Dysfunctional Beliefs and Attitudes about Sleep-6 (DBAS-6): Data-driven shortened version from a machine learning
Hyeontae Jo1, Hong Jun Jeon2, Junseok Ahn3
1Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea; Division of Applied Mathematical Sciences, Korea University, Sejong, 30019, Republic of Korea.
A new six-item scale (DBAS-6) effectively predicts dysfunctional beliefs about sleep, shortening the original 16-item scale. This validated tool offers a practical clinical assessment for sleep-related cognition.
Area of Science:
- Sleep research
- Cognitive psychology
- Psychometrics
Background:
- The Dysfunctional Beliefs and Attitudes about Sleep Scale (DBAS-16) is a key tool for sleep cognition assessment.
- Its length poses challenges for efficient clinical application.
Purpose of the Study:
- To develop a data-driven, shortened version of the DBAS-16.
- To create a tool that efficiently predicts total scores of the original scale in the general population.
Main Methods:
- Utilized Exploratory Factor Analysis (EFA) on 1000 DBAS-16 responses.
- Employed eXtreme Gradient Boosting (XGBoost) to identify items with high predictive power.
- Selected the most representative item from each of six identified EFA groups.
Main Results:
- Developed the six-item DBAS-6, a shortened version of the DBAS-16.
- DBAS-6 achieved a high R-squared value of 0.90 in predicting DBAS-16 total scores.
- The new scale demonstrated robust predictive power across diverse datasets.
Conclusions:
- The DBAS-6 provides a concise and accurate method for assessing dysfunctional sleep beliefs.
- This shortened scale is a practical tool for clinical settings, improving efficiency in sleep cognition assessment.
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