An innovative SMS intervention to improve adherence to stimulants in children with ADHD: Preliminary findings
Bibliographic record
Abstract
BACKGROUND: Although large datasets document that stimulants decrease the risk for many adverse ADHD-associated outcomes, compliance with stimulants remains poor. AIMS: This study examined the effectiveness of a novel ADHD-centric text messaging-based intervention aimed to improve adherence to stimulant medications in children with ADHD. METHODS: Subjects were 87 children aged 6-12, who were prescribed a stimulant medication for ADHD treatment. Prescribers gave permission to contact their patients for participation in the study. Subjects were primarily from the primary care setting with a subsample of psychiatrically referred subjects for comparison. Age- and sex-matched comparators were identified (3:1) from the same pool of prescriber-approved subjects that did not participate. Timely prescription refills (within 37 days) were determined from prescription dates documented in patients' electronic medical record. RESULTS: < 0.001). The number needed to treat statistic was computed as five, meaning for every five patients who receive the SMS intervention, we can keep one adherent to their stimulant treatment. CONCLUSIONS: These preliminary findings support the potential utility of a readily accessible technology to improve the poor rate of adherence to stimulant treatment in children with ADHD. To the best of our knowledge, this study is the first digital health intervention aimed at improving adherence to stimulant medication for children with ADHD. These results support the need for further examination of this technology through more definitive randomized clinical trials.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".