Effectiveness and User Experience of a Coloring-based Serious Game for Chinese College Students with Alexithymia: Mixed Methods Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Alexithymia, characterized by difficulty identifying, describing, and expressing emotions, is prevalent among Chinese college students. Accessible, low-threshold digital tools are needed to help address this challenge. Coloring the Emoji, a coloring-based serious game, is such a tool designed to support emotion regulation for people with alexithymia. OBJECTIVE This study aimed to explore the effectiveness of the Coloring the Emoji game as a digital art therapy for emotion regulation and to examine the user experience of the game among Chinese college students with alexithymia. METHODS A mixed methods design was employed. The quantitative component included a 15-day intervention with 21 college students identified with elevated alexithymia. Pretest and posttest assessments used the 20-item Toronto Alexithymia Scale (TAS-20), with the Emotion Regulation Questionnaire (ERQ) and the Game Experience Questionnaire (GEQ) administered at posttest. The qualitative component consisted of semi-structured interviews conducted on day 1, day 7, and day 15, focusing on participants’ emotional engagement and user experiences with the coloring-based serious game. Statistical analyses comprised paired t tests with effect sizes (Cohen’s d), one-way ANOVAs, and linear regressions exploring demographic and behavioral predictors of changes in alexithymia, while qualitative data were thematically analyzed to provide complementary insights into intervention effectiveness and user experience. RESULTS A total of 21 participants (47.6% male, 52.4% female; mean age 22.3 years) completed the study. Alexithymia decreased significantly from pretest to posttest, both on the TAS-20 total score (ΔM=2.90; P=0.009) and on the Difficulty Identifying Feelings (DIF) subscale (ΔM=2.10; P=0.037). ERQ results indicated the improvement in emotion regulation, with higher cognitive reappraisal and lower expressive suppression than normative levels. GEQ ratings suggested positive engagement with low stress. Regression analysis showed that the number of coloring works predicted improvements in identifying emotions, whereas demographics and total playtime did not. Qualitative interviews reinforced these findings: participants consistently described coloring as calming and focus-enhancing, helping them to recognize emotional states and release negative feelings. Daily engagement was said to heighten emotional awareness and gradually make expression more habitual. Additionally, participants noted limitations in template variety and color-emotion mapping and emphasized the need for greater personalization, expanded creative options, and social functions to maintain motivation and long-term use. CONCLUSIONS This study provides evidence that a coloring-based serious game can enhance emotion regulation in Chinese college students with alexithymia, showing a significant reduction in TAS-20 total scores alongside a positive, low-tension user experience. Findings highlight the potential of coloring-based digital art therapy to support emotion regulation in populations facing challenges in emotional barriers.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".