High‐Intensity Interval Training for Knee Osteoarthritis: A Pilot Study
Bibliographic record
Abstract
Objective To assess the feasibility and changes in outcomes of a 12‐week high‐intensity interval training (HIIT) program in individuals with symptomatic knee osteoarthritis (OA). Methods The single‐arm trial included 29 participants (mean ± SD age 63 ± 7 years; 66% women; 66% obese). Measures of participant flow, adherence, and tolerability were collected. Pain, function, and balance were assessed at baseline, 6 weeks, and 12 weeks using the Western Ontario and McMaster Universities Osteoarthritis Index, 20‐m fast‐paced walk test, 30‐second chair‐stand test, stair‐climb test, timed up and go test, and single leg stance. Cardiorespiratory fitness, strength, and body composition were evaluated using peak oxygen consumption (VO2peak), isometric knee extensor/flexor strength, and dual‐energy x‐ray absorptiometry, respectively. HIIT was completed two times/week (cycling or treadmill) and consisted of 10 repetitions of 1‐minute bouts at 90% VO2peak, with 1‐minute rest periods. Separate multivariable‐adjusted linear mixed models were fit for each outcome with fixed effects of time, age, sex, body mass index, and random effects of baseline values to estimate mean changes and 95% confidence intervals (CIs) between baseline and 12‐week assessments. Results Recruitment aligned with the anticipated enrollment rate, adherence was 70%, and no adverse events were reported. At 12 weeks, improvements were observed for most outcomes, with notable mean changes for the 20‐m fast‐paced walk (−1.13 [95% CI −1.61 to −0.64] seconds), 30‐second chair‐stand (2.6 [1.8‐3.4] stands), and VO2peak (0.14 [0.03‐0.24] liters/minute). Conclusion In this 12‐week pilot study, HIIT improved multiple aspects of health in individuals with knee OA; larger studies are needed.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".