Gamified Digital Mental Health Interventions for Young People: Scoping Review of Ethical Aspects During Development and Implementation
Bibliographic record
Abstract
BACKGROUND: Young people are particularly at risk of developing mental health problems, a challenge exacerbated by the COVID-19 pandemic. Digital tools such as apps and chatbots show promise in providing accessible, cost-effective, and less stigmatized ways of strengthening their mental health. However, while these interventions offer benefits, they extend mental health measures beyond traditional therapeutic settings and relationships, which raises ethical concerns due to the absence of established guidelines and regulations. This is particularly notable for technologies incorporating serious gaming elements. In addition, adolescents are in a sensitive and at times vulnerable phase, which shows great potential for the effective use of preventive and sensitizing mental health measures. Considering the lack of an integration into existing mental health structures among many young users, ethical considerations become crucial. OBJECTIVE: This scoping review aims to build a knowledge base on the ethical aspects of developing and implementing gamified digital mental health interventions for young people. METHODS: We conducted a search on research articles and conference papers from 2015 to 2023 in English, German, and Spanish. We identified 1815 studies using a unique combination of keywords in the databases Scopus, Web of Science, MEDLINE, and PsycINFO. After removing duplicates (741/1816, 40.8%), we included a total of 38 publications in this review following a double screening process. RESULTS: This review found that ethically relevant aspects were discussed with regard to (1) research ethics, (2) ethical principles (including privacy, accessibility, empowerment and autonomy, cultural and social sensitivity, and co-design), (3) vulnerable groups, and (4) social implications (including implementation using facilitators in specific social contexts, relationship with other therapeutic options, economic aspects, and social embeddedness of technologies). CONCLUSIONS: This scoping review identified a prevailing limited interpretation of "ethics" as research ethics across the included publications. It also shows a lack of discussion on the social embeddedness of technologies and that co-design is frequently viewed in instrumental terms and vulnerability is mostly addressed pragmatically. Through providing concrete examples of how mental health researchers and game designers thus far have addressed and mitigated ethical challenges in specific interventions, this review illustrates how ethical issues do or do not prompt diverse reflections, mitigation strategies, and actions. It advocates for ethics to be integrated as an ongoing practice throughout all stages of developing and implementing serious game elements in mental health interventions for young people.
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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 | 0.089 | 0.248 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".