Unlocking the promise of digital inhalers: Insights from an in-vitro study
Bibliographic record
Abstract
INTRODUCTION: Despite decades of inhaler use, inhalation device errors are prevalent, compromising treatment efficacy and substantial healthcare costs. Digital dry powder inhalers (DPIs) can offer real-time monitoring and remote support, potentially enhancing treatment adherence and patient technique. However, the impact of digitalization on the in-vitro performance and the bioavailable dose delivered remains understudied. METHOD 28 study participants received a DPI together and a training session from a licenced pharcacist. Participants in the "intervention" group utilized a smartphone application providing real-time feedback on inhalation technique, while no monitoring was available to the control group. Adherence was monitored and the impact of various improper inhalation techniques observed among participants on the device performance were evaluated in-vitro. Results: The intervention group showed significantly better inhalation technique and adherence (figure 1). erj;64/suppl_68/PA2324/F1 F1 F1 Figure 1 Inhaler technique comparison between Control (A) and Intervention (B) group. In-vitro simulations has shown that the various improper inhalation techniques observed among participants in the control group could lead to up to 65% of loss of bioavailable dose delivered from the device. CONCLUSION: Participants using a digitalized DPI with a companion app significantly improved adherence and demonstrated inhalation technique comparable to theoretical perfect inhalation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".