Usability, Acceptability, and Usefulness of an mHealth App for Diagnosing and Monitoring Patients With Breakthrough Cancer Pain
Bibliographic record
Abstract
BACKGROUND: Breakthrough pain is a major problem and a source of distress in patients with cancer. We hypothesized that health care professionals may benefit from a real-time mobile app to assist in the diagnosis and monitoring of breakthrough cancer pain (BTcP). OBJECTIVE: This study aimed to test the usability, acceptability, and usefulness in real-world practice of the mobile App INES·DIO developed for the management of patients with BTcP. METHODS: This study consisted of a survey of a multidisciplinary sample of 175 physicians who evaluated the mobile app after testing it with 4 patients with BTcP each (for a total of 700 patients). The digital profile of the physicians, use of the different resources contained in the app, usefulness of the resources, acceptability, usability, potential improvements, intention to use, and additional resources to add were recorded. RESULTS: Of the 175 physicians, 96% (168/175) were working in public hospitals. They had an average of 12 (SD 7) years of experience in BTcP and almost all (174/175, 99.43%) had an active digital profile. The Eastern Cooperative Oncology Group and Karnofsky performance scales, the Visual Analogue Scale, and the Davies algorithm to diagnose BTcP were the most frequently used tools with patients and were assessed as very useful by more than 80% (140/175) of physicians. The majority (157/175, 90%) answered that App INES·DIO was well designed and 94% (165/175) would probably or very probably recommend it to other colleagues. More than two-thirds indicated that the report provided by the app was worth being included in patients' clinical records. The most valued resource in the app was the recording of the number, duration, and intensity of pain flares each day and baseline pain control to enhance diagnosis of BTcP. Additional patient-oriented cancer pain educational content was suggested for inclusion in future versions of App INES·DIO. CONCLUSIONS: Our study showed that App INES·DIO is easy to use and useful for physicians to help diagnose and monitor breakthrough pain in patients with cancer. Participants suggested the implementation of additional educational content about breakthrough pain. They agreed on the importance of adding new clinical guidelines/protocols for the management of BTcP, improving their communication skills with patients, and introducing an evidence-based video platform that gathers new educational material on BTcP.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".