Impact of combined balance and strength exercise program on lower limb energy flow in individuals with knee osteoarthritis
Bibliographic record
Abstract
Introduction Knee osteoarthritis disrupts biomechanical energy flow, resulting in joint instability, impaired movement, and pain. These issues impact daily activities and increase the risk of falls. Effective interventions are essential. Objective This study examined the effects of a six-month balance and strength training program on lower limb biomechanics and self-reported outcomes in individuals with mild to moderate knee osteoarthritis. Methods Twenty-three participants (mean age: 62.4 years; 69.57% female) completed a structured balance and strength exercise program three times per week. Gait analysis was used to assess lower limb energy flow, while the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) measured symptoms before and after the intervention. Results Significant improvements were observed in overall WOMAC scores (p = 0.009), including subscales for pain (p = 0.022), stiffness (p = 0.005), and physical function (p = 0.013). Energy flow analysis revealed increased energy inflow and outflow at the hip (p < 0.001), reduced energy absorption at the knee (p < 0.001), and enhanced energy outflow at the ankle (p < 0.001), suggesting improved gait dynamics. Discussion The combined balance and strength exercise program effectively enhanced lower limb biomechanics and reduced knee osteoarthritis symptoms. Energy flow analysis may support personalized rehabilitation approaches and help identify individuals at elevated fall risk. Conclusion This exercise program improved lower limb biomechanics, reduced pain and stiffness, enhanced energy flow, and may optimize rehabilitation and fall prevention in individuals with knee osteoarthritis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".