Patient and Parent Perspectives on Improving Pediatric Asthma Self-Management Through a Mobile Health Intervention: Pilot Study
Bibliographic record
Abstract
BACKGROUND: Asthma is a common chronic pediatric disease that can negatively impact children and families. Self-management strategies are challenging to adopt but critical for achieving positive outcomes. Mobile health technology may facilitate self-management of pediatric asthma, especially as adolescents mature and assume responsibility for their disease. OBJECTIVE: This study aimed to explore the perceptions of youths with high-risk asthma and their caregivers on the use of a smartphone app, Smartphone Asthma Management System, in the prevention and treatment of asthma symptoms, possible use of the app to improve self-management of asthma outside traditional clinical settings, and the impact of asthma on everyday life to identify potential needs for future intervention development. METHODS: Key informant interviews were completed with parent-child dyads post participation in an asthma management feasibility intervention study to explore the perceptions of users on a smartphone app designed to monitor symptoms and medication use and offer synchronous and asynchronous provider encounters. A thematic qualitative analysis was conducted inductively through emergent findings and deductively based on the self-determination theory (SDT), identifying 4 major themes. RESULTS: A total of 19 parent-child dyads completed the postintervention interviews. The major themes identified included autonomy, competence, relatedness, and the impact of asthma on life. The participants also shared their perceptions of the benefits and challenges associated with using the app and in the self-management of asthma. Both children and parents conveyed a preference for using technology to facilitate medication and disease management, and children demonstrated a strong willingness and ability to actively engage in their care. CONCLUSIONS: Our study included support for the app and demonstrated the feasibility of enhancing the self-management of asthma by youth in the community. Participant feedback led to intervention refinement and app improvements, and the use of the SDT allowed insight into motivational drivers of behavioral change. The use of mobile apps among high-risk children with asthma and their parents shows promise in improving self-management, medication adherence, and disease awareness and in reducing overall disease morbidity.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".