An mHealth App to Promote Adherence to Immunosuppressant Medication and Track Symptoms in Children After Hematopoietic Stem Cell Transplant: Protocol for a Mixed Methods Usability Study
Bibliographic record
Abstract
BACKGROUND: In the United States, poor adherence accounts for up to 70% of all medication-related hospital admissions, resulting in $100 billion in health care costs annually. In pediatrics, adherence is largely dependent on caregivers. In a high-risk hematopoietic stem cell transplant (HSCT) population, caregivers are isolated with their child due to infection risk and must manage challenging treatment regimens at home, often with limited time and support. Complex behavioral interventions, typically employed to address adherence, are difficult to deliver and manage in the context of these daily tasks. The most successful adherence interventions, and thus improved clinical outcomes, have included mobile health (mHealth) reminder approaches and a direct measure of adherence. OBJECTIVE: This is a 3-phase project, with this protocol describing phase 2, to determine the usability and feasibility of an mHealth app (BMT4me) designed to promote adherence to immunosuppressant medication and to track symptoms among children who received HSCT. METHODS: This study uses an iterative convergent mixed methods design to develop and assess the usability and feasibility of an adherence digital health intervention. We will recruit 15 caregivers of pediatric patients receiving HSCT to complete user testing. Qualitative and quantitative data will be integrated to enhance and expand upon study findings. RESULTS: Enrollment began in September 2021 and is ongoing. A total of 7 caregivers have enrolled. We anticipate completion by fall 2022. We anticipate high usability scores and a better understanding of unique features within the app that are needed for HSCT families post transplant. To date, usability scores among enrolled participants are greater than 70%. Feedback from qualitative interviews is being used to further adapt the app by adding specific weekly logs, call provider options, and voice to text. CONCLUSIONS: This protocol describes a mixed methods usability and feasibility study to develop and implement a smartphone app for caregivers of children receiving HSCT. The app was designed to improve immunosuppressant adherence and to track symptoms in the acute phase post discharge. Study findings will inform further refinement of the app and the feasibility of a pilot randomized controlled trial examining efficacy on clinical outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT04976933; https://clinicaltrials.gov/ct2/show/NCT04976933. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/39098.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.047 | 0.008 |
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".