Mapping the World Health Organization Disability Assessment Scale 2.0 to the EQ-5D-5L in patients with mental disorders.

OBJECTIVE: The current study aims to develop an algorithm for mapping the WHODAS 2.0 to the EQ-5D-5 L for patients with mental disorders. METHODS: This cross-sectional study was conducted at the Institute of Mental Health and Community Wellness Clinics in Singapore between June 2019 and November 2022. We included four regression methods including the Ordinary Least Square (OLS) regression, the Tobit regression model (Tobit), the robust regression with MM estimator (MM), and the adjusted limited dependent variable mixture model (ALDVMM) to map EQ-5D-5 L utility scores from the WHODAS 2.0. RESULTS: A total of 797 participants were included. The mean EQ-5D-5 L utility and WHODAS 2.0 total scores were 0.615 (SD = 0.342) and 11.957 (SD = 8.969), respectively. We found that the EQ-5D-5 L utility score was best predicted by the robust regression model with the MM estimator. Our findings suggest that the WHODAS 2.0 total scores were significantly and inversely associated with the EQ-5D-5 L utility scores. CONCLUSION: This study provides a mapping algorithm for converting the WHODAS 2.0 scores into EQ-5D-5 L utility scores which can be implemented using a simple online calculator in the following web application: https://eastats.shinyapps.io/whodas_eq5d/.

Equipercentile linking of the Sheehan Disability Scale and the World Health Organization Assessment Schedule 2.0 scales in people with mental disorders.

BACKGROUND: The Sheehan Disability Scale (SDS) and the World Health Organization Disability Assessment Scale (WHODAS 2.0) have been widely used to measure functional impairment and disability. To ensure that the scores from these two scales are practically exchangeable across diseases, therapies, and care programmes, the current study aimed to examine the linkage of the WHODAS 2.0 with the SDS and develop a simple and reliable conversion table for the two scales in people with mental disorders. METHODS: A total of 798 patients (mean age = 36.1, SD = 12.7) were recruited from outpatient clinics of the Institute of Mental Health, and the Community Wellness Clinic in Singapore. Using a single-group design, an equipercentile equating method with log-linear smoothing was used to establish a conversion table from the SDS to the WHODAS 2.0 and vice versa. RESULTS: The conversion table showed that the scores were consistent for the entire range of scores when the scores were converted either from the SDS to the WHODAS 2.0 or from the WHODAS 2.0 to the SDS. The agreement between the WHODAS 2.0's raw and converted scores and SDS's raw and converted scores were interpreted as good with intraclass correlation coefficient of 0.711 and 0.725, respectively. CONCLUSION: This study presents a simple and reliable method for converting the SDS scores to the WHODAS 2.0 scores and vice versa, enabling interchangeable use of data across these two disability measures.

Working Adults' Intentions to Participate in Microlearning: Assessing for Measurement Invariance and Structural Invariance.

The current study set out to understand the factors that explain working adults' microlearning usage intentions using the Decomposed Theory of Planned Behaviour (DTPB). Specifically, the authors were interested in differences, if any, in the factors that explained microlearning acceptance across gender, age and proficiency in technology. 628 working adults gave their responses to a 46-item, self-rated, 5-point Likert scale developed to measure 12 constructs of the DTPB model. Results of this study revealed that a 12-factor model was valid in explaining microlearning usage intentions of all working adults, regardless of demographic differences. Tests for measurement invariance showed support for invariance in model structure (configural invariance), factor loadings (metric invariance), item intercepts (scalar invariance), and item residuals (strict invariance) between males and females, between working adults below 40 years and above 40 years, and between working adults with lower technology proficiency and higher technology proficiency levels. While measurement invariance existed in the data, structural invariance was only found across gender, not age and technology proficiency. We then assessed latent mean differences and structural path differences across groups. Our findings suggest that a tailored approach to encourage the use of microlearning is needed to suit different demographics of working adults. The current study discusses the implications of the findings on the use and adoption of microlearning and proposes future research possibilities.