“Let’s get Wasted!” and Other Apps: Characteristics, Acceptability, and Use of Alcohol-Related Smartphone Applications
Bibliographic record
Abstract
BACKGROUND: Smartphone applications ("apps") offer a number of possibilities for health promotion activities. However, young people may also be exposed to apps with incorrect or poor quality information, since, like the Internet, apps are mostly unregulated. Little is known about the quality of alcohol-related apps or what influence they may have on young people's behavior. OBJECTIVE: To critically review popular alcohol-related smartphone apps and to explore young people's opinions of these apps, their acceptability, and use for alcohol-related health promotion. METHODS: First, a content analysis of 500 smartphone apps available via Apple iTunes and Android Google Play stores was conducted. Second, all available blood alcohol concentration (BAC) apps were tested against four individual case profiles of known BAC from a previous study. Third, two focus group discussions explored how young people use alcohol-related apps, particularly BAC apps. RESULTS: 384 apps were included; 50% (192) were entertainment apps, 39% (148) were BAC apps, and 11% (44) were health promotion and/or stop drinking-related apps. When testing the BAC apps, there was wide variation in results, with apps tending to overestimate BAC scores compared with recorded scores. Participants were skeptical of the accuracy of BAC apps, and there was an overall concern that these apps would be used as a form of entertainment, further encouraging young people to drink, rather than reduce their drinking and risk taking. CONCLUSIONS: The majority of popular alcohol-related apps encouraged alcohol consumption. Apps estimating blood alcohol concentration were widely available but were highly unreliable. Health departments and prominent health organizations need to endorse alcohol smartphone apps that are accurate and evidence-based to give specific apps credibility in the ever-expanding market of unregulated apps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".