Effectiveness of an online self-management tool for people with rheumatoid arthritis: a research protocol
Bibliographic record
Abstract
Abstract Background. Active self-management is a process where patients are fully engaged in managing their health in daily life by having access to contextualized health data and tailored guidance to support a healthy lifestyle. The current study aims to determine whether an e-health intervention which incorporates symptom/disease activity monitoring and physical activity counselling can improve self-management ability in patients with rheumatoid arthritis (RA). Methods. The Empowering active self-management of arthritis: Raising the bar with OPERAS (an On-demand Program to EmpoweR Active Self-management) project is a randomized controlled trial which uses a delayed control design. 134 participants with RA will be randomized to either start the intervention immediately (Immediate Group) or start 6 months later (Delayed Group). The intervention involves: 1) use of a Fitbit-compatible web app to record and monitor their RA disease activity, symptoms and time spent on physical activity and a Fitbit; 2) group education and individual counselling by a physiotherapist (PT), and 3) 6 phone calls with a PT. The primary outcome measure is self-management ability measured by the Patient Activation Measure. Secondary outcome measures include disease status, fatigue, pain, depressive symptoms, and characteristics of habitual behavior. In addition, time spent in physical activity and sedentary activity with a wearable multi-sensor device (SenseWear Mini). Following the 6-month intervention, we will interview a sample of participants to examine their experiences with the intervention. Discussion. The results of this study will help to determine whether this technology-enhanced self-management intervention improves self-management ability and their health outcomes for people living with RA. A limitation of this study is that participants will need to self-report their symptoms, disease status, and treatment use through questionnaires on the OPERAS web app. The user-friendly interface, reminder emails from the research staff, and tailored guidance from PTs will encourage participants to actively engage with the app. Trial Registration. Date of last update in ClinicalTrials.gov: January 2, 2019 ClinicalTrials.gov Identifier: NCT03404245
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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 | 0.049 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".