Personalized Approach Bias Modification Smartphone App (“SWIPE”) to Reduce Alcohol Use Among People Drinking at Hazardous or Harmful Levels: Protocol for an Open-Label Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Alcohol accounts for 5.1% of the global burden of disease and injury, and approximately 1 in 10 people worldwide develop an alcohol use disorder. Approach bias modification (ABM) is a computerized cognitive training intervention in which patients are trained to "avoid" alcohol-related images and "approach" neutral or positive images. ABM has been shown to reduce alcohol relapse rates when delivered in residential settings (eg, withdrawal management or rehabilitation). However, many people who drink at hazardous or harmful levels do not require residential treatment or choose not to access it (eg, owing to its cost, duration, inconvenience, or concerns about privacy). Smartphone app-delivered ABM could offer a free, convenient intervention to reduce cravings and consumption that is accessible regardless of time and place, and during periods when support is most needed. Importantly, an ABM app could also easily be personalized (eg, allowing participants to select personally relevant images as training stimuli) and gamified (eg, by rewarding participants for the speed and accuracy of responses) to encourage engagement and training completion. OBJECTIVE: We aim to test the feasibility and acceptability of "SWIPE," a gamified, personalized alcohol ABM smartphone app, assess its preliminary effectiveness, and explore in which populations the app shows the strongest indicators of effectiveness. METHODS: We aim to recruit 500 people who drink alcohol at hazardous or harmful levels (Alcohol Use Disorders Identification Test score≥8) and who wish to reduce their drinking. Recruitment will be conducted through social media and websites. The participants' intended alcohol use goal (reduction or abstinence), motivation to change their consumption, and confidence to change their consumption will be measured prior to training. Participants will be instructed to download the SWIPE app and complete at least 2 ABM sessions per week for 4 weeks. Recruitment and completion rates will be used to assess feasibility. Four weeks after downloading SWIPE, participants will be asked to rate SWIPE's functionality, esthetics, and quality to assess acceptability. Alcohol consumption, craving, and dependence will be measured prior to commencing the first session of ABM and 4 weeks later to assess whether these variables change significantly over the course of ABM. RESULTS: We expect to commence recruitment in August 2020 and complete data collection in March 2021. CONCLUSIONS: This will be the first study to test the feasibility, acceptability, and preliminary effectiveness of a personalized, gamified ABM intervention smartphone app for hazardous or harmful drinkers. Results will inform further improvements to the app, as well as the design of a statistically powered randomized controlled trial to test its efficacy relative to a control condition. Ultimately, we hope that SWIPE will extend the benefits of ABM to the millions of individuals who consume alcohol at hazardous levels and wish to reduce their use but cannot or choose not to access treatment. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12620000638932p; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12620000638932p. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/21278.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".