The simplification of the insomnia severity index and epworth sleepiness scale using machine learning models

Psychometric Properties of the Insomnia Severity Index and Its Comparison With the Shortened Versions Among the General Population

OBJECTIVE

The aim of this study was to explore the psychometric properties of the Insomnia Severity Index (ISI) based on modern test theory, such as item response theory (IRT) and Rasch analysis, with shortened versions of the ISI among the general population.

METHODS

We conducted two studies to evaluate the reliability and validity of the shortened versions of the ISI in a Korean population. In Study I, conducted via online survey, we performed an exploratory factor analysis (n=400). In Study II, confirmatory factor analysis (CFA) was conducted (n=400). IRT and Rasch analysis were performed on all samples. Participants symptoms were rated using the ISI, Dysfunctional Beliefs and Attitudes about Sleep-16 items, Dysfunctional Beliefs about Sleep-2 items, Patient Health Questionnaire-9 items, and discrepancy between desired time in bed and desired total sleep time.

RESULTS

CFA showed a good fit for the 2-factor model of the ISI (comparative fit index=0.994, Tucker-Lewis index=0.990, root-meansquare-error of approximation=0.039, and standardized root-mean-square residual=0.046). The 3-item versions also showed a good fit for the model. All scales showed good internal consistency reliability. The scale information curve of the 2-item scale was similar to that of the full-scale ISI. The Rasch analysis outputs suggested a good model fit.

CONCLUSION

The shortened 2-factor ISI is a reliable and valid model for assessing the severity of insomnia in the Korean population. The results are needed to be explored further among the clinical sample of insomnia.

Developing a Short-Form Buss-Warren Aggression Questionnaire Based on Machine Learning

For adolescents, high levels of aggression are often associated with suicide, physical injury, worsened academic performance, and crime. Therefore, there is a need for the early identification of and intervention for highly aggressive adolescents. The Buss-Warren Aggression Questionnaire (BWAQ) is one of the most widely used offensive measurement tools. It consists of 34 items, and the longer the scale, the more likely participants are to make an insufficient effort response (IER), which reduces the credibility of the results and increases the cost of implementation. This study aimed to develop a shorter BWAQ using machine learning (ML) techniques to reduce the frequency of IER and simultaneously decrease implementation costs. First, an initial version of the short-form questionnaire was created using stepwise regression and an ANOVA F-test. Then, a machine learning algorithm was used to create the optimal short-form questionnaire (BWAQ-ML). Finally, the reliability and validity of the optimal short-form questionnaire were tested using independent samples. The BWAQ-ML contains only four items, thirty items less than the BWAQ, and its AUC, accuracy, recall, precision, and F1 score are 0.85, 0.85, 0.89, 0.83, and 0.86, respectively. BWAQ-ML has a Cronbach's alpha of 0.84, a correlation with RPQ of 0.514, and a correlation with PTM of -0.042, suggesting good measurement performance. The BWAQ-ML can effectively measure individual aggression, and its smaller number of items improves the measurement efficiency for large samples and reduces the frequency of IER occurrence. It can be used as a convenient tool for early adolescent aggression identification and intervention.