Caregiver needs and perception of data sharing for research through mHealth in pediatric asthma: a cross-sectional survey (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> Asthma is the most common chronic respiratory disease of childhood. Caregivers often report lacking knowledge in several aspects of asthma management at home. While the use of mHealth tools such as mobile applications could facilitate asthma self-management and simultaneously collect data for research, few studies have explored the features that caregivers would like to see in such a tool and their perception for data sharing. </sec> <sec> <title>OBJECTIVE</title> This study evaluated caregivers’ perceived knowledge gaps in asthma management, their perception of certain features and resources that should be included in a potential mobile application, and any concerns that they may have on data sharing for research, including privacy and security. </sec> <sec> <title>METHODS</title> In this cross-sectional study, we surveyed 200 caregivers of children aged 1-13 years with asthma followed at a pediatric tertiary care center in Montreal, Canada. Anonymous data was collected through the institutional online survey platform. We collected the participant’s answers using a 5-category Likert scale (completely agree, agree, neither agree nor disagree, disagree, completely disagree), multiple choice questions, and free text questions on the topics above. Descriptive statistics were performed and answers were compared between caregivers of preschool- and school-aged children. </sec> <sec> <title>RESULTS</title> Participating children had a mean(standard deviation) age of 5.9(3.4) years, with 54% aged ≤5 years and 46% >6 years. Overall, caregivers reported having adequate knowledge about asthma and asthma self-management. Nonetheless, they identified several desirable features for a mobile application focused on asthma self-management. The most frequently identified features include receiving alerts about environmental triggers of asthma (76.9%), having videos demonstrating symptoms of asthma (66.8%), and being able to log their child’s asthma action plan in the application (66.8%). Interestingly, more caregivers of preschool-aged children preferred textual information compared to caregivers of school-aged children (P=.008 for explaining asthma and P=.005 for the symptoms of asthma). Caregivers were generally highly in favor of sharing data collected through a mobile application for research. </sec> <sec> <title>CONCLUSIONS</title> Caregivers of children with asthma in our study identified several desirable educational and interactive features 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. </sec>
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | MetaresearchOpen science Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".