Assessing the Usability of a Prescription-Based Mobile App for Patients With Panic Disorder and Its Management Console for Clinicians: Controlled User Study
Bibliographic record
Abstract
Background: Panic disorder is characterized by sudden panic attacks and persistent anticipatory anxiety. While pharmacotherapy remains effective, patients with panic disorder often experience residual symptoms and functional impairments. Lifestyle factors influence symptom severity but are often unaddressed in routine psychiatric care. Most current digital therapeutics for panic disorder have a limited scope, lack integration with clinicians, and fail to consider behavioral patterns. To address these limitations, our research team developed a prescription-based app that supports structured cognitive behavioral therapy practice, real-life symptom management, and lifestyle modifications for patients with panic disorder, and a management console-a web-based platform that allows clinicians to monitor the patients' engagement and progress as well as determine therapeutic options if necessary. Objective: This study aimed to test the usability of the app and management console by evaluating their interface, functionality, and user experience. The primary goal was to identify the strengths and areas for further improvement of these software devices and to develop a list of modifications to improve the user experience and clinical applicability in updates to refine the devices for a future clinical trial. Methods: Usability data were collected by investigators at a medical device usability research center without the involvement of the development research team, and the participants were 15 patients with panic disorder and 15 psychiatrists. Each group completed experimental use of the app or management console and scored the convenience and safety of its modules, questionnaire evaluations for the acceptability, and presentation of verbal subjective feedback on areas for improvement. Based on the participants' suggestions, a list of items that need to be modified to improve functionality and ease of use for each device was created. Results: Patients completed 155 assigned tasks for the app with more than 98% success, and psychiatrists completed 34 tasks for the management console with more than 86% success. The convenience and safety scores for the app and management console exceeded the neutral threshold (mean >4.5). For all statements about the acceptability, both patients and psychiatrists responded at the level of agreeing with a score exceeding 3 (mean: 3.6~4.3 and 4~4.7, respectively). There were 38 suggestions for app improvements and 66 suggestions for management console improvements, most of which were incorporated in the modification list. Conclusions: Patients reported that the app might be easy to use and help manage anxiety, and psychiatrists found the management console practical and well-suited for outpatients. By combining patient-facing therapeutic tools with clinician-driven prescription and monitoring, the devices offer a solution aligned with clinically integrated, real-world psychiatric care. Modified devices based on the improvement suggestions presented in this study will be evaluated in future clinical trials for their impact on engagement and treatment outcomes.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".