Adaptation and validation of the Chinese version of the digital addiction scale for children (DASC)

Abstract

Background: With more than 90% penetration of personal digital devices, digital addiction in children has emerged as a significant concern in China. Environmental and socioeconomic stressors in China-a highly collectivist society-may contribute to a higher prevalence of digital addiction. However, there is a lack of culturally adapted tools to assess digital addiction among children in China.

Objectives: This study aimed to: (1) linguistically and culturally adapt the Digital Addiction Scale for Children (DASC) to the Chinese context; (2) examine its psychometric properties, including validity (i.e., construct and convergent validity) and reliability (i.e., internal consistency and test-retest reliability); and (3) establish a potential cut-off score for identifying children at risk of digital addiction.

Methods: The DASC was translated into Chinese and adapted following forward translation, back translation, harmonization and pilot testing with 24 students in grades four to eight to ensure conceptual and semantic equivalence, clarity and cultural relevance. The final Chinese DASC consists of 24 items after excluding item 11. This study employed a cross-sectional design to validate the Chinese version of the DASC. Based on convenience sampling, Nanling County in Anhui Province, China was selected as the study site. Six schools were then chosen using a stratified randomised cluster sampling method, with three strata: urban, peri-urban, and rural areas. Given the feasibility of completing questionnaires independently, students in grades five to eight (aged 12-16 years old) from primary and secondary schools were invited. One class of students in each grade in each selected school was randomly invited to participate. To assess the psychometric properties of the DASC, both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted using SPSS 30.0 and AMOS 28.0, respectively. Convergent validity was evaluated using Pearson correlation coefficients between the DASC and Young's Internet Addiction Test (IAT). Reliability was assessed using Cronbach's alpha for internal consistency, split-half reliability, and test-retest reliability over a two-week interval. Receiver operating characteristic (ROC) curve analysis was conducted to determine the optimal cut-off score, using the IAT as a reference criterion for identifying at-risk individuals.

Results: After forward translation, back translation, harmonization and pilot testing, the English version of the DASC was adapted into Chinese. In total, 592 students (age 12.8 ± 1.7 years; 285 girls and 307 boys) participated in the validation study. The 24-item Chinese version of DASC was validated, with item 11 excluded due to cross-loading. Two components (i.e., interpersonal and intrapersonal dimensions) were identified for the Chinese version of the DASC based on exploratory and confirmatory factor analysis (RMSEA = 0.06, CFI = 0.94). The interpersonal dimension includes 19 items related to conflict, problems, displacement, deception, withdrawal and relapse. The intrapersonal dimension comprises 5 items related to mood modification and the perceived importance of digital devices. The DASC demonstrated acceptable convergent validity (r = 0.83 [95% confidence interval (CI) 0.80, 0.85], p < 0.001), internal consistency reliability (Cronbach's alpha 0.95; split-half reliability coefficient 0.885) and test-retest reliability (intraclass correlation coefficient = 0.71 (0.67, 0.75), p < 0.001). Using the IAT as the criterion, the suggested cut-off score for the risk of digital addiction was 53, yielding a sensitivity of 85.9% and specificity of 92.3%.

Conclusion: The linguistically and culturally adapted Chinese version of the DASC is a reliable and valid instrument, with an established cut-off score for identifying at-risk individuals. However, the results are limited by the use of a single-county sample, the exclusion of younger children (grade 4 and below) and those with cognitive or reading difficulties, and reliance on self-reported data. When appropriately generalized, the Chinese version of the DASC has potential applications in routine school mental health screening, paediatric check-ups to identify at-risk children and national health surveys. Tracking and understanding digital addiction using the DASC enables the development of evidence-based interventions and supports policy recommendations to address digital addiction.

Keywords: Adaptation; Children; China; Digital addiction; Internet addiction; Validation.

Fig. 1

Flowchart of the study design

Fig. 2

Scree plot of eigenvalues. Notes: The x-axis represents the number of components, while the y-axis displays the corresponding eigenvalues. As shown, there is a clear inflection point—or "elbow"—after the second component, indicating that only the first two components have eigenvalues greater than 1.0, consistent with Kaiser’s criterion. This sharp decline in eigenvalues suggests that the first two components account for the most meaningful variance in the data, while subsequent components contribute minimally and are likely to reflect random noise or less interpretable patterns

Fig. 3

The receiver operator characteristic curve of the DASC with IAT as criteria (n = 592) Notes: The ROC curve plots sensitivity (true positive rate) on the y-axis against 1 - specificity (false positive rate) on the x-axis. The blue curve represents the relationship between sensitivity and 1 - specificity across various cutoff points. The area under the curve (AUC) quantifies the overall ability of the cutoff points to discriminate between classes; a larger area indicates better discriminatory power. The red diagonal line is a reference line that represents the performance of a random classifier—one that predicts outcomes with no discriminatory ability