The Use of Mobile Health Apps in Clinical Practice Remains Challenging
Bibliographic record
Abstract
According to several international recommendations, disease activity should be regularly monitored with validated outcome measures in patients with axial spondyloarthritis (axSpA), and therapy should be adapted accordingly.1-3 Increasingly, healthcare providers are encouraged to also use patient-reported outcomes (PROs) for this purpose, to capture valuable data from the patient’s perspective. However, because of workforce capacity issues and limited resources, close monitoring with frequent visits is often not feasible in many rheumatology outpatient clinics.4,5 Due to the widespread adoption of smart technology, mobile health apps are being increasingly used to monitor the disease course with electronic PRO measures (ePROMs).6 These apps collect personal data from patients and enable self-monitoring of disease activity and functioning over time. Health apps can also aid in providing education and support, increase adherence to therapy, and facilitate delivery of care.6,7 The large advantage of mobile apps is that they are accessible anytime and anywhere, offering the possibility for flexible, patient-tailored, off-site monitoring.7 Further, frequent PRO assessments have a huge potential to gain insight into what actually happens to patients in between visits and enriches the understanding of the disease course in an individual patient. This way, patterns in disease activity and physical functioning that may have gone unnoticed otherwise, and in particular, the occurrence of disease flares, may be revealed.8,9 For patients, tracking symptoms may increase the sense of being in control of the disease.10 Subsequently, discussing these results with healthcare providers enables patients to actively participate in their own disease management and may result in a more personal approach to treatment and enhance shared decision making.9-11 Additionally, the use of these health apps have the potential to improve the efficiency of care, such as in determining whether to … Address correspondence to Prof dr. Astrid van Tubergen, Department of Internal Medicine, Division of Rheumatology, Maastricht University Medical Center, PO Box 5800, 6202 AZ Maastricht, the Netherlands. Email: a.van.tubergen{at}mumc.nl.
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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 | 0.026 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.036 | 0.033 |
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".