Pokémon “Go” but for how long?: a qualitative analysis of motivation to play and sustainability of physical activity behaviour in young adults using mobile augmented reality
Bibliographic record
Abstract
Purpose: There is a lack of analysis on players’ perceptions of mobile augmented reality (MAR) games and their sustainable effect. This study analyses Pokémon Go players motivation to play and their perceived physical activity behaviour.Method: Semi-structured interviews of 12 young adult participants residing in Singapore, aged 21–32 years, were performed between February and April 2017. Participants were 6 regular active players of Pokémon Go and 6 were ex-players.Findings: Findings revealed players were motivated to play to fulfil their needs for competency and relatedness. While the game seemed to impact players’ physical activity behaviour, it was not sustained. In addition, already physically active participants may have reduced their physical intensity level when playing.Implications: The study raises questions with regards to the sustained physical activity impact of MAR games for already active young adults. Sport organisations’ managers could use MAR games high attractiveness to secure new membership.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".