Patient-Facing Digital Inhalers for Asthma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The benefits and harms of patient-facing digital inhalers (inhalers with a sensor providing patients immediate feedback on adherence and technique) for asthma remain unclear. OBJECTIVE: To systematically synthesize treatment outcomes of patient-facing digital inhalers for asthma. METHODS: As part of developing upcoming American Academy of Allergy, Asthma & Immunology and American College of Allergy, Asthma, and Immunology Joint Task Force on Practice Parameters severe and difficult-to-control asthma guidelines, we searched MEDLINE, Embase, CENTRAL, CINAHL, PsycINFO, International Clinical Trials Registry Platform (ICTRP), and Latin American and Caribbean Literature on Health Sciences (LILACS), and monitored for additional studies to April 1, 2025, for randomized controlled trials evaluating patient-facing digital inhalers in asthma. Paired reviewers independently screened records and extracted data. Individual patient-level data in random effects analysis of covariance models addressed asthma control and asthma-related quality of life. Random-effects meta-analyses addressed severe exacerbations and harms. We used the Grading of Recommendations Assessment, Development and Evaluations (GRADE) approach to assess certainty of evidence (PROSPERO CRD42024525051). RESULTS: Twelve trials enrolled 2,483 children (aged 4-17 y) and adults with asthma. Patient-facing digital inhalers probably improve asthma control (Asthma Control Test, mean difference 0.63 [95% confidence interval {95% CI} 0.29-0.96]; 44.3% vs 39.8% achieving a 3-point increase, moderate certainty) and may reduce severe exacerbations in patients at high risk for future exacerbations (risk ratio 0.89 [95% CI 0.69-1.16]; risk difference 45 fewer per 1,000 [95% CI 127 fewer to 66 more per 1,000], low certainty), with little to no difference in asthma-related quality of life (low certainty). The median of mean device failure rate was 12%, with trials reporting issues regarding sensor synchronization with smartphones (very low certainty). One trial reported a protected health information exposure while using patient-facing digital inhalers. CONCLUSIONS: Patient-facing digital inhalers probably improve asthma control and may reduce severe asthma exacerbations in patients at high risk for future exacerbations with minimal harm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".