A Smartphone App for Self-Monitoring of Rheumatoid Arthritis Disease Activity to Assist Patient-Initiated Care: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Telemedicine based on self-measurement of disease activity could be one of the key components to create the health care system of the future. Previous publications in various medical fields have shown that it is possible to safely telemonitor patients while reducing the number of outpatient clinic visits. For this purpose, we developed a mobile phone app for patients with rheumatoid arthritis (RA), which allows them to self-monitor their disease. OBJECTIVE: The objective of this study is to assess the safety and efficacy of self-initiated care assisted by a smartphone app in patients with RA. METHODS: This is a randomized controlled trial that will be performed for 1 year. A total of 176 patients with RA will be randomized to either self-initiated care with only one scheduled follow-up consultation assisted by our app or usual care. The coprimary outcome measures are the number of outpatient clinic consultations with a rheumatologist taking place during the trial period and the mean disease activity score as measured by the disease activity score 28 (DAS28) at 12 months. The secondary outcomes are patient satisfaction, adherence, patient empowerment, and cost evaluation of health care assisted by the app. RESULTS: Recruitment started in May 2019, and up to 18 months will be required for completion of recruitment. Thus far, 78 patients have been randomized, and thus far, experiences with the app have been positive. The study results are expected to be published by the end of 2021. CONCLUSIONS: The completion of this study will provide important data regarding the following: (1) safety of self-initiated care supported by a smartphone app in terms of DAS28 and (2) efficacy of lowering health care usage with this new strategy of providing health care. TRIAL REGISTRATION: Netherlands Trial Register NL7715; https://www.trialregister.nl/trial/7715. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/15105.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".