Efficacy of a Physical Activity Counseling Program With Use of a Wearable Tracker in People With Inflammatory Arthritis: A Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: To assess the efficacy of a multifaceted counseling intervention at improving physical activity participation and patient outcomes. METHODS: We recruited people with rheumatoid arthritis (RA) or systemic lupus erythematosus (SLE). In weeks 1-8, the immediate group received education and counseling by a physical therapist, used a Fitbit and a web application to obtain feedback about their physical activity, and received 4 follow-up calls from the physical therapist. The delay group received the same intervention in weeks 10-17. Participants were assessed at baseline and at weeks 9, 18, and 27. The primary outcome was time spent in moderate/vigorous physical activity (MVPA; in bouts of ≥10 minutes) measured with a SenseWear device. Secondary outcomes included step count, time in sedentary behavior, pain, fatigue, mood, self-management capacity, and habitual behaviors. RESULTS: A total of 118 participants enrolled. The adjusted mean difference in MVPA was 9.4 minutes/day (95% confidence interval [95% CI] -0.5, 19.3, P = 0.06). A significant effect was found in pain (-2.45 [95% CI -4.78, -0.13], P = 0.04), and perceived walking habit (0.54 [95% CI 0.08, 0.99], P = 0.02). The remaining secondary outcomes improved, but were not statistically significant. Post hoc analysis revealed a significant effect in MVPA (14.3 minutes/day [95% CI 2.3, 26.3]) and pain (-4.05 [95% CI -6.73, -1.36]) in participants with RA, but not in those with SLE. CONCLUSION: Counseling by a physical therapist has the potential to improve physical activity in people with inflammatory arthritis, but further study is needed to understand the intervention effect on different diseases. We found a significant improvement in pain, suggesting that the intervention might have 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 | 0.002 | 0.004 |
| 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) | 0.007 | 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".