A multilevel mHealth intervention boosts adherence to hydroxyurea in individuals with sickle cell disease
Bibliographic record
Abstract
Hydroxyurea reduces sickle cell disease (SCD) complications, but medication adherence is low. We tested 2 mobile health (mHealth) interventions targeting determinants of low adherence among patients (InCharge Health) and low prescribing among providers (HU Toolbox) in a multi-center, non-randomized trial of individuals with SCD ages 15-45. We compared the percentage of days covered (PDC), labs, healthcare utilization, and self-reported pain over 24 weeks of intervention and 12 weeks post-study with a 24-week preintervention interval. We enrolled 293 patients (51% male; median age 27.5 years, 86.8% HbSS/HbSβ0-thalassemia). The mean change in PDC among 235 evaluable subjects increased (39.7% to 56.0%; P < 0.001) and sustained (39.7% to 51.4%, P < 0.001). Mean HbF increased (10.95% to 12.78%; P = 0.03). Self-reported pain frequency reduced (3.54 to 3.35 events/year; P = 0.041). InCharge Health was used ≥1 day by 199 of 235 participants (84.7% implementation; median usage: 17% study days; IQR: 4.8-45.8%). For individuals with ≥1 baseline admission for pain, admissions per 24 weeks declined from baseline through 24 weeks (1.97 to 1.48 events/patient, P = 0.0045) and weeks 25-36 (1.25 events/patient, P = 0.0015). PDC increased with app use (P < 0.001), with the greatest effect in those with private insurance (P = 0.0078), older subjects (P = 0.033), and those with lower pain interference (P = 0.0012). Of the 89 providers (49 hematologists, 36 advanced care providers, 4 unreported), only 11.2% used HU Toolbox ≥1/month on average. This use did not affect change in PDC. Tailoring mHealth solutions to address barriers to hydroxyurea adherence can potentially improve adherence and provide clinical benefits. A definitive randomized study is warranted. This trial was registered at www.clinicaltrials.gov as #NCT04080167.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".