Parent and Clinician Preferences for an Asthma App to Promote Adolescent Self-Management: A Formative Study
Bibliographic record
Abstract
Background: Most youth asthma apps are not designed with parent and clinician use in mind, and rarely is the app development process informed by parent or clinician input. Objective: This study was conducted to generate formative data on the use, attitudes, and preferences for asthma mHealth app features among parents and clinicians, the important stakeholders who support adolescents with asthma and promote adolescent self-management skills. Methods: We conducted a mixed-methods study from 2013 to 2014 employing a user-centered design philosophy to acquire feedback from a convenience sample of 20 parents and 6 clinicians. Participants were given an iPod Touch and asked to evaluate 10 features on 2 existing asthma apps. Participant experiences using the apps were collected from questionnaires and a thematic analysis of audio-recorded and transcribed (verbatim) interviews using MAXQDA. Descriptive statistics were calculated to characterize the study sample and app feature feedback. Independent samples t tests were performed to compare parent and clinician ratings of app feature usefulness (ratings: 1=not at all useful to 5=very useful). Results: All parents were female (n=20), 45% were black, 20% had an income ≥US $50,000, and 45% had a bachelor’s degree or higher education. The clinician sample included 2 nurses and 4 physicians with a mean practice time of 13 years. Three main themes provided an understanding of how participants perceived their roles and use of asthma app features to support adolescent asthma self-management: monitoring and supervision, education, and communication/information sharing. Parents rated the doctor report feature highest, and clinicians rated the doctor appointment reminder highest of all evaluated app features on usefulness. The peak flow monitoring feature was the lowest ranked feature by both parents and clinicians. Parents reported higher usefulness for the doctor report (t(10)=2.7, P<.02), diary (t(10)=2.7, P<.03), and self-check quiz (t(14)=2.5, P<.02) features than clinicians. Specific participant suggestions for app enhancements (eg, a tutorial showing correct inhaler use, refill reminders, pop-up messages tied to a medication log, evidence-based management steps) were also provided. Conclusions: Parent and clinician evaluations and recommendations can play an important role in the development of an asthma app designed to help support youth asthma management. Two-way asthma care communication between families and clinicians and components involving families and clinicians that support adolescent self-management should be incorporated into adolescent asthma apps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".