Test-retest reliability and variability of self-reported normal treadmill walking speeds in adults aged 20–80 years old
Bibliographic record
Abstract
BACKGROUND: Overground walking speeds show good to excellent reliability. However, intra-session test-retest reliability and variability of self-reported speeds during treadmill walking have not been quantified. Additionally, literature surrounding the effects of age on variability of overground walking speeds has presented mixed findings. OBJECTIVE: This study evaluated intra-session test-retest reliability and variability of self-reported normal speeds during a treadmill walking speed protocol, and changes in these measures across adults aged 20-80 years old. METHODS: Self-reported normal treadmill walking speeds were collected in two bouts (six and three speeds, respectively) during one session from 59 participants (20-80 years old). Intraclass correlation coefficients (ICC), standard errors of measurement (SEM), and Bland-Altman plots were derived for four sets of self-reported speeds, for the full sample and each age group (grouped by decade). RESULTS: Most ICCs presented good to excellent reliability (ICCs ≥ 0.80), with SEMs ≤ 0.45 km/h and ≤ 12 %. The earliest and latest self-reported speeds in each set of analyzed speeds contributed the most variation. The oldest groups demonstrated decreased reliability and increased variability compared to adults in the middle age groups, with similarly low reliability among the youngest group. CONCLUSION: Results provide insight into the magnitude, patterns, and sources of reliability and variability in self-reported normal speeds during treadmill walking. Furthermore, reliability and variability of self-reported speeds during treadmill walking may be affected by age and/or walking speed. These findings may have utility for research and clinical settings, for applications in which reliably identifying normal walking speeds while minimizing time burden is an important consideration.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".