Real-Time Mobile Monitoring of Drinking Episodes in Young Adult Heavy Drinkers: Development and Comparative Survey Study
Bibliographic record
Abstract
BACKGROUND: Binge drinking, defined as consuming five or more standard alcoholic drinks for men (four for women) within a 2-hour period, is common among young adults and is associated with significant alcohol-related morbidity and mortality. To date, most research on this problem in young adults has relied upon retrospective questionnaires or costly laboratory-based procedures. Smartphone-based ecological momentary assessment (EMA) may address these limitations by allowing researchers to measure alcohol use and related consequences in real time and in drinkers' natural environments. To date, however, relatively less research has systematically examined the utility of this approach in a sample of young adults targeting real-world heavy drinking episodes specifically. OBJECTIVE: This study aimed to evaluate the feasibility, acceptability, and safety of a smartphone-based EMA method targeting binge drinking and related outcomes in heavy drinking young adults during real-world drinking occasions. METHODS: Young adult binge drinkers in the smartphone group (N=83; mean 25.4 (SD 2.6) years; 58% (48/83) male; bingeing on 23.2% (6.5/28) days in the past month) completed baseline measures of alcohol use and drinking-related consequences, followed by up to two smartphone-based EMA sessions of typical drinking behavior and related outcomes in their natural environments. They also completed next-day and two-week follow-up surveys further assessing alcohol use and related consequences during the EMA sessions and two weeks after study participation, respectively. A separate demographic- and drinking-matched safety comparison group (N=25) completed the baseline and two-week follow-up surveys but did not complete EMA of real-world drinking behavior. RESULTS: Most participants (71%, 59/83) in the smartphone group engaged in binge drinking during at least one 3-hour EMA session, consuming 7.3 (SD 3.0) standard alcoholic drinks. They completed 87.2% (507/581) system-initiated EMA prompts during the real-world drinking episode, supporting the feasibility of this approach. The procedure was acceptable, as evidenced by high participant ratings for overall satisfaction with the EMA software and study procedures and low ratings for intrusiveness of the mobile surveys. Regarding safety, participants endorsed few drinking-related consequences during or after the real-world drinking episode, with no adverse or serious adverse events reported. There were no differences between the groups in terms of changes in drinking behavior or consequences from baseline to two-week follow-up. CONCLUSIONS: This study provided preliminary support for the feasibility, acceptability, and safety of a smartphone-based EMA of real-time alcohol use and related outcomes in young adult heavy drinkers. The results suggest that young adults can use smartphones to safely monitor drinking even during very heavy drinking episodes. Smartphone-based EMA has strong potential to inform future research on the epidemiology of and intervention for alcohol use disorder by providing researchers with an efficient and inexpensive way to capture large amounts of data on real-world drinking behavior and consequences.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".