Associations between mobility capacity and walking performance in community-dwelling older people with outdoor walking limitations
Bibliographic record
Abstract
Introduction: Walking is the most common physical activity reported by Canadian adults. Community-ambulation requires many skills such as muscle strength, balance, endurance along with other factors. Clinicians use capacity tests to measure physical abilities to better understand the relationship between what people can do (capacity) and what people really do in their daily lives (performance). Our objective was to determine individual and collective relationships between mobility capacity and walking performance to better understand how to use and interpret tests of capacity in clinical situations. Methods: This study was a secondary data analysis. Baseline data from 168 participants (≥63 years of age) of the GO-OUT study, conducted in 4 Canadian cities (Edmonton, Winnipeg, Toronto, and Montreal), were analyzed. The multiple linear regression analyses included mobility capacity tests (6-minute Walk Test (6MWT), comfortable 10-metre Walk Test (10mWT), 30-second Sit-to-Stand (30sSTS) and Mini-BESTest); and walking performance measures collected with 7-day accelerometry (peak 30-minute cadence, time walked in bouts and steps/day). Frailty status data were used to test for a moderation effect. Results: Outcomes from multiple linear regression models with single capacity measures demonstrated that tests of capacity were positively related with walking performance measures, however they explained only a small amount of the variance in peak 30-minute cadence (24-28%), time walked in bouts (12-17%), and steps/day (18-22%). Analyses of the combined capacity measures using multiple linear regression demonstrated larger amounts of variance were explained in all walking performance measures (peak 30-minute cadence R2 =37%, with 6MWT and 30sSTS significant; time walked in bouts R2 =19%, with 10mWT significant; steps/day R2=25%, with 30sSTS significant; all p ≤ 0.05). Frailty did not moderate any relationships. Conclusion: The 6MWT and 30sSTS are significantly associated with peak 30-minute cadence, which is said to represent the best natural effort in daily life. These two tests assess different aspects of physical capacity and physiotherapists may use them to better understand walking performance of their clients to plan and evaluate treatment progress.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".