Who stop telemonitoring disease activity and who adhere: a prospective cohort study of patients with inflammatory arthritis
Bibliographic record
Abstract
BACKGROUND: The use of frequent electronic patient reported outcome measures (ePRO's) enables monitoring disease activity at a distance (telemonitoring) in patients with inflammatory arthritis. However, telemonitoring studies report declining long-term adherence to reporting ePRO's, which may oppose the benefits of telemonitoring. Therefore, the objective was to investigate what factors are associated with (non-)adherence to telemonitoring with a weekly ePRO in patients with inflammatory arthritis (IA). METHODS: We performed a prospective cohort study in patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA) and ankylosing spondylitis (AS) at Reade Amsterdam, The Netherlands. Patients telemonitored their disease activity weekly for 6 months with a modified Multidimensional Health Assessment Questionnaire completed in a smartphone application. The primary outcome was time to dropout, defined as ≥ 4 weeks of consecutively nonresponse. Based on literature and through expert meetings, a predefined set of 13 baseline factors were selected to assess the association with time to dropout through a multivariable Cox-regression analysis. RESULTS: A total of 220 consecutive patients were included (mean age 54, SD 12; 55% females; 99 RA, 81 PsA, and 40 AS). A total of 141 patients (64%) dropped out, with a median time to dropout of 17 weeks (IQR 9-26). Women had a significant higher chance to dropout over 6 months compared to men (HR 1.58, 95% CI 1.06-2.36). CONCLUSION: In the set of investigated factors, women stopped reporting the weekly ePRO sooner than men. Future focus group discussions will be performed to investigate the reasons for dropout, and in specific why women dropped out sooner. Trial registration This trials was prospectively registered at www.trialregister.nl (NL8414).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".