Validity and Reliability of the Korean Version of the Health Information Technology Usability Evaluation Scale: Psychometric Evaluation
Bibliographic record
Abstract
BACKGROUND: Rigorous development of mobile technologies requires the use of validated instruments to evaluate the usability of these tools, which has become more relevant with the expansion of these technologies. Although various usability evaluation tools have been developed, there are relatively few simple evaluation instruments that have been validated across diseases and languages in mobile health (mHealth) information technology for use in multiple diseases. OBJECTIVE: The purpose of this study is to validate the Korean version of the Health Information Technology Usability Evaluation Scale (Korean Health-ITUES) and assess its applicability for different health conditions. METHODS: To develop the Korean Health-ITUES, we used a validation process involving the following 3 steps: (1) customization of the Health-ITUES for menstrual symptoms, (2) translation of the Health-ITUES from English into Korean, and (3) examination of the reliability and validity of the instrument. The translation process adhered to the World Health Organization (WHO) guidelines for translation and back-translation, expert review, and reconciliation. RESULTS: The Korean Health-ITUES showed reliable internal consistency with Cronbach α=.951; meanwhile, factor loadings of the 20 items in the 4 subscales ranged from 0.416 to 0.892. CONCLUSIONS: The Health-ITUES demonstrated reliability and validity for its use in assessing mHealth apps' usability in young Korean women with menstrual discomfort. Given the strong psychometric properties of this tool in Korean and English and across 2 different health conditions, the Health-ITUES is a valid and reliable instrument for assessing the usability of mHealth apps. The Health-ITUES is also a valid instrument for evaluating mHealth technologies, which are widely used by patients to self-manage their health and by providers to improve health care delivery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".