Benefits and Barriers of Caregiver App Engagement for Supporting Diverse Children With Asthma: Mixed Methods Study
Bibliographic record
Abstract
Background: Asthma is one of the most common pediatric conditions affecting millions of US children. Digital health apps may provide children and their caregivers (parents or legal guardians) with ways to manage asthma and improve health and educational outcomes. Objective: As digital health technology becomes more prevalent to help manage chronic conditions, like asthma, this study examined the reported benefits and barriers of caregiver interactions with an asthma-specific app. The app, created by physicians and digital health development professionals, was designed to educate, inform, and help caregivers manage the health of their child. We evaluated app logins and feature use (collectively defined as "app engagement") for caregivers of children with asthma aged 5-11 years. We examined whether (1) app engagement differed due to children's demographic and asthma health characteristics, (2) themes about app engagement emerged from caregiver-reported app experiences, (3) these themes correlated with demographic and asthma health characteristics, and (4) engagement with the app was associated with reduced school absences. Methods: Eighty caregivers and their children with asthma participated between September 2019 and November 2020. Pretest (Time 1) and posttest (Time 2) data were collected over 6 months on caregiver and child demographic and health characteristics, health care usage, app engagement, and app experiences. Additionally, caregiver app engagement data and child health care data were collected retrospectively, 2 years prior to the start of the study. We used a mixed methods design, which included correlation, regression, chi-square, and content analysis to examine caregiver app engagement. Results: Most caregivers were mothers (76/80, 95%) and had a college degree (45/80, 56%). Children's mean age was 8.76 (SD 1.79), and all were English speakers (80/80, 100%). About half of the participants were White children (43/80, 54%), and 26% (21/80) of them had uncontrolled asthma. Logistic regression revealed that caregivers of White children (OR [odds ratio] 8.57, 95% CI 1.68-43.65) with uncontrolled asthma (OR 17.81, 95% CI 2.36-134.24) who earned a college degree (OR 6.94, 95% CI 1.38-34.87) were statistically significantly more likely to use the app than caregivers of children of other races with controlled asthma without a college degree (P<.001). Qualitative findings support and expand on the logistic regression results. Five themes regarding app engagement emerged, including relevancy, acceptability and understandability, technology limitations, educational barriers, and information and communication benefits. Caregivers also identified specific app features that may promote child health and education. Conclusions: Understanding caregiver and child experiences in using digital health technologies for managing asthma may inform ways to support app engagement among caregivers and their children in the effort to improve patient health 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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".