Tailoring Strength Training Prescriptions for People with Rheumatoid Arthritis: A Scoping Review
Bibliographic record
Abstract
Introduction: Prescribing strength training (ST) for people with rheumatoid arthritis (RA) is complicated by factors (barriers and facilitators) that affect participation. It is unclear whether guidelines include recommendations beyond prescription parameters (frequency, intensity, time, type, volume, and progression) and adequately incorporate participation factors tailored to people with RA. Objective: To summarize available recommendations to aid in the tailoring of ST prescriptions for people with RA. Methods: Medline, Embase, and CINAHL databases and gray literature were searched for guidelines, recommendations, and review articles containing ST prescription recommendations for RA. Article screening and data extraction were performed in duplicate by two reviewers. Results: Twenty-seven articles met the inclusion criteria. The recommendations address RA-specific ST participation factors including: knowledge gaps (of equipment, ST benefits, disease), memory problems, the management of joint deformity, comorbidity, the fluctuating nature of the disease and symptoms (pain, stiffness, flares), fear avoidance, motivation, need for referral to other professionals, and provision of RA-specific resources. Conclusion: This review summarizes recommendations for tailoring ST prescriptions for people with RA. Future research is required to understand how pain, symptom assessment, and unaddressed ST participation factors like sleep and medication side effects can be addressed to support ST participation amongst people with RA.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".