Maximal daily stepping cadence partially explains functional capacity of individuals with end‐stage knee osteoarthritis
Bibliographic record
Abstract
Abstract Background Individuals with end‐stage knee osteoarthritis (OA) walk at a lower intensity (ie, slower step cadence) contributing to worse physical function. Previous literature reports daily step counts and sedentary time, with little information regarding stepping bouts or cadence. Determining relationships between daily higher stepping cadence duration and clinical outcomes can move the field toward optimal daily stepping prescription. Objective To quantify daily physical activity patterns of individuals with end‐stage knee OA and determine the contribution of high stepping cadence to explain functional capacity variability. Design Cross‐sectional analysis. Setting Veterans Administration medical center. Participants U.S. military veterans (n = 104; age: 67.1 years [7.2]; mean [SD]; male [89.3%]) with end‐stage knee OA were enrolled. Intervention Not applicable. Main outcome measure Functional capacity (6‐Minute Walk Test [6MWT]). Physical activity (activPAL wearable sensor; cadence and time sitting, standing, and stepping), pain (Western Ontario and McMaster Universities Osteoarthritis Index‐pain subscale) sociodemographic variables, and comorbidities (body mass index and Functional Comorbidity Index) are the main explanatory variables. Results Participants' wake time was mainly sitting (11.0 h/day) in ≥60‐minute bouts (29.7% ± 12.7 of sitting time). Standing (3.4 hours/day) and stepping (1.4 h/day) primarily occurred in 0–5 minute bouts (standing: 87.7% ± 14.4 of standing time, stepping: 98.7% ± 12.7 of stepping time) and stepping cadence was predominantly incidental (1–19 spm; 52.9% ± 9.6 of total stepping time). Backward elimination model results indicated shorter medium‐to‐brisk cadence bout duration, older age, and higher pain significantly explained shorter 6MWT distance (AdjR2=0.24, p < .01). Conclusions Individuals with knee OA spend most of their waking hours sitting, while standing and stepping occurs in short bouts at very low stepping cadence. Decreased time in high stepping cadence is associated with lower functional capacity. Future studies should explore if increasing the daily time spent in higher step cadence can improve functional capacity in this population.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".