Using Serious Games for Antismoking Health Campaigns: Experimental Study
Bibliographic record
Abstract
Background Serious games for health have been gaining in popularity among scholars and practitioners. However, there remain a few questions to be addressed. Objective This study tests the effects of a serious game and fear appeals on smoking-related outcomes. More specifically, this research aims to understand how serious games function as a more effective vehicle for a health campaign than a traditional medium, such as a print-based pamphlet. Further, while serious games utilize a variety of persuasive strategies in the game’s content, it is not clear whether fear appeals, which are widely used persuasive-message strategies for health, can be an effective strategy in serious games. Thus, we are testing the effect of fear appeals in a serious game. Methods We created a computer game and a print brochure to educate participants about the risks of smoking. More specifically, a flash-based single-player game was developed in which players were asked to avoid cigarettes in the gameplay context. We also developed an online brochure based on existing smoking-related brochures at a university health center; antismoking messages on the computer game and in the brochure were comparable. Then, an experiment using a 2 (media type: game vs. print) x 2 (fearful image: fear vs. no-fear) between-subjects design was conducted. The study recruitment was announced to undergraduate students enrolled in a large, public Midwestern university in the United States. After a screening test, a total of 72 smokers, who reported smoking in the past 30 days, participated in the experiment. Results Overall, gameplay, when compared to print-based pamphlets, had greater impacts on attitudes toward smoking and the intention to quit smoking. Further, the game’s persuasive effects were especially pronounced when messages contained fear appeals. When fearful images were presented, participants in the game condition reported significantly more negative attitudes toward social smoking than those in the print condition [F(1,67)=7.28; P=.009; ηp2=0.10]. However, in the no-fear condition, there was no significant difference between the conditions [F(1,67)=0.25; P=.620]. Similarly, the intention to quit smoking [F(1,67)=4.64; P=.035; ηp2=0.07] and susceptibility [F(1,67)=6.92; P=.011; ηp2=0.09] were also significantly different between the conditions, but only when fear appeals were used. Conclusions This study extends fear appeal research by investigating the effects of different media types. It offers empirical evidence that a serious game can be an effective vehicle for fear appeals.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".