A Serious Game for Patients With Eating Disorders (Maze Out): Pilot User Experience and Acceptance Study
Bibliographic record
Abstract
BACKGROUND: Eating disorders (EDs) are severe mental disorders associated with notable impairments in the quality of life. Despite the severity of the disorders and extensive research in the field, effective treatment for EDs is lacking. Digital interventions are gaining an evidence-based position in mental health, providing new perspectives in psychiatric treatment. Maze Out is a serious game coproduced by patients and therapists that focuses on supporting patients with EDs. OBJECTIVE: The aim of this study was to investigate the experiences of engaging in and acceptability of Maze Out among patients with EDs and therapists. METHODS: This study is a qualitative pilot study involving data collected through focus groups and individual interviews and user analytics collected through the game. The participants were recruited from the Odense Mental Health Service of the Region of Southern Denmark. Qualitative interviews analyzed by thematical analysis and interpreted by interpretative phenomenological analysis were used to evaluate the acceptance and experience of Maze Out among patients and therapists. The mobile health evidence reporting and assessment checklist was used to describe the content, context, and technical features of the game in a standardized manner for mobile health apps. RESULTS: The participants found Maze Out to be engaging, easy to use, and a good platform for reflecting on their disorder. They primarily used Maze Out as a conversational tool with their close relationships, giving them insights into the experiences and daily life struggles of someone with EDs. CONCLUSIONS: Maze Out seems to be a promising tool supplementing the current ED treatment. Further research should focus on evaluating the effectiveness of the game and its potential to support patients with different types of EDs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".