Usability and Acceptability of a Mobile App for the Self-Management of Alcohol Misuse Among Veterans (Step Away): Pilot Cohort Study
Bibliographic record
Abstract
BACKGROUND: Alcohol misuse is common among Operation Enduring Freedom and Operation Iraqi Freedom veterans, yet barriers limit treatment participation. Mobile apps hold promise as means to deliver alcohol interventions to veterans who prefer to remain anonymous, have little time for conventional treatments, or live too far away to attend treatment in person. OBJECTIVE: This pilot study evaluated the usability and acceptability of Step Away, a mobile app designed to reduce alcohol-related risks, and explored pre-post changes on alcohol use, psychological distress, and quality of life. METHODS: This single-arm pilot study recruited Operation Enduring Freedom and Operation Iraqi Freedom veterans aged 18 to 55 years who exceeded National Institute on Alcohol Abuse and Alcoholism drinking guidelines and owned an iPhone. Enrolled veterans (N=55) completed baseline and 1-, 3-, and 6-month assessments. The System Usability Scale (scaled 1-100, ≥70 indicating acceptable usability) assessed the effectiveness, efficiency, and satisfaction dimensions of usability, while a single item (scaled 1-9) measured the attractiveness of 10 screenshots. Learnability was assessed by app use during week 1. App engagement (proportion of participants using Step Away, episodes of use, and minutes per episode per week) over 6 months measured acceptability. Secondary outcomes included pre-post change on heavy drinking days (men: ≥5 drinks per day; women: ≥4 drinks per day) and Short Inventory of Problems-Revised, Kessler-10, and brief World Health Organization Quality of Life Questionnaire scores. RESULTS: Among the 55 veterans enrolled in the study, the mean age was 37.4 (SD 7.6), 16% (9/55) were women, 82% (45/55) were White, and 82% (45/55) had an alcohol use disorder. Step Away was used by 96% (53/55) of participants in week 1, 55% (30/55) in week 4, and 36% (20/55) in week 24. Step Away use averaged 55.1 minutes (SD 57.6) in week 1 and <15 minutes per week in weeks 2 through 24. Mean System Usability Scale scores were 69.3 (SD 19.7) and 71.9 (SD 15.8) at 1 and 3 months, respectively. Median attractiveness scores ranged from 5 to 8, with lower ratings for text-laden screens. Heavy drinking days decreased from 29.4% (95% CI 23.4%-35.4%) at baseline to 16.2% (95% CI 9.9%-22.4%) at 6 months (P<.001). Likewise, over 6 months, Short Inventory of Problems-Revised scores decreased from 6.3 (95% CI 5.1-7.5) to 3.6 (95% CI 2.4-4.9) (P<.001) and Kessler-10 scores decreased from 18.8 (95% CI 17.4-20.1) to 17.3 (95% CI 15.8-18.7) (P=.046). Changes were not detected on quality of life scores. CONCLUSIONS: Operation Enduring Freedom and Operation Iraqi Freedom veterans found the usability of Step Away to be acceptable and engaged in the app over the 6-month study. Reductions were seen in heavy drinking days, alcohol-related problems, and Kessler-10 scores. A larger randomized trial is warranted to confirm our findings.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".