Dark patterns, dark nudges, sludge and misinformation: alcohol industry apps and digital tools
Bibliographic record
Abstract
Many alcohol-industry-funded (AIF) organizations disseminate eHealth/mHealth tools that claim to assist users in making health decisions by monitoring alcohol consumption, e.g. blood alcohol calculators, AUDIT scores, consumption trackers. Previously, AIF materials were found to contain health misinformation that could increase consumption (dark nudges) or make healthy behaviour change more difficult (sludge). The accuracy and functionality of AIF tools have never been analysed, and given the history of AIF materials it is possible they contain misinformation and function as covert marketing channels to promote alcohol-industry-friendly narratives on the causes and possible solutions of alcohol-related harms. We evaluated the information accuracy and framing, behaviour change techniques (BCTs), and functions of AIF digital tools (n = 15, from the UK, Ireland, the USA, Canada, New Zealand, Australia; including Drinkaware, Drinkwise, Educ'alcool and others), compared to a non-industry-funded independent sample (n = 10). We identified misinformation and 'dark patterns' (interface design strategies for influencing users against their interest) throughout AIF tools; significantly fewer provided accurate feedback (33% vs 100%), and significantly more omitted information on cancer (67% vs 10%) and cardiovascular disease (80% vs 30%) and promoted industry-friendly narratives (47% vs 0%). AIF tools encouraged consumption through priming nudges (53%) and social norming (40%). AIF tools utilized fewer BCTs, provided users with more limited pre-set options (54%), and fewer drink choices (mean 24 vs 275). Their input structure often impeded their ability to provide guideline advice. We conclude that AIF tools contain pro-industry misinformation strategies and dark patterns that misinform users about their consumption and could 'nudge' them towards continuing to drink alcohol-characteristics of 'Dark Apps' designs.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".