A Smarter Pathway for Delivering Cue Exposure Therapy? The Design and Development of a Smartphone App Targeting Alcohol Use Disorder
Bibliographic record
Abstract
BACKGROUND: Although the number of alcohol-related treatments in app stores is proliferating, none of them are based on a psychological framework and supported by empirical evidence. Cue exposure treatment (CET) with urge-specific coping skills (USCS) is often used in Danish treatment settings. It is an evidence-based psychological approach that focuses on promoting "confrontation with alcohol cues" as a means of reducing urges and the likelihood of relapse. OBJECTIVE: The objective of this study was to describe the design and development of a CET-based smartphone app; an innovative delivery pathway for treating alcohol use disorder (AUD). METHODS: The treatment is based on Monty and coworkers' manual for CET with USCS (2002). It was created by a multidisciplinary team of psychiatrists, psychologists, programmers, and graphic designers as well as patients with AUD. A database was developed for the purpose of registering and monitoring training activities. A final version of the CET app and database was developed after several user tests. RESULTS: The final version of the CET app includes an introduction, 4 sessions featuring USCS, 8 alcohol exposure videos promoting the use of one of the USCS, and a results component providing an overview of training activities and potential progress. Real-time urges are measured before, during, and after exposure to alcohol cues and are registered in the app together with other training activity variables. Data packages are continuously sent in encrypted form to an external database and will be merged with other data (in an internal database) in the future. CONCLUSIONS: The CET smartphone app is currently being tested at a large-scale, randomized controlled trial with the aim of clarifying whether it can be classified as an evidence-based treatment solution. The app has the potential to augment the reach of psychological treatment for AUD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".