Preferences for Using a Mobile App in Sickle Cell Disease Self-management: Descriptive Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Individuals with sickle cell disease (SCD) and their caregivers may benefit from technology-based resources to improve disease self-management. OBJECTIVE: This study explores the preferences regarding a mobile health (mHealth) app to facilitate self-management in adults with SCD and their caregivers living in urban and rural communities. METHODS: Five community listening sessions were conducted in 2 urban and rural communities among adults with SCD and their caregivers (N=43). Each session comprised 4 to 15 participants. Participants were asked questions on methods of finding information about SCD self-care, satisfaction with current methods for finding SCD management information, support for SCD management, important features for development of an mHealth app, and areas of benefit for using an mHealth app for SCD self-management. An inductive-deductive content analysis approach was implemented to identify the critical themes. RESULTS: Seven critical themes emerged, including the current methods for receiving self-management information, desired information, recommendations for communicating sickle cell self-management information, challenges of disease management, types of support received for disease management, barriers to and facilitators of using an mHealth app, and feature preferences for an mHealth app. In addition, we found that the participants were receptive to using mHealth apps in SCD self-management. CONCLUSIONS: This study expands our knowledge on the use of mHealth technology to reduce information access barriers pertaining to SCD. The findings can be used to develop a patient-centered, user-friendly mHealth app to facilitate disease self-management, thus increasing access to resources for families of patients with SCD residing in rural communities.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".