|
Affiliation
|
Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand. |
|
Abstract
|
INTRODUCTION: The Stay Independent Brochure (SIB) is a widely used fall-risk self-assessment tool, which is part of the Stopping Elderly Accident, Deaths & Injuries (STEADI) program in the US. However, the validity and reliability of the SIB have not been established in an elderly Thai population.
OBJECTIVE: To construct a fall risk screening tool based on the SIB in a Thai elderly population and investigate its psychometric effect in a community context.
METHODS: A total of 480 elderly participants volunteered to take part in this study from the Nakhon Ratchasima province. In the first part of the study, the original version of the SIB was translated into Thai (total 12 questions) and adapted into a modified version (total 18 questions). The translated SIBs were cross-culturally adapted and tested for content validity, test-retest reliability, inter-rater reliability, construct validity and internal consistency. In the second part of the study, the psychometric properties of the translated SIBs were assessed using test-retest and inter-rater reliability and content and construct validity.
RESULTS: The SIBs had good content validity (IOC: 0.80 to 1.00), and the interclass correlation coefficient (ICC) of test-retest and inter-rater reliability was excellent for both SIB versions (ICC 0.89-0.95). The construct validity of 18 questions was tested by principal component factor analysis with varimax rotation and using factor loading greater than 0.4, and yielded 6 factors that explained 59.1% of the variance in fall risk (more than 12 questions). The coefficient alpha was higher than the usually recommended value of 0.70 for the total score of both SIB versions. The convergent validity between the TUG and BBS tests was statistically significant (p<0.001).
CONCLUSION: Based on psychometric properties, it is recommended that the two Thai versions of the SIB are an appropriate initial screening tool for the multi-steps fall risk assessment algorithm in predicting falls in an elderly Thai community.
© 2019 Loonlawong et al.
Language: en |