A Digital Asthma Self-Management Program for Adults
Bibliographic record
Abstract
Importance: Digital health technologies may improve asthma self-management, but evidence is limited in this area. Objective: To investigate the effect of a digital asthma self-management (DASM) program on asthma symptoms in adults. Design, Setting, and Participants: Patient-reported outcome results were reported from a randomized, pragmatic, parallel-arm, open-label, decentralized clinical trial. Adults with asthma were recruited via email, enrolled from October 29, 2020, through November 4, 2021, and were randomized to DASM or usual care (control). Participants completed study activities outside a clinical setting. Data were analyzed between October 13, 2023, and November 29, 2024. Intervention: The app-based DASM program provided tailored notifications, symptom logging, wearable device integration, and other tools. Main Outcomes and Measures: Change in the Asthma Control Test (ACT) was a primary outcome. The ACT is a validated measure of asthma control. Secondary outcomes included engagement and self-reported medication adherence. Results: Nine hundred and one participants were enrolled, with data available for 899 (639 [71.1%] female; mean [SD] age, 36.6 [10.5] years). For subgroup analyses, 195 participants (21.7%) were African American; 125 (13.9%), Hispanic or Latino; 680 (75.6%), commercially insured; and 219 (24.4%), Medicaid insured. Prespecified analyses of participants with uncontrolled asthma at baseline (n = 550) showed improvements after 12 months by 4.6 (95% CI, 4.1-5.2) ACT points among DASM participants (P < .001) and 1.8 (95% CI, 1.3-2.4) ACT points among controls (P < .001) (adjusted difference, 2.8 [95% CI, 2.0-3.6] points; P < .001). Race moderated this effect. At 12 months, the difference between arms in ACT change favored DASM over control by 1.0 (95% CI, -0.7 to 2.7) points (P = .26) for African American participants and 3.3 (95% CI, 2.4-4.2) points (P < .001) for participants not endorsing African American race (adjusted difference, -2.3 [95% CI, -4.2 to -0.4] points; P = .02 for interaction). Moderation was not observed by insurance (Medicaid vs commercial; adjusted difference, 1.0 [95% CI, -0.8 to 2.8] points; P = .18 for interaction) or ethnicity (Hispanic or Latino vs non-Hispanic; adjusted difference, 1.0 [95% CI, -1.3 to 3.3] points; P = .70 for interaction). Conclusions and Relevance: In this randomized clinical trial of DASM, improved asthma control was observed relative to usual care. Program adaptations may be appropriate to confer benefit throughout diverse populations. Trial Registration: ClinicalTrials.gov Identifier: NCT04609644.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".