User Needs in the Development of a Health App Ecosystem for Self-Management of Cystic Fibrosis: User-Centered Development Approach
Bibliographic record
Abstract
BACKGROUND: Digital self-management in cystic fibrosis (CF) is foreseen as a means toward better understanding of the disease and its treatment and better adherence to the treatment. Mobile apps hold the potential to provide access to information, motivate, and strengthen compliance. However, to deliver high-quality apps, the development should be based on thorough knowledge about user needs. Empirical research on the user-centered development of mobile apps for health care is, however, still limited. OBJECTIVE: The aim of this research is to develop and evaluate an app ecosystem for self-management in CF. It targets not only those directly affected by CF but also parents and health care professionals involved in the treatment. This paper covers the first step of the design process that aims to analyze the context and the user requirements. The primary research question is as follows: what digital support has the potential to usefully support persons with CF and their caregivers in the CF care? To answer this question, we address two preliminary questions: what important factors in everyday life affect the care of persons with CF? and how is the CF care delivered today and what are the limitations of CF care services? METHODS: The overall research adopts a user-centered design approach in which future users are involved in the development process from the very beginning to ensure that the apps developed best suit the potential users. The research presented in the paper follows an interpretative case study research strategy seeking to understand the concerns and needs of persons with CF and their caregivers. Data were collected through semistructured qualitative interviews involving 74 participants in seven European countries and from internet forums. RESULTS: The results of the analysis phase show a strong need for individuality of the digital support, as well as for its adaptability to different contexts. The paper presents the concerns and needs of the participants in the study and extracts a set of relevant features for a self-management app ecosystem. Education, enzyme dosage calculation, nutrition management, treatment organization, health diary, treatment follow-up, practical guidelines for treatment, communication with doctors, and communication with peers are foreseen as useful features. CONCLUSIONS: The results indicate the readiness for self-management in the CF care even in countries that provide well-functioning health care services for CF care. The large diversity of user requirements identified reflects the crucial role user integration plays in developing apps for a chronic condition such as CF. The need for personalization stemming from the individuality of the patients and the need for communication with health care professionals support the idea of an app ecosystem for the self-management of CF.
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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 | 0.029 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".