Coloring-based Serious Game in Chinese College Students with Alexithymia: A Prospective Longitudinal Exploratory Mixed-Methods Study (Preprint)
Bibliographic record
Abstract
Abstract Background Alexithymia, characterized by difficulty identifying, describing, and expressing emotions, is prevalent among Chinese college students. Traditional art therapies face barriers, such as high psychological thresholds and limited accessibility. Accessible, low-threshold digital tools are needed to help address this challenge. Coloring the Emoji, a coloring-based serious game designed as a digital art therapy, offers a low-threshold tool to support emotion regulation for this population. Objective This study aimed to evaluate the effectiveness of Coloring the Emoji as a digital art therapy in improving alexithymia symptoms and examine the user experience of the game, thereby providing empirical support for the application of coloring-based digital art therapy in alexithymia intervention. Methods A prospective, exploratory convergent parallel mixed methods longitudinal design was used, involving a 15-day daily game intervention with Chinese college students exhibiting alexithymic tendencies. Participants were purposively sampled from 332 prescreened students. Eligibility criteria included age 18‐30 years, basic smartphone operation, and no ongoing psychotherapy or psychiatric medication. In total, 20 participants completed the intervention. Quantitative assessments used the 20-item Toronto Alexithymia Scale to measure alexithymia and the Game Experience Questionnaire to assess user experience. Statistical analyses comprised Wilcoxon signed-rank tests for pre-post comparisons, Mann-Whitney U tests for sex, education, and major comparisons, and Kruskal-Wallis H tests for 20-item Toronto Alexithymia Scale pretest severity comparisons. The qualitative component consisted of 3 longitudinal semistructured interviews conducted across the intervention period (days 1, 8, and 15), analyzed using Colaizzi’s phenomenological method and thematic analysis to explore emotion regulation dynamics, user experience, and in-depth mechanisms underlying intervention changes. Results Total TAS-20 scores decreased significantly post intervention ( Z =2.425; P =.02), with a median reduction of 3.00 points (95% CI 1.00‐4.50, 5000 bootstrap replications). The Difficulty Identifying Feelings subscale showed a marginal improvement ( Z =1.826; P =.07). Males exhibited significantly greater Difficulty Identifying Feelings reduction ( Z =−2.137; P =.03), and participants with severe baseline alexithymia showed greater improvement in Externally Oriented Thinking (H=8.233; P =.02). Game Experience Questionnaire scores indicated high competence and sensory and imaginative immersion. Qualitative results confirmed 2 major themes and 10 subthemes; visual coloring practice helped participants externalize vague emotions, differentiated engagement habits explained subgroup discrepancies, and low-pressure interaction supported sustainable emotional regulation. This study further integrated quantitative and qualitative outcomes through subgroup comparative analysis, revealing distinct intervention response patterns across sex and alexithymia severity. Conclusions Coloring the Emoji is an effective, acceptable intervention for improving emotional identification in Chinese college students with alexithymia. Its innovation lies in a localized low-threshold design that meets daily emotional needs. Unlike mainstream Western interventions emphasizing open emotional expression and social sharing, this tool adapts to the reserved emotional disclosure norms of Chinese college students. It advances the field by supplementing culturally contextualized evidence for nonverbal digital art therapy. As a low-stress, demedicalized solution, it provides a scalable, feasible supplement to traditional mental health services for campus emotional regulation.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".