Step-Count Distribution as an Indicator of Walking Reserve in People with Gait Vulnerabilities
Bibliographic record
Abstract
Background: Steps per day can provide a lot of information about the activity of the average person whose main source of activity is derived from walking. This study looks at the distribution of step-count data to identify different subgroups of people which could be used to indicate walking reserve. Methods: A time series design of a secondary data analysis was conducted to track the variability of daily step count for 44 seniors post-fracture. The mean age was 75.8 years (SD: 9.75). The full percentile distribution was used in a cluster analysis and group-based trajectory analysis was used for the longitudinal data. Ordinal regression was used to identify factors associated with cluster membership. Results: percentile of the step-count distribution. Cluster 1, with the lowest reserve would also be classified as sedentary based on median step count (1,555 step count; 1,314 reserve). Cluster 2 represented people with limited activity with low reserve (4,081 step count; 2,439 reserve). Cluster 3 represented active people with high reserve (7,197 step count; 4,370 reserve). Cluster 4, was very active with very high reserve (9,202 step count, 6,964 reserve).The factors associated with cluster membership were gait speed, sit-to-stand, and depression. Conclusions: percentile over a longer period indicates the potential "reserve" for participating in activities that demand additional walking.
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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 | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".