Perceptions Toward an Attentional Bias Modification Mobile Game Among Individuals With Low Socioeconomic Status Who Smoke: Qualitative Study
Bibliographic record
Abstract
Background: Attentional bias modification (ABM) can help address implicit attention from individuals who smoke toward smoking cues, which trigger cravings and lapses that impede smoking cessation. We developed an ABM mobile game, Fruit Squish, to support individuals who smoke and are quitting as part of a multicomponent smoking cessation mobile app, Quit Journey. Users advance in the game by tapping on neutral (ie, fruit) rather than smoking-related (eg, cigarette pack) imagery that they are presented with, essentially training them to avoid focusing on smoking cues. Objective: This study aimed to gauge acceptance of an ABM smoking cues mobile game among young adults who smoked and were socioeconomically disadvantaged. Methods: We recruited 38 individuals who smoked cigarettes, aged 18-29 years, who were neither 4-year college graduates nor enrollees in 4-year colleges to participate in 12 semistructured digital focus groups. Sessions were audio recorded and transcribed verbatim. We used ATLAS.ti software to code the transcripts for salient themes based on the Second Unified Theory of Acceptance and Use of Technology constructs (ie, effort expectancy, facilitating conditions, hedonic motivation, performance expectancy, and social influence) and sentiment (ie, negative, neutral, and positive). Results: Performance expectancy of the mobile game was the dominant technology acceptance construct discussed (34/110, 30.90%). Perceived usefulness of the game was mixed in sentiment owing to perceptions that the game aimed to distract individuals who smoke during cravings and concerns that cue imagery in the game could trigger cravings. Hedonic motivation was the second most discussed technology acceptance construct (17/110, 15.45%), with participants describing the game as neither fun nor engaging. Participants referenced their past experiences with mobile games and mobile device characteristics as facilitating conditions for using the game (10/110, 9.09%). Although effort expectancy was minimally discussed (6/110, 5.45%), the game was characterized as easy to use. To improve the game, participants suggested adding new levels with increasing difficulty (eg, increase stimuli speed and limit session time) and new game elements (eg, leaderboard). Other suggestions included improving game graphics and renaming the game to capture its relation to smoking cessation. Conclusions: Young adults with low socioeconomic status who smoke had mixed reactions to a mobile smoking cues ABM game. Results suggest the need to communicate the rationale underlying ABM games to users and their potential positive effects on smoking cessation. To promote the uptake and sustained use of ABM mobile games, they need to be on par with commercially available entertainment mobile apps. Research is needed to explore the efficacy of gamified ABM on cognitive biases in real-life settings.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| 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".