Psychometric properties of a Korean version of the pre-sleep arousal scale

by Namhee Kim, Bo Gyeong Lee

Purpose

Sleep quality is a multidimensional construct encompassing the effectiveness and restorativeness of sleep. The pre-sleep arousal scale is a widely used instrument for evaluating aspects of arousal that are closely related to sleep quality. This study aimed to evaluate the psychometric properties of the Korean version of the pre-sleep arousal scale (K-PSAS).

Methods

We performed a secondary analysis of cross-sectional data from 286 adults aged 19–70 years who used electronic cigarettes or heated tobacco products. The original PSAS was translated into Korean, with content validity assessed by experts. Construct validity was evaluated via exploratory factor analysis, and concurrent validity was assessed by correlating the K-PSAS with the Insomnia Severity Index, Pittsburgh Sleep Quality Index, and Hospital Anxiety and Depression Scale. Reliability was examined using Cronbach’s α, and split-half reliability coefficient.

Results

Both the item-level content validity index for all items and the scale-level content validity index average for the K-PSAS-16 were 1.0. After removing the survey item on “being mentally alert and active at bedtime” (item 13) due to low factor loading, the K-PSAS-15 demonstrated a two-factor structure, with somatic and cognitive arousal factors explaining 42.36% and 10.19% of the variance, respectively. A significant positive correlation was observed between the two factors (ρ = 0.61, p  Conclusion

The K-PSAS-15, which excludes one poorly performing item from the original scale, is a reliable and valid tool for assessing pre-sleep arousal.

Advancing fall risk prediction in older adults with cognitive frailty: A machine learning approach using 2-year clinical data

by Catherine Park, Namhee Kim, Miji Kim, Chang Won Won, Beom-Chan Lee

Falls are a critical concern in older adults with cognitive frailty (CF). However, previous studies have not fully examined whether machine learning models can predict falls in older individuals with CF. The 2-year longitudinal data set from the Korean Frailty and Aging Cohort Study and machine learning approach were utilized to predict fall risk. We analyzed multidimensional health data, including demographics, clinical conditions, as well as the physical and psychological health factors of 443 older adults with CF identified out of 2,404 older adults. For fall risk prediction, we developed a machine learning framework incorporating logistic regression, bootstrapping, and recursive feature elimination. Statistical analysis revealed significant differences between the non-faller and faller groups for nine clinical conditions as well as physical and psychological variables. Using nine significant variables, our machine-learning-based model demonstrated good predictive performance with an area under the curve (AUC) exceeding 80%. Furthermore, our machine learning framework identified four optimal variables: the number of Fried physical frailty (PF) phenotypes, PF-Mobility scores, scores from the Korean version of the Short Geriatric Depression Scale, and scores from SARC-F (consisting of five components: strength, assistance with walking, rising from a chair, climbing stairs, and experiencing falls). It demonstrated excellent predictive performance, with an AUC, sensitivity, specificity, and accuracy exceeding 95%. These variables reflect the critical association between physical and psychological health and fall risk. These findings underscore the importance of integrating multidimensional health data with machine learning methodologies to accurately predict fall risk in older adults with CF, design targeted interventions, and enable healthcare professionals to implement strategies to reduce and prevent such falls.