Does an mHealth system reduce health service use for asthma?
Bibliographic record
Abstract
BACKGROUND: Breathe is a mobile health (mHealth) application developed for the self-management of asthma in adults. There is evidence to suggest that mHealth interventions can be used for asthma control; however, their effects on the use of health services remain poorly understood. We sought to determine whether Breathe reduces health services use amongst asthma patients who used the app compared to controls who did not. METHODS: The impact of Breathe on health services use was estimated using a quasi-experimental approach. Two groups of subjects who had participated in a previous randomised clinical trial were included: an intervention group of asthma patients who used the app for 12 months, and a group of controls who did not use the app but received equivalent quality asthma care. A third, external control group of asthma patients were matched to the intervention participants. Generalised linear mixed models were used to determine relative changes in rates of asthma hospitalisations, emergency department (ED) visits, outpatient physician visits and completion of pulmonary function tests (PFTs) over time. RESULTS: A total of 677 individuals with asthma were included in the study: 132 in the intervention group, and 149 and 396 in the internal and external control groups, respectively. There were no statistically significant differences in the change of asthma hospitalisations, ED visits, physician office visits or completion of PFTs between the intervention group and either control group. CONCLUSIONS: Use of the Breathe app is not associated with changes in health services use in adults with asthma.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".