A randomized trial evaluating an mHealth system to monitor and enhance adherence to pharmacotherapy for alcohol use disorders
Bibliographic record
Abstract
BACKGROUND: Nonadherence to prescribed medication regimens is a substantial barrier to the pharmacological management of alcohol use disorders. The availability of low-cost, sustainable interventions that maximize medication adherence would likely lead to improved treatment outcomes. Mobile health (mHealth) technologies are increasingly being adopted as a method of delivering behavioral health interventions and represent a promising tool for adherence interventions. We are evaluating a cell-phone-based intervention called AGATE that seeks to enhance adherence with regular text-messaging. METHODS/DESIGN: A randomized controlled effectiveness trial in the context of an eight-week open label naltrexone efficacy trial delivered in a naturalistic clinical setting. Treatment-seeking heavy drinkers (N = 105) are currently being recruited and randomly assigned to the AGATE intervention or a control condition. Daily measures of alcohol use and medication side effects are being recorded via cell phone in both conditions. Additionally, participants randomized to the AGATE condition receive medication reminders via SMS text message according to a schedule that adjusts according to their level of adherence. DISCUSSION: Results from this trial will provide initial information about the feasibility and efficacy of mHealth interventions for improving adherence to alcohol pharmacotherapies. TRIAL REGISTRATION: NCT01349985.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".