The Effect of Expectations on Experiences and Engagement with an Applied Game for Mental Health
Bibliographic record
Abstract
Objective: Applied games are considered a promising approach to deliver mental health interventions. Nonspecific factors such as expectations and motivation may be crucial to optimize effectiveness yet have not been examined so far. The current study examined the effect of expectations for improvement on (1) experienced fun and positive affect, and (2) in-game play behaviors while playing MindLight, an applied game shown to reduce anxiety. The secondary aim was to examine the moderating role of symptom severity and motivation to change. Materials and Methods: Fifty-seven participants (47 females; 17–21 years old) preselected on anxiety symptoms viewed a trailer in which MindLight was promoted as either a mental health or an entertainment game. These trailers were used to induce different expectations in participants. Participants subsequently played the game for 60 minutes. Before playing, participants filled out questionnaires about their general anxiety symptoms, motivation to change, state anxiety, affect, and arousal. While playing, in-game behaviors and galvanic skin response (GSR) were recorded continuously. After playing, state anxiety, affect, and arousal were measured again as well as experienced fun. Results: Participants in both trailer conditions showed increases in state anxiety, arousal, and GSR. Expectations did not influence experienced fun and positive affect, nor in-game behaviors. In addition, no moderation effects of motivation to change and symptom severity were found. Conclusion: Experiences and engagement with MindLight were not influenced by expectations, motivation to change, and symptom severity. For future research, it is recommended to examine individual differences in these effects, and long-term and more distal outcomes and processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".