Is There an App for That? Assessing the Quality and Content of Apps for Asthma Management Available In Canada
Bibliographic record
Abstract
Abstract BACKGROUND: Asthma is the most common chronic respiratory disease in Canada, with highest prevalence in children 5-17 years of age. The use of effective apps for asthma management for children and adolescents may decrease the significant morbidity of this disease. A wide range of apps designed to assist in the home management of asthma are available online. However, there are no published data assessing the quality and content of asthma apps for children and families in Canada. OBJECTIVES: To evaluate apps targeted at children or parents for the active management of asthma based on quality, accuracy of content and presence of advertising. DESIGN/METHODS: The iOS and Google Play stores were searched using the key terms “asthma”, “reactive airways”, “puffer”, and “wheeze”. Apps that were available in English or French, were interactive, were targeted at children or their families, and addressed the medical management of asthma were included. Apps were examined using a detailed data collection tool to assess usability and content. Quality criteria was based on a published, validated tool. Content was evaluated based on the Canadian Thoracic Society asthma guidelines. RESULTS: A total of 95 apps were screened, and 11 met the inclusion criteria. Average app quality score was 3.65 (range 2.63 – 4.38) out of 5. One app used 7 out of 7 CTS criteria in assessing asthma control, one used 6, six used 5, and three used 4. 10 out of 11 apps had the capacity to track symptoms, which was linked to level of asthma control based on CTS/CPS criteria. 8 out of 11 apps had the capacity to record medication doses in a journal format and provided daily medication reminders. 6 apps had the capacity to create and save a personalized asthma action plan. All apps used medications available in Canada. The most functional app based on these criteria was asthmamd, followed by AsthmaSense. One app was funded but a pharmaceutical company and had industry logo, but no significant brand bias. No other apps had specific industry advertising. None were specifically designed for use by children or adolescents. CONCLUSION: 11 relatively high-quality apps are available for asthma management for Canadian families. Of these apps, the ones that best match quality, adherence to CTS guidelines, and lack of marketing/branding are asthmamd and AsthmaSense. There exists a gap to create apps that are specifically targeted at children and adolescents with high functionality for managing 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 | 0.007 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".