A mobile health intervention for improving the technique of inhaled medications among children with asthma: A pilot study
Bibliographic record
Abstract
Objective: BreatheSuite MDI is a Bluetooth-enabled inhaler attachment and mobile application which aims to improve asthma control. The objective was to compare pressurized metered dose inhaler (pMDI) technique and asthma control test (ACT) scores pre- and post-use of the device and mobile application. Secondary objectives were to assess user satisfaction and therapy adherence. Methods: Patients between the ages of 8 and 18 were recruited from several pediatric asthma clinics. Technique and ACT scores were assessed at baseline. Users were given no prompts on technique during the first month of device use. For the subsequent three months, users were given technique scores through the mobile application after each inhaler use and provided weekly performance summaries. At the end of the study, technique and ACT scores were analyzed and an exit survey was completed. Conditional logistic regression was used to examine the association between well-controlled asthma (ACT score > 19) and the intervention. Results: = 0.76). 87% of study subjects agreed or strongly agreed that their asthma control improved while using BreatheSuite; 79% were satisfied with the device and mobile application; and 91% preferred using the device compared to a standard logbook to track inhaler usage. Conclusions: In this pilot study, the use of BreatheSuite device was associated with improved technique scores. These results need to be confirmed by a randomized controlled trial. There was high user satisfaction with the BreatheSuite device.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".