Speed and cadence adaptations during overground sloped running under real-world conditions
Bibliographic record
Abstract
Abstract Background Biomechanical adaptations to sloped running have been widely studied in laboratory settings, but these are limited by artificial conditions and constrained speeds. Advances in wearable technology now allow for analysis of running biomechanics in real-world environments. Aims This study examined the relationship between surface gradient, running speed, and cadence in recreational runners using field-based data. Methods Data were extracted from the We-TRAC database, comprising GPS-enabled Garmin watch records. Runs were included if they spanned at least 5 km, featured elevation changes over 100 m, and averaged speeds above 1.2 m/s. Each run was segmented into 100 m intervals and categorized by slope: uphill (+ 3 to 15%), level (− 2 to + 2%), and downhill (− 3 to − 15%). Results A total of 148 participants (3001 runs) were included. Uphill segments showed significantly reduced cadence and speed compared to level segments ( p < 0.001, Cohen’s d = 0.30–1.13). Downhill segments were associated with significantly higher speed ( p = 0.013, Cohen’s d = 0.213) but no change in cadence ( p = 0.694). Within individual runners, increases in slope were associated with decreases in both cadence and speed during uphill running, though this pattern was less consistent during downhill running. Conclusion These findings underscore slope-dependent adaptations in real-world running and highlight the utility of wearable data in capturing ecological biomechanics. Recreational runners naturally adjust their cadence and speed according to gradient, suggesting that training programs and wearable feedback systems should account for slope to better monitor performance and reduce injury risk.
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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".