The Role of Resistance Training Dosing on Pain and Physical Function in Individuals With Knee Osteoarthritis: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Dosing parameters are needed to ensure the best practice guidelines for knee osteoarthritis. OBJECTIVE: To determine whether resistance training affects pain and physical function in individuals with knee osteoarthritis, and whether a dose-response relationship exists. Second, we will investigate whether the effects are influenced by Kellgren-Lawrence grade or location of osteoarthritis. DATA SOURCES: . STUDY SELECTION: Inclusion criteria were randomized controlled trials reporting changes in pain and physical function on humans with knee osteoarthritis comparing resistance training interventions with no intervention. Two reviewers screened 471 abstracts; 12 of the 13 studies assessed were included. STUDY DESIGN: Systematic review. LEVEL OF EVIDENCE: Level 2. DATA EXTRACTION: Mean baseline and follow-up Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores and standard deviations were extracted to calculate the standard mean difference. Articles were assessed for methodological quality using the CONSORT (Consolidated Standards of Reporting Trials) 2010 scale and Cochrane Collaboration tool for assessing risk of bias. RESULTS: The 12 included studies had high methodological quality. Of these, 11 studies revealed that resistance training improved pain and/or physical function. The most common regimen was a 30- to 60-minute session of 2 to 3 sets of 8 to 12 repetitions with an initial resistance of 50% to 60% of maximum resistance that progressed over 3 sessions per week for 24 weeks. Seven studies reported Kellgren-Lawrence grade, and 4 studies included osteoarthritis location. CONCLUSION: Resistance training improves pain and physical function in knee osteoarthritis. Large effect sizes were associated with 24 total sessions and 8- to 12-week duration. No optimal number of repetitions, maximum strength, or frequency of sets or repetitions was found. No trends were identified between outcomes and location or Kellgren-Lawrence grade of osteoarthritis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".