Association of Low Physical Activity Levels With Gait Patterns Considered at Risk for Clinical Knee Osteoarthritis Progression
Bibliographic record
Abstract
OBJECTIVE: Although gait analysis provides an estimate of joint loading magnitude and patterns during a typical step, accelerometry provides information about loading frequency. Understanding the relationships between these components of loading and knee osteoarthritis (OA) progression may improve conservative management, as gait interventions may need to account for physical activity levels or vice versa. The primary objective was to examine relationships between gait patterns that have previously been associated with OA progression and accelerometer-derived metrics of loading frequency. The secondary objective examined the association of accelerometer-derived metrics and total knee arthroplasty (TKA) at a mean follow-up of 3.5 years. METHODS: Fifty-seven individuals with knee OA underwent gait analysis and 1 week of accelerometer wear. Spearman correlations were calculated between accelerometer-derived metrics and gait patterns. Differences across quartiles of step count were examined with Jonckheere-Terpstra tests. In a subsample, baseline differences between TKA and no TKA groups were examined with Mann-Whitney U-tests. RESULTS: Gait variables previously related to progression were correlated to both step count and moderate- to vigorous-intensity, but not lower-intensity, physical activity. Individuals in the lowest quartile (~4000 steps/day) exhibited gait patterns previously related to progression. There were no differences in any baseline accelerometer-derived metrics between those that did and did not undergo TKA at follow-up. CONCLUSION: Complex relationships exist between gait, physical activity, and OA progression. Accelerometer-derived metrics may contribute unique information about overall loading for individuals above a certain activity threshold, but for those with lower activity levels, gait may be sufficient to predict clinical progression risk, at least over the short term.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".