Mobile Phone Apps for University Students With Hazardous Alcohol Use: Study Protocol for Two Consecutive Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: About 50% of university students overconsume alcohol, and drinking habits in later adulthood are to some extent established during higher educational studies. Several studies have demonstrated that Internet-based interventions have positive effects on drinking habits among university students. Our recent study evaluated two mobile phone apps targeting drinking choices at party occasions via personalized feedback on estimated blood alcohol concentration (eBAC) for students with hazardous drinking. No changes in drinking parameters were found over a seven-week period apart from an increase in number of drinking occasions among men for one of the apps tested. Up to 30% of the study participants drank at potentially harmful levels: higher than the national recommended number of standard drinks per week (a maximum of 9 for women and 14 for men) in Sweden. OBJECTIVE: (1) To evaluate improved versions of the two mobile phone apps tested in our prior trial, in a new, 3-armed randomized controlled trial among university students with at least hazardous drinking habits according to the Alcohol Use Disorders Identifications Test (AUDIT; Study 1). (2) After 6 weeks, to target study participants showing alcohol consumption higher than the national recommended levels for standard drinks per week by offering them participation in a second, 2-armed randomized trial evaluating an additional mobile phone app with skill enhancement tasks (Study 2). (3) To follow participants at 6, 12 and 18 weeks after recruitment to Study 1 and at 6 and 12 weeks after recruitment to Study 2. METHODS: Two randomized controlled trials are conducted. Study 1: Students are recruited at four Swedish universities, via direct e-mail and advertisements on Facebook and student union web sites. Those who provide informed consent, have a mobile phone, and show at least hazardous alcohol consumption according to the AUDIT (≥6 for women; ≥8 points for men) are randomized into three groups. Group 1 has access to the Swedish government alcohol monopoly's app, Promillekoll, offering real-time estimated eBAC calculation; Group 2 has access to a Web-based app, PartyPlanner, developed by the research group, offering real-time eBAC calculation with planning and follow-up functions; and Group 3 participants are controls. Follow-up is conducted at 6, 12 and 18 weeks. Study 2. Participants who at the first 6-week follow-up show drinking levels higher than 9 (W) or 14 (M) standard drinks (12 g alcohol) per week, are offered participation in Study 2. Those who consent are randomized to either access to a skills training app, TeleCoach or to a wait-list control group. RESULTS: Latent Markov models for Study 1 and mixed models analyses for Study 2 will be performed. Study 2 data will be analyzed for publication during the spring of 2016; Study 1 data will be analyzed for publication during the fall of 2016. CONCLUSIONS: If mobile phone interventions for reducing hazardous alcohol use are found to be effective, the prospects for positively influencing substance use-related health among university students can considerably improve. TRIAL REGISTRATION: ClinicalTrials.gov http://clinicaltrials.gov/ct2/show/NCT02064998 (Archived by WebCite at http://www.webcitation.org/6dy0AlVRP).
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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 | 0.034 | 0.041 |
| Meta-epidemiology (narrow) | 0.008 | 0.005 |
| Meta-epidemiology (broad) | 0.018 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.087 | 0.015 |
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".