Development, Validation, and Implementation of an Innovative Mobile App for Alcohol Dependence Management: Protocol for the SIDEAL Trial
Bibliographic record
Abstract
BACKGROUND: Information and communication technologies (ICT) have become one of the main pathways to the new paradigm of increased self-management of chronic conditions such as alcohol dependence. Validation of some mobile phone apps has begun, while validation of many others is forthcoming. OBJECTIVE: To describe the protocol for validation of a new app called SIDEAL (an acronym of the Spanish name "Soporte Innovador a la persona con DEpendencia del ALcohol," or innovative support for people with alcohol dependence). METHODS: The project consists of 3 complementary, consecutive studies, including a pilot feasibility study, a qualitative study using focus groups, and, finally, a randomized controlled trial where patients will be randomized to standard treatment or standard treatment plus SIDEAL. During the pilot study, feasibility, usability, and acceptance by users will be the main outcomes explored. An electronic questionnaire will be sent to patients asking for their opinions. Focus groups will be the next step, after which improvements and refinements will be implemented in the app. During the final phase, consumption variables (heavy drinking days per month, mean standard drinks per day) will be investigated, in order to test app efficacy. RESULTS: Because of the encouraging results with previous similar apps, we expect patients to widely accept and incorporate SIDEAL into their therapeutic options. Significant reductions in drinking-related variables are also expected. The pilot study has concluded with the inclusion of 29 patients. Results are expected to be available soon (expected mid-2016). CONCLUSIONS: SIDEAL may represent a useful, reliable, effective, and efficient tool to complement therapeutic options available to both patients and professionals.
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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 | 0.061 | 0.063 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.070 | 0.019 |
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".