Smartphone Alcohol Use Disorder Recovery Apps: Cross-sectional Survey of Behavioral Intention to Use (Preprint)
Bibliographic record
Abstract
BACKGROUND Alcohol use disorder (AUD) carries a huge health and economic cost to society. Effective interventions exist but numerous challenges limit their adoption, especially in a pandemic context. AUD recovery apps (AUDRA) have emerged as a potential complement to in-person interventions. They are easy to access and show promising results in terms of efficacy. However, they rely on individual adoption decisions and remain underused. OBJECTIVE The aim of this survey study is to explore the beliefs that determine the intention to use AUDRA. METHODS We conducted a cross-sectional survey study of people with AUD. We used the Unified Theory of Acceptance and Use of Technology, which predicts use and behavioral intention to use based on performance expectancy, effort expectancy, social influence, and facilitating conditions. Participants were recruited directly from 2 sources; first, respondents at addiction treatment facilities in Ontario, Canada, were contacted in person, and they filled a paper form; second, members from AUD recovery support groups on social media were contacted and invited to fill an internet-based survey. The survey was conducted between October 2019 and June 2020. RESULTS The final sample comprised 159 participants (124 involved in the web-based survey and 35 in the paper-based survey) self-identifying somewhat or very much with AUD. Most participants (n=136, 85.5%) were aware of AUDRA and those participants scored higher on performance expectancy, effort expectancy, and social influence. Overall, the model explains 35.4% of the variance in the behavioral intention to use AUDRA and 11.1% of the variance in use. Social influence (P=.31), especially for women (P=.23), and effort expectancy (P=.25) were key antecedents of behavioral intention. Facilitating conditions were not significant overall but were moderated by age (P=.23), suggesting that it matters for older participants. Performance expectancy did not predict behavioral intention, which is unlike many other technologies but confirms other findings associated with mobile health (mHealth). Open-ended questions suggest that privacy concerns may significantly influence the use of AUDRA. CONCLUSIONS This study suggests that unlike many other technologies, the adoption of AUDRA is not mainly determined by utilitarian factors such as performance expectancy. Rather, effort expectancy and social influence play a key role in determining the intention to use AUDRA.
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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 | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".