A pilot randomized controlled trial on the impact of text messaging check-ins and a web-based asthma action plan versus a written action plan on asthma exacerbations
Bibliographic record
Abstract
OBJECTIVE: We compared electronic asthma action plans (eAAP) supported by automated text messaging service (SMS) with written asthma action plans (AAP) on assessing acceptability and asthma control improvement. We hypothesized that the patients in eAAP group would have more improvements in their quality of life, asthma control and decreased asthma exacerbations. METHODS: Patients with physician-diagnosed asthma having at least one asthma exacerbation in the previous 12 months were recruited. Participants received individualized action plans and were randomly assigned into either the intervention (eAAP) or control (AAP) group. Intervention participants received weekly SMS, triggering assessment of asthma control and viewing their eAAP. We assessed applicability of Telehealth platform on asthma exacerbations, asthma control, and quality of life over a 12-month period. RESULTS: 106 patients were enrolled (eAAP = 52, AAP = 54). The cumulative response rate to all weekly SMS check-ins was 68.4%. Overall, 28% of patients checked into their eAAP during the intervention period. There were fewer exacerbations in the eAAP group (18%) compared to the AAP group (RR = 0.82 [95%CI 0.49, 1.36]), (P = 0.44). The mean scores for asthma control and quality of life were higher in the eAAP group compared to the AAP group by 4% (RR = 1.04 [95%CI 0.83, 1.30]), (P = 0.73) and 5.5% (RR = 1.06 [95%CI 0.87, 1.28]), (P = 0.59), respectively, but were not statistically significant. CONCLUSIONS: We demonstrated that the eAAP presented improved asthma control outcomes, but as expected the sample size was inadequate to show a significant difference, but based on this pilot study we plan a larger appropriately powered randomized controlled trial (RCT).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".