical approval. Participants were evaluated using standard and objective dementia cognitive test tools such as the Korean version of the Mini-Mental State Examination (K-MMSE), the Clinical Dementia Rating Scale (CDR), and the Standardized Seoul Neuropsychological Screening Battery-II (SNSB-II). To verify the effectiveness of Co-Wis, the concurrent validity, test–retest reliability (Pearson’s correlation coefficients), construct validity (Factor analysis), and signal detection analysis (ROC curve) were used. In most of the Co-Wis subtests, the concurrent validity and test–retest reliability showed statistically significant correlations (p < 0.05, p < 0.01). The factor analysis showed that Co-Wis assessed the most major cognitive areas (Tucker–Lewis Index (TLI) = 0.876, Comparative Fit Index (CFI) = 0.897, RMSEA = 0.88). Thus, Co-Wis appears clinically applicable and with high reliability and validity. In the future, we should develop tests to evaluate both standard data and big data-based machine learning.