An Augmented Reality Technology to Provide Demonstrative Inhaler Technique Education for Patients With Asthma: Interview Study Among Patients, Health Professionals, and Key Community Stakeholders
Bibliographic record
Abstract
BACKGROUND: Many people with asthma use incorrect inhaler technique, resulting in suboptimal disease management and increased health service use. Novel ways of delivering appropriate instructions are needed. OBJECTIVE: This study explored stakeholder perspectives on the potential use of augmented reality (AR) technology to improve asthma inhaler technique education. METHODS: On the basis of existing evidence and resources, an information poster displaying the images of 22 asthma inhaler devices was developed. Using AR technology via a free smartphone app, the poster launched video demonstrations of correct inhaler technique for each device. In total, 21 semistructured, one-on-one interviews with health professionals, people with asthma, and key community stakeholders were conducted, and data were analyzed thematically using the Triandis model of interpersonal behavior. RESULTS: A total of 21 participants were recruited into the study, and data saturation was achieved. People with asthma were confident with inhaler technique (mean score 9.17, SD 1.33, out of 10). However, health professionals and key community stakeholders identified that this perception was misguided (mean 7.25, SD 1.39, and mean 4.5, SD 0.71, for health professionals and key community stakeholders, respectively) and facilitates persistent incorrect inhaler use and suboptimal disease management. Delivering inhaler technique education using AR was favored by all participants (21/21, 100%), particularly around ease of use, with the ability to visually display inhaler techniques for each device. There was a strongly held belief that the technology has the capacity for improving inhaler technique across all participant groups (mean 9.25, SD 0.89, for participants; mean 9.83, SD 0.41, for health professionals; and mean 9.5, SD 0.71, for key community stakeholders). However, all participants (21/21, 100%) identified some barriers, particularly regarding access and appropriateness of AR for older people. CONCLUSIONS: AR technology may be a novel means to address poor inhaler technique among certain cohorts of patients with asthma and serve as a prompt for health professionals to initiate review of inhaler devices. A randomized controlled trial design is needed to evaluate the efficacy of this technology for use in the clinical care setting.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".