Usage of a Multipurpose mHealth App Among Adults With Sickle Cell Disease: Randomized Controlled Trial
Bibliographic record
Abstract
Background: While mobile health (mHealth) apps have been made for various diseases, including sickle cell disease (SCD), most focus on a single purpose. SCD is a chronic disease that requires knowledge of the disease, self-management, and adherence to treatment plans. While mHealth apps have been made with single features for SCD, there is limited understanding of using an mHealth app with a more comprehensive set of features that could engage adults with SCD, depending on what features they prefer and need to engage and empower them in living with their disease. Objective: We evaluated the usage of an mHealth app with various features, including pain tracking, quizzes for patient-facing guidelines, pain and asthma action plans, and goal setting. Methods: Adults with SCD were enrolled at 2 sickle cell centers between 2018 and 2022 as part of a 6-month feasibility randomized controlled trial with participants completing surveys at baseline and 6 months. Participants were randomized into receiving either an mHealth app and booklet with patient-facing guidelines or a booklet with the guidelines alone. The mHealth app comprised web pages with patient-facing guideline material and a Research Electronic Data Capture (REDCap) project. The REDCap project included a personal profile, a pain tracker, goal setting, quizzes about the guidelines, and pain or asthma action plans. The REDCap project also included the ability to send daily text messages at a time they chose, which contained a message they could create and a link to their profile. Outcomes included SCD-specific knowledge and acute health care utilization (emergency room visits and hospitalizations). We evaluated the usage of these different features and relationships with baseline variables, each other, and study outcomes. Results: Approximately 75% (50/67) of the enrolled and randomized participants completed all the study components, and 100% (26/26) of the participants who were randomized to the mHealth app arm and completed the study used the mHealth app. Further, 15/30 (50%) participants used multiple features. Baseline sickle cell knowledge and female gender were associated with more usage of pain diary (P=.04) and mission (P=.046) features, respectively. While not significant, mission completion was associated with lower hospitalizations (P=.06). Conclusions: Adults with SCD engaged differently with an mHealth app with multiple features. As this study was not focused on one part of our app, engagement with features in this app was entirely patient-driven, which may demonstrate the expected real-world use of an mHealth app in this population. A multipurpose app can help engage participants in self-management strategies through different features and potentially improve outcomes.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".