JMIR Publications

ABSTRACT

Background:

Digital health competence is increasingly recognized as a core competence for healthcare professionals. A comprehensive evaluation of knowledge, skills, performance, values, and attitudes necessary to adapt to evolving digital health technologies is essential. The DigiHealthCom is a well-established instrument designed to assess digital health competence across diverse healthcare professionals.

Objective:

This study aimed to translate and culturally adapt the DigiHealthCom into a simplified Chinese (Mandarin) and verify its reliability and validity in assessing digital health competence of Chinese healthcare professionals.

Methods:

The DigiHealthCom was translated into Chinese following the guideline proposed by its original developers (Jarva and colleagues). The cultural adaptation involved expert review and cognitive interviewing. Internal consistency, test-retest reliability, content validity, convergent validity, discriminant validity, and factor structure were examined. Item analysis tested item discrimination, item correlation, and item homogeneity. Internal consistency was assessed using Cronbach alpha, and test-retest reliability was measured by intraclass correlation coefficient. Content validity was assessed through both item and scale content validity indices. Convergent validity was measured by the Average Variance Extracted and Composite Reliability, while discriminant validity was measured by the Heterotrait-Monotrait Ratio. A five-dimension model of the DigiHealthCom was confirmed using confirmatory factor analysis.

Results:

The finalized Chinese version of the DigiHealthCom was completed after addressing differences between the back-translations and the original version. No discrepancies affecting item clarity were reported during cognitive interviewing. The validation process involved 398 eligible healthcare professionals from 36 cities across 15 provinces in China, with 43 participants undergoing a retest after a 2-week interval. Critical ratio values (Range: 16.05-23.77, P<.001), item-total correlation coefficients (Range: 0.69-0.89), and Cronbach alpha if the item deleted (Range: 0.91-0.96) indicated satisfactory item discrimination, item correlation, and item homogeneity. Cronbach alpha for dimensions and the scale ranged from 0.93 to 0.98, indicating good internal consistency. Intraclass correlation coefficient was 0.90 (95% confidence interval 0.81-0.95), indicating good test-retest reliability. Item content validity index ranged from 0.82 to 1.00, and the scale content validity index was 0.97, indicating satisfactory content validity. Convergent validity (average variance extracted: 0.60-0.79; composite reliability: 0.94-0.95) and divergent validity (Heterotrait-Monotrait Ratio: 0.72-0.89) were satisfactory. Confirmatory factor analysis confirmed a well-fit five-dimension model (robust chi-square to degrees-of-freedom ratio= 3.10, comparative fit index= 0.91, Tucker-Lewis index= 0.90, incremental fit index= 0.91, root mean square error of approximation= 0.07, standardized root mean squared residual= 0.05), with each item having a factor loading exceeding 0.40.

Conclusions:

The Chinese version of the DigiHealthCom has been proved to be reliable and valid. It is now available for assessing digital health competence among Chinese healthcare professionals. This assessment can be used to guide healthcare policymakers and educators in designing comprehensive and implementable educational programs and interventions.