A Serious Game to Improve Emotion Regulation in Treatment-Seeking Individuals With Gambling Disorder: A Usability Study
Bibliographic record
Abstract
Background: Serious games have shown positive results in increasing motivation, adherence to treatment and strengthening the therapeutic alliance in multiple psychiatric disorders. In particular, patients with impulse control disorders and other disorders in which the patient suffers from inhibitory control deficits (e.g., behavioral addictions) have been shown to benefit from serious games. Aim: The aim of this study was to describe the characteristics and to evaluate the usability of a new serious videogame, e-Estesia. This serious videogame was designed to improve emotion regulation in patients with gambling disorder (GD). Preliminary results from a pilot sample are also reported. Method: A pilot sample of 26 patients undergoing treatment for GD was recruited (ranging from 22 to 74 years, mean = 41.2 and SD = 12.9; 80.8% men). Participants used e-Estesia on a tablet, which was connected to a thoracic band that sent heart rate (HR) and heart rate variability (HRV) data to the videogame platform in order to provide biofeedback. The System Usability Scale was completed by patients to determine the usability of e-Estesia. Results and Discussion: e-Estesia performed comparatively well for all the explored groups (i.e., sex, age, and online vs. offline gambling: mean usability score = 83.8, SD = 13.1). Around 84.6% of the patients endorsed that it was easy to use. Female patients with GD presented higher HRV during the use of the serious videogame compared to men.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, 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".