Relationship of strength training lifetime exposure with functional outcomes and mobility over 4 years: Data from the Osteoarthritis Initiative
Bibliographic record
Abstract
• Among 3192 individuals at risk for, or who had symptoms of knee osteoarthritis, lifelong participation in strength training activity can boost sport and recreational participation levels in later life. • Individuals who did not engage in any strength training during life had the highest prevalence of mobility loss and assistive device use after 50 years of age. • Participation in some strength training during different life periods or participation in all life periods is related to lower knee pain severity compared to no strength training participation. • Strength training throughout life may help people with or at risk for knee osteoarthritis maintain functional levels and activity in sport and recreational activities in later life, while keeping knee pain levels relatively lower. This study compared knee osteoarthritis (OA) outcomes specific to pain, physical function, and quality of life in later life based on strength training (ST) participation over a lifetime. Participants from the Osteoarthritis Initiative ( n = 3192) were grouped by ST engagement during ages 12–18 years, 19–34 years, 35–49 years, and 50+ years. Participants were categorized as: No ST (no ST at any point; 61.7 ± 9.0 years (mean ± SD)), Some ST (engaged in ST during 1–3 life stages; 58.9 ± 8.7 years), and Lifelong ST (consistently engaged in ST across all life stages; 55.6 ± 8.1 years). Measures were collected at baseline and Year 4: Western Ontario and McMaster Universities Osteoarthritis Index scores (WOMAC; pain, daily activities), Knee Injury and Osteoarthritis Outcome Score (KOOS; sports, recreation), Physical Activity Score for the Elderly (PASE), Short Form-12 Physical Component Score (SF-12 PCS), mobility disability, chair rise time, and walking speed (20-m and 400-m). At Year 4, the Lifelong ST group reported better WOMAC activity scores in the right knee along with better WOMAC pain, KOOS sports/recreation, and PASE scores compared to other groups ( p < 0.05). The Lifelong ST group had the lowest incidence of mobility disability of all groups (0.8% vs. 2.3%–4.1%; p = 0.015) and maintained the fastest walking speeds in Year 4. For those with knee OA, ST throughout life may help preserve function and mobility, allowing for greater physical activity engagement while keeping pain levels relatively lower.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".