Validity and reliability of a smartphone motion analysis app for lower limb kinematics during treadmill running
Bibliographic record
Abstract
OBJECTIVE: To investigate the validity and reliability of a smartphone application for selected lower-limb kinematics during treadmill running. DESIGN: Validity and reliability study. SETTING: Biomechanics laboratory. PARTICIPANTS: Twenty healthy female runners. MAIN OUTCOME MEASURE(S): Sagittal-plane hip, knee, and ankle angle and rearfoot eversion were assessed using the Coach's Eye Smartphone application and a 3D motion capture system. Paired t-test and intraclass correlation coefficients (ICC) established criterion validity of Coach's Eye; ICC determined test-retest and intrarater/interrater reliability. Standard error of measurement (SEM) and minimal detectable change (MDC) were also reported. RESULTS: Significant differences were found between Coach's Eye and 3D measurements for ankle angle at touchdown and knee angle at toe-off (p < 0.05). ICCs for validity of Coach's Eye were excellent for rearfoot eversion at touchdown (ICC = 0.79) and fair-to-good for the other kinematics (range 0.51-0.74), except for hip at touchdown, which was poor (ICC = 0.36). Test-retest (range 0.80-0.92), intrarater (range 0.95-0.99) and interrater (range 0.87-0.94) ICC results were excellent for all selected kinematics. CONCLUSION: Coach's Eye can be used as a surrogate for 3D measures of knee and rearfoot in/eversion at touchdown, and hip, ankle, and rearfoot in/eversion at toe-off, but not for hip and ankle at touchdown or knee at toe-off. Reliable running kinematics were obtained using Coach's Eye, making it suitable for repeated measures.
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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".