Development, implementation and real-life use assessment of HIBOOT: a smartphone application for safety, self-assessment and medication adherence for patients with inflammatory arthritis. A user-centred step-by-step approach.
Bibliographic record
Abstract
Abstract Background Mobile health applications (apps) are increasing in interest for enhancing patient self-management in rheumatology. However, few have been developed with the involvement of patients and health professionals and actually used by patients. Objective To develop and implement a mobile app for safety, self-assessment and medication adherence for patients with inflammatory arthritis treated with disease modifying anti-rheumatic drugs (DMARDs) and assess its real-life use. Methods A mixed qualitative-quantitative study including 42 and 344 patients, respectively, identified patients’ treatment practices and their use of health apps in general and their needs in terms of content and potential use. A multidisciplinary team including 7 rheumatologists, 3 patient association representatives and 4 members of a digital company developed the first version of the app with face-to-face meetings and patient feedback during the process. After the launch of the app, users’ feedback assessment included 7 patients and 3 rheumatologists. The number of app installations, current users, users’ requests and functionalities used were collected. Results Preliminary studies indicated numerous safety issues and needs for counselling, leading to the 6 functionalities of the app HIBOOT (OWL in English): a safety checklist before treatment administration, aid in daily life situations related to self-management and safety, treatment reminders, global well-being self-assessment, periodic counselling messages, and a diary to note comments and appointments. The app is free, with no personal data collection. The presentation is a friendly companion that interacts with the user. The content was based on the French recommendations for DMARD management, drug leaflets and public national health websites. HIBOOT was installed 20,500 times from 2017 to 2020, with 4300 regular current users and still increasing usage curves. The checklist, diary and queries on daily life situations were the most used functionalities. Overall, 18,000 requests were identified for information on safety or other patient matters over a 8-month period in 2020. Scores were 4.4/5 stars at Android and iOS stores. Conclusion HIBOOT is a free app for patients with inflammatory arthritis that was developed with a preliminary qualitative–quantitative study including patients during the process and has scientifically validated content. The number of current users is substantial. Future evaluation of the HIBOOT benefit is needed.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".