Relationships between spatiotemporal and kinematic domains during treadmill gait change across adulthood
Bibliographic record
Abstract
BACKGROUND: Spatiotemporal and kinematic variables during gait undergo characteristic changes with aging. However, the relationships between these domains, and how these change with aging, have not been extensively investigated. RESEARCH QUESTION: How does age affect relationships between spatiotemporal and joint/segment range-of-motion variables during treadmill gait? METHODS: In this cross-sectional study, a motion capture system tracked 60 participants (20-80 years old), walking at self-selected and slow speeds on a treadmill. Spatiotemporal (step length, width, time, velocity) and range-of-motion (ankle, knee, hip, pelvis, trunk) variables were extracted from 50 steps at each speed. Mixed-effects models were used to quantify relationships between spatiotemporal variables and age, range-of-motion, and interactions between age and range-of-motion. RESULTS: Significant relationships with range-of-motion were found for some spatiotemporal variables; the direction of the relationship was most often positive, with several negative relationships. Significant interaction effects were observed for several pairings (ankle range-of-motion - step length, ankle range-of-motion - step velocity, hip range-of-motion - step velocity), with stronger relationships between spatiotemporal and range-of-motion variables as age increased. SIGNIFICANCE: Stronger relationships at both the ankle and hip in older adults may suggest that older adults rely more on motion at these joints, and constrain other degrees-of-freedom, in an attempt to maintain gait velocity for successful task execution and/or safety on the treadmill. This study statistically quantifies the effects of age on the relationships between spatiotemporal and kinematic domains while accounting for stride-to-stride variations during gait, which provides insight into the control of spatiotemporal variables in healthy individuals across adulthood, and may inform strategies for identifying gait dysfunction with aging.
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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 | 0.000 |
| 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".