Development of a Management App for Postviral Fibromyalgia-Like Symptoms: Patient Preference-Guided Approach
Bibliographic record
Abstract
BACKGROUND: Persistent fibromyalgia-like symptoms have been increasingly reported following viral infections, including SARS-CoV-2. About 30% of patients with post-COVID-19 syndrome fulfill the fibromyalgia criteria. This complex condition presents significant challenges in terms of self-management. Digital health interventions offer a viable means to assist patients in managing their health conditions. However, the challenge of ensuring their widespread adoption and adherence persists. This study responds to this need by developing a patient-centered digital health management app, incorporating patient preferences to enhance usability and effectiveness, ultimately aiming to improve patient outcomes and quality of life. OBJECTIVE: This research aims to develop a digital health self-management app specifically for patients experiencing postviral fibromyalgia-like symptoms. By prioritizing patient preferences and engagement through the app's design and functionality, the study intends to facilitate better self-management practices and improve adherence. METHODS: Using an exploratory study design, the research used patient preference surveys and usability testing as primary tools to inform the development process of the digital health solution. We gathered and analyzed patients' expectations regarding design features, content, and usability to steer the iterative app development. RESULTS: The study uncovered crucial insights from patient surveys and usability testing, which influenced the app's design and functionality. Key findings included a preference for a symptom list over an automated chatbot, a desire to report on a moderate range of symptoms and activities, and the importance of an intuitive onboarding process. While usability testing identified some challenges in the onboarding process, it also confirmed the importance of aligning the app with patient needs to enhance engagement and satisfaction. CONCLUSIONS: Incorporating patient feedback has been a significant factor in the development of the digital health app. Challenges encountered with user onboarding during usability testing have highlighted the importance of this process for user adoption. The study acknowledges the role of patient input in developing digital health technologies and suggests further research to improve onboarding procedures, aiming to enhance patient engagement and their ability to manage digital health resources effectively. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/32193.
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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 | 0.014 |
| 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) | 0.007 | 0.002 |
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".