Leveraging Swipe Gesture Interactions From Mobile Games as Indicators of Anxiety and Depression: Exploratory Study
Bibliographic record
Abstract
BACKGROUND: Anxiety and depression are serious mental health conditions affecting millions of people worldwide; however, they are often underdiagnosed due to limited health care resources. Mobile games, with their widespread popularity and availability, offer a unique opportunity to use user-game interaction data for mental health screening. OBJECTIVE: This study aimed to explore whether swipe gesture interactions from mobile games can serve as indicators of anxiety and depression symptoms. METHODS: A total of 82 participants played 3 casual mobile games (puzzle, infinite runner, and object slicing games) for 15 minutes each and completed validated measures of anxiety (Generalized Anxiety Disorder-7; GAD-7) and depression (Patient Health Questionnaire-8; PHQ-8). Data were logged for each swipe event, and metrics were computed using statistical measures, yielding roughly 150 metrics per game. Spearman rank correlations were calculated between each metric and GAD-7 and PHQ-8 scores. RESULTS: Multiple swipe gesture metrics showed significant associations with both anxiety and depression scores. For the puzzle game, mean swipe speed correlated with PHQ-8 (ρ=-0.405; P<.001) and GAD-7 (ρ=-0.400; P<.001) scores. For the infinite runner game, mean variance in swipe end pressure showed moderate to strong negative correlation with PHQ-8 (ρ=-0.405; P<.001) and GAD-7 (ρ=-0.309; P=.007) scores. In the object slicing game, minimum swipe start position along the y-axis correlated positively with PHQ-8 (ρ=0.368; P<.001) and GAD-7 (ρ=0.370; P<.001) scores. CONCLUSIONS: The findings from this exploratory study provide preliminary evidence supporting the feasibility of using swipe gesture interactions in mobile games as novel, engaging, and nonintrusive indicators of anxiety and depression.
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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 | 0.002 | 0.006 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".