Effect of digital monitoring and counselling on self-management ability in patients with rheumatoid arthritis: a randomised controlled trial
Bibliographic record
Abstract
OBJECTIVES: To assess a remote physiotherapist (PT) counselling intervention using self-monitoring tools for improving self-management ability, physical activity participation and health outcomes in people with rheumatoid arthritis (RA). METHODS: Eligible participants were randomly assigned to receive group education, a Fitbit®, a self-monitoring app, and PT counselling phone calls (Immediate Group). The Delayed Group received a monthly e-newsletter until week 26, and then the intervention. The primary outcome was Patient Activation Measure (PAM-13). Participants were assessed at baseline, 27 weeks (the primary end point) and 53 weeks. Secondary outcomes included disease activity, pain, fatigue, depression, sitting/walking habits, daily physical activity time and daily awake sedentary time. Generalized Linear Mixed-effect Models (GLMMs) were used to assess the effect of the intervention on the change of each outcome measure from the initiation to 27 weeks after the intervention. RESULTS: Analysis included 131 participants (91.6% women; 80.2% completed during the COVID-19 pandemic). The mean change of PAM-13 at 27 weeks was 4.6 (Standard Deviation [SD] = 14.7) in the Immediate Group vs -1.6 (SD = 12.5) in the Delayed Group. The mean change in Delayed Group at 53 weeks (after the 26-week intervention) was 3.6 (SD = 14.6). Overall, the intervention improved PAM-13 at 27 weeks post-intervention from the GLMM analysis (adjusted coefficient: 5.3; 95% CI: 2.0, 8.7; P ≤ 0.001). Favourable intervention effects were also found in disease activity, fatigue, depression and self-reported walking habit. CONCLUSION: Remote counselling paired with self-monitoring tools improved self-management ability in people with RA. Findings of secondary outcomes indicate that the intervention had a positive effect on symptom management.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".