The Use of Mobile Health Technology and Behavioral Economics to Encourage Adherence to Statins and Blood Pressure–Lowering Medication in Adolescents with Familial Hypercholesterolemia or Hypertension: Protocol for a Pre-Post Cohort Study
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease (CVD) is a leading cause of mortality and morbidity in the United States, with risk factors such as hypertension and elevated low-density lipoprotein (LDL) cholesterol originating in childhood. While statins and blood pressure-lowering medications can mitigate these risks, adherence is often poor, particularly among youth. Innovative solutions, such as monetary incentives via smartphone apps, may enhance adherence, but evidence in youth is lacking. OBJECTIVE: This study aims to evaluate the efficacy of a smartphone app (Wellth) offering financial incentives to improve adherence to statins and blood pressure-lowering medications among youth aged 12 to 19 years at risk for cardiovascular disease. METHODS: We initially designed a randomized controlled trial to compare the efficacy of 2 different incentive structures in youth treated with a statin for familial hypercholesterolemia with inadequate adherence. After facing recruitment challenges, the study protocol was changed to evaluating a single incentive in a pre-post design. The primary outcome was the change in adherence rate over the 60-day incentive period compared to the adherence rate during the 14-day run-in period. The secondary outcome was a change in LDL cholesterol level. Adjustments to the protocol were made in response to recruitment challenges during the COVID-19 pandemic, simplifying the incentive structure and expanding eligibility criteria. RESULTS: The study is currently undergoing recruitment and collection of data from the first participants. The study has faced recruitment challenges exacerbated by the COVID-19 pandemic, necessitating protocol modifications. Detailed analysis of adherence rates and LDL cholesterol changes is ongoing. CONCLUSIONS: This study explores the efficacy of monetary incentives delivered through a smartphone app to improve medication adherence in youth at risk for CVD. The findings will be used to build upon the existing literature in an effort to improve medication adherence throughout the life course and ultimately reduce CVD. TRIAL REGISTRATION: ClinicalTrials.gov NCT04458766; https://clinicaltrials.gov/study/NCT04458766. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/65105.
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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 | 0.039 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.043 | 0.009 |
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".