Effect of Pokémon Go on Self-Harm Using Population-Based Interrupted Time-Series Analysis: Quasi-Experimental Study
Bibliographic record
Abstract
BACKGROUND: Pokémon Go is a very popular location-based augmented reality game with widespread influences over the world. An emerging body of research demonstrates that playing Pokémon Go can lead to improvements in physical activity and psychosocial well-being; however, whether Pokémon Go reduces self-harm incidence at the population-level is still questionable. OBJECTIVE: This study aimed to quantify how the launch of Pokémon Go in Hong Kong affected the incidence of self-harm using a quasi-experimental design. METHODS: An interrupted time-series design with Poisson segmented regression adjusted for time and seasonality trends was used on data from 2012 to 2018 to detect any changes in the number of accident and emergency attendances due to self-harm, after Pokémon Go was launched. The findings were validated using a baseline control period and using other intentional injuries and minor noninjuries as control outcomes. We also assessed intervention effects by age group. RESULTS: From January 1, 2012 to July 31, 2018, there were 13,463 accident and emergency attendances due to self-harm in Hong Kong. During the period after launching Pokémon Go, self-harm attendances dropped by 34% (adjusted incidence rate ratio: 0.66, 95% CI 0.61-0.73). When analyzed by age group, a reduction in self-harm incidence was only apparent in adults (18 to 24 years of age: adjusted incidence rate ratio: 0.78, P=.02; 25 to 39 years of age: adjusted incidence rate ratio: 0.75, P<.001; 40 years of age and older: adjusted incidence rate ratio: 0.57, P<.001). CONCLUSIONS: Self-harm incidence in the population, particularly in adults, showed a significant decline in the period after Pokémon Go was launched. Augmented reality games such as Pokémon Go show great promise as a tool to enhance psychosocial well-being and improve mental health.
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 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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".