Collecting Feedback From Neurologists and Patients to Guide Development of a Parkinson Disease App (DigiPark): Qualitative, Noninterventional Study
Bibliographic record
Abstract
Background: Parkinson disease (PD) is a worldwide, fast-growing, progressive neurodegenerative condition. Its multifaceted clinical presentation includes a wide range of motor and nonmotor symptoms. Smartphones present a potential solution to better monitor and subsequently alleviate PD symptoms. Objective: The aim of this study is to explore neurologists' and patients' needs and preferences regarding the design and functionality of a new smartphone app for PD, DigiPark. Methods: This qualitative, noninterventional study gathered data through two primary methods: (1) by conducting interviews with 9 neurologists and (2) through a usability test including 5 patients with PD. Results: The neurologists affirmed the necessity for a patient-centered app, highlighting the complexities of PD management. They advocated for personalized app functionalities to improve patients' quality of life and emphasized the need for enhanced patient-provider communication. Feedback from the usability test indicated a preference for a clear, simple user interface, as well as elucidation of the app's benefits. Concerns about the app's time demands and the complexity of certain features like medication management were expressed. Furthermore, patients with PD consistently showed interest in features that could track and monitor their progress over time. This highlights the need to include clear benefits within the app to maintain user engagement and commitment. Conclusions: Neurologists' and patients' feedback on the design and functionality of the app complement each other. Collaborative efforts in shaping the app should better address genuine PD management needs. Future clinical trial inclusion can further validate the efficacy of DigiPark.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".