Comparing Approaches to Teaching Patients How to Use an App-Based Home Spirometer: Randomized Controlled Trial
Bibliographic record
Abstract
Background: Bluetooth-enabled, app-based home spirometry has been validated for use in the diagnosis and monitoring of respiratory disease. Remote teaching (virtual or self-directed) of patients on how to use an app-based home spirometry offers the opportunity to deliver diagnostics safely and at scale. However, the most appropriate method of teaching home spirometry to patients is unknown. Objective: The aim of this pragmatic study undertaken during the COVID-19 pandemic was to determine whether virtual or self-directed teaching were valid methods for deploying home spirometry to patients referred for outpatient lung physiology testing. Methods: REACH-SPIRO is a single-center, unblinded, randomized controlled trial of adults referred for spirometry. Participants were randomized (1:1:1) to be taught to use a Bluetooth, app-based spirometer either (A) face-to-face, (B) virtually (live video conferencing), or (C) self-directed. Forced vital capacity (FVC) and forced expiratory volume in 1 second (FEV1) were recorded. Home spirometry (Spirobank Smart Spirometer) readings for each training modality were compared with gold standard hospital measurements (Vyaire Medical) using Bland-Altman Limits of Agreement (LoA). Patients' feedback questionnaires on acceptability and adherence were also collected, and common themes were described using content analysis. Results: A total of 106 participants were randomized. Bland-Altman analysis between hospital and home FEV1 measurements for group A showed a mean difference of 0.108L, LoA -0.331L to 0.548L, group B 0.152L, LoA -0.358L to 0.661L, and group C 0.153L, LoA -0.358L to 0.661L. FVC measurements in group A showed a mean difference of 0.123L, LoA -0.402L to 0.648L, group B 0.249L, LoA -0.297L to 0.795L, and group C 0.340L, LoA -0.556L to 1.235L. There was no significant difference between randomized arms for either mean FEV1 (P=.41) or FVC (P=.84). Patient feedback was similar across all groups, with more positive feedback for face-to-face and virtual teaching methods. Conclusions: There is no meaningful difference in spirometry measurement between patients taught to use a home spirometer remotely (virtual or self-directed) versus traditional face-to-face treatment. Patients' feedback was favorable to all three methods of teaching. This study supports the use of either virtual or self-directed teaching of home spirometry for the monitoring of respiratory disease. In doing so, we can improve access to spirometry in communities facing barriers to health care and clinically vulnerable populations.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".