Effectiveness and User Experience of a Coloring-based Serious Game for Chinese College Students with Alexithymia: Mixed Methods Study (Preprint)
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».