Caregivers’ Perceptions, Needs, and Data Sharing Concerns in mHealth Research on Pediatric Asthma: Cross-Sectional Survey Study
Bibliographic record
Abstract
Background: Pediatric asthma is the most common chronic respiratory disease of childhood. Caregivers often report lacking knowledge in several aspects of asthma management at home. Although the use of mobile health (mHealth) tools, such as mobile apps, could facilitate asthma self-management and, simultaneously, the collection of data for research, few studies have explored the features that caregivers would like to see in such a tool and their perceptions on data sharing. Objective: This study evaluates caregivers' perceived knowledge gaps in asthma management; their perceptions of certain features and resources that should be included in a potential mobile app; and any concerns that they may have regarding data sharing for research, including privacy and security concerns. Methods: In this cross-sectional study, we surveyed 200 caregivers of children (aged 1-13 y) with asthma who were followed at a pediatric tertiary care center in Montreal, Canada. Anonymous data were collected through the institutional web-based survey platform. We collected the participants' answers by using a 5-category Likert scale ("completely agree," "agree," "neither agree nor disagree," "disagree," and "completely disagree"), multiple-choice questions, and free-text questions on the abovementioned topics. Descriptive statistics were performed, and answers were compared between caregivers of preschool-aged children and caregivers of school-aged children. Results: Participating children's mean age was 5.9 (SD 3.4) years, with 54% (108/200) aged ≤5 years and 46% (92/200) aged >6 years. Overall, caregivers reported having adequate knowledge about asthma and asthma self-management. Nonetheless, they identified several desirable features for a mobile app focused on asthma self-management. The most frequently identified features included receiving alerts about environmental triggers of asthma (153/199, 76.9%), having videos that demonstrate symptoms of asthma (133/199, 66.8%), and being able to log children's asthma action plans in the app (133/199, 66.8%). Interestingly, more caregivers of preschool-aged children preferred textual information when compared to caregivers of school-aged children (textual information for explaining asthma: P=.008; textual information for the symptoms of asthma: P=.005). Caregivers were generally highly in favor of sharing data collected through a mobile app for research. Conclusions: Caregivers of children with asthma in our study identified several desirable educational and interactive features that they wanted to have in a mobile app for asthma self-management. These findings provide a foundation for designing and developing mHealth tools that are relevant to caregivers of children with asthma.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".