American Heart Association’s Cholesterol CarePlan as a Smartphone-Delivered Web App for Patients Prescribed Cholesterol-Lowering Medication: Protocol for an Observational Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Adoption of healthy lifestyle and compliance with cholesterol-lowering medication reduces the risk of cardiovascular disease (CVD). The use of digital tools and mobile technology may be important for sustaining positive behavioral change. OBJECTIVE: The primary objective of this study is to evaluate the feasibility and acceptability of administering the Cholesterol CarePlan Web app developed by the American Heart Association aimed at improving lifestyle and medication adherence among patients prescribed cholesterol-lowering medication. The secondary objective is to assess the Web app's efficacy. METHODS: A prospective, observational feasibility study will be conducted to demonstrate whether the Web app may be successfully taken up by patients and will be associated with improved clinical and behavioral outcomes. The study will aim to recruit 180 study participants being prescribed cholesterol-lowering medication for at least 30 days across 14 general practices in London, England. Potentially eligible patients will be invited to use the Web app on a smartphone and visit general practice for three 20-minute clinical assessments of blood pressure, height, weight, smoking, and nonfasting cholesterol over 24 weeks. The feasibility of administering the Web app will be judged by recruitment and dropout statistics and the sociodemographic and comorbidity profile of consenting study participants, consenting nonparticipants, and all potentially eligible patients. Acceptability will be assessed using patients' readiness to embrace new technologies, the usability of the Web app, and patient satisfaction. The efficacy of the Web app will be assessed by changes in medication adherence and clinical risk factors by levels of the Web app compliance. RESULTS: This study is currently funded by the American Heart Association. Initial study recruitment will take place between February and July 2018 followed by patient follow-up. Patient level data will be obtained in January 2019. Data analysis will be completed by February 2019. Results will be submitted for publication in March 2019. CONCLUSIONS: The potential of an app to improve patients' lifestyle and management of cholesterol may inform the design of a randomized controlled trial and the delivery of more effective CVD prevention programs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/9017.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.039 | 0.010 |
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".