A Smartphone App to Support Adherence to Inhaled Corticosteroids in Young Adults With Asthma: Multi-Methods Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Young adults with asthma often report low adherence to inhaled corticosteroids (ICS), leading to uncontrolled symptoms and poor disease outcomes. Technology-enabled digital supports such as mobile health (mHealth) asthma smartphone apps have the potential to support adherence to ICS and asthma self-management. There is a need for feasibility studies to determine the usability, acceptability, and feasibility of these interventions. In addition, it is essential to determine the feasibility of recruiting and retaining young adults to plan future efficacy and effectiveness trials and therefore, establish evidence-based asthma apps. OBJECTIVE: This study aimed to determine (1) the feasibility of recruiting and retaining young adults to a trial and (2) the usability, acceptability, and feasibility of using the AsthmaMD app to support adherence to ICS in a population of young adults living with asthma. METHODS: A multi-methods feasibility study was conducted. Young adults aged 18-30 years with asthma and current prescription for ICS were eligible and invited to take part through a university circular email, social media, and general practice sites. Participation involved completing a baseline self-report questionnaire, downloading and using the AsthmaMD app for 2 weeks, and completing the follow-up assessment, including self-report and open-ended questions about participants' experience of using the app. Primary outcomes included participant recruitment and retention and the usability, acceptability, and feasibility of using AsthmaMD. Quantitative self-report data were analyzed using descriptive statistics, and qualitative open-ended data were analyzed using inductive reflexive thematic analysis. RESULTS: A total of 122 young adults (females, n=101, 82.8%) with a mean age of 24.4 (SD 3.8) years were recruited and they completed baseline measures. Of the 122 young adults, 59 (48.4%) completed the study. The AsthmaMD app received a mean score of 63.1/100 (SD 20.1) on the System Usability Scale (ie, a standardized measure of usability for technology-based apps), and an overall user satisfaction score of 5.8/10 (SD 2.2). Of the 59 participants who completed the study, 49 (83%) participants used the app ≥1 day per week. Two main themes were identified in the qualitative analysis of user experiences: (1) learning how to use the app to suit the individual and (2) benefits and relevance of using the app. CONCLUSIONS: The findings from this study indicate that it is feasible to recruit and retain young adults to examine efficacy and effectiveness in a future trial and that young adults living with asthma may find AsthmaMD to be usable, acceptable, and feasible to support adherence to ICS. Our findings also identified opportunities to further optimize the usability of AsthmaMD and similar apps. Based on our findings, we recommend providing more accessible information on how to use the app and replacing medical terminology with simplified language within the app to improve usability. TRIAL REGISTRATION: ISRCTN Registry ISRCTN11295269; https://www.isrctn.com/ISRCTN11295269.
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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 | 0.021 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".