Development and pilot testing of a mobile health solution for asthma self‐management: Asthma action plan smartphone application pilot study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Collaborative self-management is a core recommendation of national asthma guidelines; the written action plan is the knowledge tool that supports this objective. Mobile health technologies have the potential to enhance the effectiveness of the action plan as a knowledge translation tool. OBJECTIVE: To design, develop and pilot a mobile health system to support asthma self-management. METHODS: The present study was a prospective, single-centre, nonrandomized, pilot preintervention-postintervention analysis. System design and development were guided by an expert steering committee. The network included an agnostic web browser-based asthma action plan smartphone application (SPA). Subjects securely transmitted symptoms and peak flow data daily, and received automated control assessment, treatment advice and environmental alerts. RESULTS: Twenty-two adult subjects (mean age 47 years, 82% women) completed the study. Biophysical data were received on 84% of subject days (subject day = 1 subject × 1 day). Subjects viewed their action plan current zone of control on 54% and current air quality on 61% of subject days, 86% followed self-management advice and 50% acted to reduce exposure risks. A large majority affirmed ease of use, clarity and timeliness, and 95% desired SPA use after the study. At baseline, 91% had at least one symptom criterion for uncontrolled asthma and 64% had ≥2, compared with 45% (P=0.006) and 27% (P=0.022) at study close. Mean Asthma Quality of Life Questionnaire score improved from 4.3 to 4.8 (P=0.047). CONCLUSIONS: A dynamic, real-time, interactive, mobile health system with an integrated asthma action plan SPA can support knowledge translation at the patient and provider levels.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it