Digital Health Intervention for Asthma: Patient-Reported Value and Usability
Bibliographic record
Abstract
BACKGROUND: Although digital health tools are increasingly recognized as effective in improving clinical outcomes such as asthma control and medication adherence, few studies have assessed patient experiences and perception of value. OBJECTIVE: The aim of this study was to evaluate patient satisfaction, perception of usability and value, and desire to continue after 12 months of using a digital health intervention to support asthma management. METHODS: Participants were enrolled in a randomized controlled study evaluating the impact of a digital health platform for asthma management. Participants used electronic inhaler sensors to track medication use and accessed their information in a digital health platform. Electronic surveys were administered to intervention arm participants aged 12 years and older after 12 months of use. The survey assessed asthma control, patient satisfaction with the sensor device, and perception of the usability and value of the digital health platform through closed-ended and open-ended questions. Logistic regression models were used to assess the impact of participants' characteristics on survey completion, satisfaction, and perception of value. RESULTS: Of the 207 intervention arm participants aged 12 years and older, 89 submitted survey responses (42.9% response rate). Of these 89 participants, 70 reported being very satisfied (79%, 70/89) or somewhat satisfied (20%, 18/89) with the inhaler sensor device. Moreover, 93% (83/89) expressed satisfaction with the reports, and 90% (80/89) found the information from the reports useful for learning about their asthma. In addition, 72% (64/89) of the participants reported that they were interested in continuing to use the sensor and platform beyond the study. There were no significant differences in satisfaction with the device or the platform across participants' characteristics, including device type, age, sex, insurance type, asthma control, or syncing history; however, participants with smartphones and longer participation were more likely to take the survey. CONCLUSIONS: Electronic sensors and a digital health platform were well received by participants who reported satisfaction and perceived value. These results were consistent across multiple participants' characteristics. These findings can add to a limited literature to keep improving digital health interventions and ensure the meaningful and enduring impact on patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".