Administration Technique and Acceptance of Inhaler Devices in Patients With Asthma or COPD
Bibliographic record
Abstract
BACKGROUND: Correct technique and patient acceptance of inhalation devices for lung disease influence successful long-term management. Patient ability to use the proper technique may differ depending on the device as well as patient factors. OBJECTIVES: The objectives of our study are to measure self-reported level of acceptability of inhaler devices in community settings and compare correct use of inhaler devices using a novel scale for measuring appropriate inhaler technique. METHODS: This prospective observational study enrolled patients from 3 different practice sites with asthma and/or chronic obstructive pulmonary disease (COPD) who were using an inhaler device. In addition to describing the samples' overall acceptability and correct use scores for the different inhalers, acceptability and correct use scores were compared based on patient characteristics. RESULTS: A total of 161 patients completed the study. The results show that acceptability was lowest for the pressurized metered dose inhaler (pMDI) with a spacer (S); effective use was best demonstrated with pMDI and poorest with pMDI-S. Older patients were found to be more accepting of the HandiHaler device than younger patients. Patients reporting taking more inhaled drugs were found to be more accepting of the pMDI-S than those taking fewer drugs. Finally, patients reporting taking more inhaled drugs demonstrated less-effective use of the HandiHaler device than patients taking fewer drugs. CONCLUSION: Pulmonary disease duration does not ensure improved use for all inhalers; patients taking more drugs demonstrate less-effective use for some devices. Assessment of patient acceptance and factors that predict the ability to use a device should be considered to individualize therapy.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".